Before / After

Walk through how a correction becomes a durable preference.

Each scenario walks through one skill and one or more corrections, in three steps:

  1. Here’s what it produced — the output generated with an empty Whetstone store. The subjective preferences we later teach are (typically) not honored.
  2. Here’s what wasn’t good — the scripted correction(s), quoted verbatim as the user would actually say them, plus the run each one started sticking on.
  3. Here’s what changed — the same task, generated again once the learned layer was applied. Most preferences are honored; when one genuinely isn’t (a miss in retrieval, or a correction that hasn’t stuck within the run budget yet), that’s shown too, not hidden. The raw learned layer Whetstone injected is one click away if you want the receipts.

The scenarios span five skills — Great Tables, frontend design, plotnine, ggplot2, and PowerPoint decks — each carrying one or more arbitrary house-style preferences (most scenarios teach one or two; plotnine_gtcars_hp_msrp and ggplot2_gtcars_hp_price each teach five). The substrate is the same for all five — Whetstone doesn’t care that ggplot2 is R and the rest are Python; only the skill and the taught preferences change.

The panels render each skill’s output natively from the committed artifacts under harness/out/: Great Tables tables inline, self-contained web pages sandboxed in an iframe, plotnine and ggplot2 plots as rendered images, and — for binary formats like pptx — the generated build script (the .pptx itself is committed alongside it).

Great Tables

Native HTML tables — real tables, not screenshots.

airquality_monthly_summary

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

New York Air Quality by Month
Monthly means of daily readings — May to September 1973
Month Monthly average conditions
Temperature
(°F)		 Wind speed
(mph)		 Ozone
(ppb)
May 65.5 11.6 23.6
June 79.1 10.3 29.4
July 83.9 8.9 59.1
August 84.0 8.8 60.0
September 76.9 10.2 31.4
Values are means of available daily observations; missing days are excluded. Source: base R airquality (New York, 1973).
Here’s what wasn’t good

The user corrects it, in their own words:

“Keep those numbers to one decimal place.”

The cold run already happened to satisfy this by chance, but this stuck starting at run 2 — honored from then on.

“Make the column labels dark green (#0B6E4F) with white text.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

Monthly Air-Quality Summary
Average temperature, wind speed, and ozone level by month
Month Temperature (°F) Wind Speed (mph) Ozone (ppb)
May 65.5 11.6 23.6
June 79.1 10.3 29.4
July 83.9 8.9 59.1
August 84.0 8.8 60.0
September 76.9 10.2 31.4
Source: New York air-quality measurements, May-September 1973. Values are monthly means (ozone means exclude missing days).
What actually changed, line by line (table.py, cold → warm)
@@ -1,20 +1,24 @@-"""Monthly air-quality summary built with great_tables.+"""Monthly air-quality summary — average Temp, Wind, and Ozone per month. -Grain of source: one row per day (New York, May-Sep 1973 -- base R `airquality`).-We aggregate to one row per month and report the mean of each daily reading,-skipping missing observations (pandas .mean() drops NaN).+Built with great_tables following the skill's 7-step flowchart. -Big Color: three neutral magnitudes qualify (Temperature, Wind, Ozone) over 5-rows, so the >=5-row gradient rule makes each eligible; the <=2 ceiling colours-exactly two. Prompt order (temperature, wind speed, ozone) selects Temperature-(primary -> Blues) and Wind (secondary -> Greens by the neutral tie-breaker-ladder). Ozone is carried by the number alone. Big Color present => LIGHT band.+Design notes:+- Step 1-2: aggregate to one row per month (May-Sep). The Month column holds+  row identifiers -> it IS the stub. Three measures (Temp, Wind, Ozone), units+  carried in the column labels.+- Step 3 Big Color: the learned dark-green header band anchors the table, and a+  dark saturated band means NO Big Color body fills (they would fight for the+  anchor role). Green is also the correct Forest/environment subject hue.+- Step 4: dark-green heading band (#0B6E4F) with white bold text.+- Step 5: grey stub tint, cell hairlines, 1-decimal numbers, frame border,+  source note. No row striping (<10 rows). """ -import numpy as np import pandas as pd-from great_tables import GT, md, loc, style+from great_tables import GT, style, loc -# ---- Step 1: understand + clean -> one correctly-typed DataFrame -----------raw = pd.read_csv("airquality.csv")+# --- Step 1: understand + clean --------------------------------------------+# CSV is already correctly typed (Ozone/Wind float, Temp/Month int); Ozone has+# NaNs which .mean() skips by default.+df = pd.read_csv("airquality.csv") @@ -22,58 +26,49 @@ -monthly = (-    raw.groupby("Month")-    .agg(avg_temp=("Temp", "mean"),-         avg_wind=("Wind", "mean"),-         avg_ozone=("Ozone", "mean"))-    .reset_index()+summary = (+    df.groupby("Month", as_index=False)[["Temp", "Wind", "Ozone"]]+    .mean()     .sort_values("Month") )-monthly["Month"] = monthly["Month"].map(MONTHS)-df = monthly[["Month", "avg_temp", "avg_wind", "avg_ozone"]].reset_index(drop=True)+summary["Month"] = summary["Month"].map(MONTHS) -# ---- Step 3: Big Color -- data-driven, one shared domain per measure -------temp_cols = ["avg_temp"]-wind_cols = ["avg_wind"]-temp_lo = float(np.nanmin(df[temp_cols].to_numpy()))-temp_hi = float(np.nanmax(df[temp_cols].to_numpy()))-wind_lo = float(np.nanmin(df[wind_cols].to_numpy()))-wind_hi = float(np.nanmax(df[wind_cols].to_numpy()))+# --- Steps 2-6: organize + style -------------------------------------------+BAND = "#0B6E4F"   # dark-green heading band (learned preference) -# ---- Steps 2, 4, 5, 6: build the whole table in one chained expression ---- gt = (-    GT(df, rowname_col="Month")                       # Step 2: Month is the stub+    GT(summary, rowname_col="Month")     .tab_header(-        title="New York Air Quality by Month",-        subtitle=md("Monthly means of daily readings &mdash; May to September 1973"),-    )-    .tab_spanner(-        label="Monthly average conditions",-        columns=["avg_temp", "avg_wind", "avg_ozone"],+        title="Monthly Air-Quality Summary",+        subtitle="Average temperature, wind speed, and ozone level by month",     )     .cols_label(-        avg_temp=md("Temperature<br>(&deg;F)"),-        avg_wind=md("Wind speed<br>(mph)"),-        avg_ozone=md("Ozone<br>(ppb)"),+        Temp="Temperature (°F)",+        Wind="Wind Speed (mph)",+        Ozone="Ozone (ppb)",     )     .tab_stubhead(label="Month")-    # Step 5(e): meaningful precision, thousands seps, missing glyph-    .fmt_number(columns=["avg_temp", "avg_wind", "avg_ozone"], decimals=1, use_seps=True)-    .sub_missing(columns=["avg_temp", "avg_wind", "avg_ozone"], missing_text="—")-    .cols_align(align="center", columns=["avg_temp", "avg_wind", "avg_ozone"])-    # Step 3: colour the two selected measures (Temperature -> Blues, Wind -> Greens)-    .data_color(-        columns=temp_cols, palette="Blues",-        domain=[temp_lo, temp_hi], truncate=False, na_color="#808080",+    # Step 5(e): one-decimal numbers (learned preference L1)+    .fmt_number(columns=["Temp", "Wind", "Ozone"], decimals=1)+    .sub_missing(columns=["Temp", "Wind", "Ozone"], missing_text="—")+    .cols_align(align="center", columns=["Temp", "Wind", "Ozone"])+    # Step 4: dark-green header band, white text (learned preference L2)+    .tab_style(+        style=[style.fill(color=BAND), style.text(color="white", weight="bold")],+        locations=loc.column_labels(),     )-    .data_color(-        columns=wind_cols, palette="Greens",-        domain=[wind_lo, wind_hi], truncate=False, na_color="#808080",+    .tab_style(+        style=[style.fill(color=BAND), style.text(color="white", weight="bold")],+        locations=loc.stubhead(),     )-    # Step 4: LIGHT heading band (washed tint of dominant Blues hue) + bottom rule+    # Step 5(d): grey stub tint+    .tab_style(+        style=style.fill(color="#F0F0F0"),+        locations=loc.stub(),+    )+    # Step 6: source note+    .tab_source_note(+        source_note="Source: New York air-quality measurements, May-September 1973. "+        "Values are monthly means (ozone means exclude missing days)."+    )+    # Step 5(a): cell hairlines + column-label bottom rule     .tab_options(-        column_labels_background_color="#EAF0F6",-        column_labels_font_weight="bold",-        column_labels_border_bottom_color="#CCCCCC",-        column_labels_border_bottom_width="2px",-        # Step 5(a): hairline between body rows         table_body_hlines_style="solid",@@ -81,3 +76,5 @@         table_body_hlines_width="1px",-        # Frame: boxed light border on all four sides+        column_labels_border_bottom_color="#CCCCCC",+        column_labels_border_bottom_width="2px",+        # Frame: boxed enclosing border on all four sides         table_border_top_style="solid", table_border_top_color="#CCCCCC", table_border_top_width="1px",@@ -87,15 +84,5 @@     )-    # Step 5(d): stub tint (grey default -- band already carries the washed blue)-    .tab_style(style=style.fill(color="#F0F0F0"), locations=loc.stub())-    .tab_style(style=style.text(weight="bold"), locations=loc.stub())-    # Step 6: source note (>=5 rows) -- state the canonical metric-    .tab_source_note(-        source_note=md(-            "Values are means of available daily observations; missing days are "-            "excluded. Source: base R `airquality` (New York, 1973)."-        )-    ) ) -# ---- Step 7: render the real PNG + the embeddable HTML --------------------+# --- Step 7: render + additional HTML artifact ----------------------------- gt.gtsave("table.png", expand=15)@@ -105,3 +92,3 @@ -print(df.to_string(index=False))-print("\nWrote table.png and table.html")+print("Wrote table.png and table.html")+print(summary.to_string(index=False))
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (number formatting, weight 0.67) Round the temperature, wind, and ozone values to one decimal place.
  - [L2] (header styling, weight 0.67) Style the column-label row with a dark green background (#0B6E4F) and white text — apply it to loc.column_labels() with style.fill(color="#0B6E4F") and style.text(color="white").

gtcars_hp_price

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

Grand Tourers by the Numbers
Horsepower and list price for 47 GT cars, ranked by peak output
Model Horsepower Price
Ferrari LaFerrari 949 $1,416,362
Ferrari F12Berlinetta 731 $319,995
Lamborghini Aventador 700 $397,500
Ferrari GTC4Lusso 680 $298,000
Ferrari 488 GTB 661 $245,400
Ferrari FF 652 $295,000
Chevrolet Corvette 650 $88,345
Ford GT 647 $447,000
Dodge Viper 645 $95,895
Rolls-Royce Wraith 624 $304,350
Lamborghini Huracan 610 $237,250
Aston Martin DB11 608 $211,195
Ferrari 458 Speciale 597 $291,744
Acura NSX 573 $156,000
McLaren 570 570 $184,900
Aston Martin Vanquish 568 $287,250
Rolls-Royce Dawn 563 $335,000
Ferrari 458 Spider 562 $263,553
Ferrari 458 Italia 562 $233,509
BMW M6 560 $113,400
Audi RS 7 560 $108,900
BMW M5 560 $94,100
Ferrari California 553 $198,973
Aston Martin Rapide S 552 $205,300
Lamborghini Gallardo 550 $191,900
Nissan GT-R 545 $101,770
Audi S8 520 $114,900
Mercedes-Benz AMG GT 503 $129,900
Bentley Continental GT 500 $198,500
Maserati Granturismo 454 $132,825
Audi S6 450 $70,900
Audi S7 450 $82,900
Audi R8 430 $115,900
Aston Martin Vantage 430 $103,300
BMW M4 425 $65,700
Maserati Quattroporte 404 $99,900
Lotus Evora 400 $91,900
BMW i8 357 $140,700
Porsche 911 350 $84,300
Maserati Ghibli 345 $70,600
Jaguar F-Type 340 $65,000
Mercedes-Benz SL-Class 329 $85,050
BMW 6-Series 315 $77,300
Porsche Panamera 310 $78,100
Porsche 718 Boxster 300 $56,000
Porsche 718 Cayman 300 $53,900
Tesla Model S 259 $74,500
Source: the gtcars dataset. Price is manufacturer's suggested retail price (MSRP).
Here’s what wasn’t good

The user corrects it, in their own words:

“Show the price as USD currency with no cents.”

The cold run already happened to satisfy this by chance, but this stuck starting at run 2 — honored from then on.

“Use the YlOrBr (yellow-orange-brown) palette for the price heatmap, not a green one.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

The GT Cars Collection
Horsepower and manufacturer's suggested retail price, by model
Horsepower Price
LaFerrari 949 $1,416,362
GT 647 $447,000
Aventador 700 $397,500
Dawn 563 $335,000
F12Berlinetta 731 $319,995
Wraith 624 $304,350
GTC4Lusso 680 $298,000
FF 652 $295,000
458 Speciale 597 $291,744
Vanquish 568 $287,250
458 Spider 562 $263,553
488 GTB 661 $245,400
Huracan 610 $237,250
458 Italia 562 $233,509
DB11 608 $211,195
Rapide S 552 $205,300
California 553 $198,973
Continental GT 500 $198,500
Gallardo 550 $191,900
570 570 $184,900
NSX 573 $156,000
i8 357 $140,700
Granturismo 454 $132,825
AMG GT 503 $129,900
R8 430 $115,900
S8 520 $114,900
M6 560 $113,400
RS 7 560 $108,900
Vantage 430 $103,300
GT-R 545 $101,770
Quattroporte 404 $99,900
Viper 645 $95,895
M5 560 $94,100
Evora 400 $91,900
Corvette 650 $88,345
SL-Class 329 $85,050
911 350 $84,300
S7 450 $82,900
Panamera 310 $78,100
6-Series 315 $77,300
Model S 259 $74,500
S6 450 $70,900
Ghibli 345 $70,600
M4 425 $65,700
F-Type 340 $65,000
718 Boxster 300 $56,000
718 Cayman 300 $53,900
Source: the gtcars dataset. Prices are U.S. MSRP.
What actually changed, line by line (table.py, cold → warm)
@@ -1,2 +1,14 @@-"""GT cars — model, horsepower, and price, built with great_tables."""+"""Clean GT-cars table: model, horsepower, and price.++Built with great_tables following the skill's 7-step flowchart:+  1. Data      : one row per car; model = identifier (stub), hp + msrp = measures.+  2. Columns   : show model (stub), hp, msrp; sorted by price so the heat-map flows.+  3. Big Color : two ordered magnitudes qualify -> both colored (<=2 ceiling).+                 hp   -> Blues  (primary neutral magnitude).+                 msrp -> YlOrBr (warm heat-map, per user preference).+  4. Band      : Big Color present -> LIGHT grey band.+  5. Small clr : hairlines, warm washed stub tint (grey-budget), fmt per type, frame.+  6. Titles    : title + subtitle + source note.+  7. Render    : .gtsave("table.png") + .as_raw_html() -> table.html+""" @@ -6,11 +18,9 @@ -# --- Step 1: understand + clean the data -------------------------------------# One row per car. Identifiers: mfr + model. Measures: hp (horsepower), msrp (price).-# CSV values already parse to numeric floats (no currency strings), no missing values.-raw = pd.read_csv("gtcars.csv")-+# --- Step 1: one clean, correctly-typed DataFrame -------------------------------+df = pd.read_csv("gtcars.csv")+# hp and msrp already parse as floats; select and sort by price (desc) so the+# warm price heat-map reads top-to-bottom. df = (-    raw.assign(car=raw["mfr"].str.strip() + " " + raw["model"].str.strip())-    .loc[:, ["car", "hp", "msrp"]]-    .sort_values("hp", ascending=False)  # order by the hero measure so the gradient reads top-down+    df[["model", "hp", "msrp"]]+    .sort_values("msrp", ascending=False)     .reset_index(drop=True)@@ -18,22 +28,23 @@ -# --- Step 3: Big Color — two neutral magnitude measures ----------------------# Neutral tie-breaker ladder (Blues -> Greens): primary hp = Blues, secondary price = Greens.-hp_lo = float(np.nanmin(df[["hp"]].to_numpy()))-hp_hi = float(np.nanmax(df[["hp"]].to_numpy()))-msrp_lo = float(np.nanmin(df[["msrp"]].to_numpy()))-msrp_hi = float(np.nanmax(df[["msrp"]].to_numpy()))+# --- Step 3: data-driven domains for each colored measure -----------------------+hp_lo, hp_hi = float(np.nanmin(df["hp"])), float(np.nanmax(df["hp"]))+msrp_lo, msrp_hi = float(np.nanmin(df["msrp"])), float(np.nanmax(df["msrp"])) -# --- Steps 2, 4, 5, 6: build the table -------------------------------------- gt = (-    GT(df, rowname_col="car")+    GT(df, rowname_col="model")+    # ---- Step 6: titles + source ----     .tab_header(-        title="Grand Tourers by the Numbers",-        subtitle="Horsepower and list price for 47 GT cars, ranked by peak output",+        title=md("**The GT Cars Collection**"),+        subtitle="Horsepower and manufacturer's suggested retail price, by model",     )-    .tab_stubhead(label="Model")-    .cols_label(hp="Horsepower", msrp="Price")-    # Step 5(e): format each measure to its semantic type+    .tab_source_note(+        source_note=md("Source: the *gtcars* dataset. Prices are U.S. MSRP.")+    )+    # ---- Step 5(e): formatting per column ----     .fmt_number(columns="hp", decimals=0, use_seps=True)     .fmt_currency(columns="msrp", decimals=0, use_seps=True)-    # Step 3: gradient fills (each measure its own domain + semantic hue)+    .sub_missing(missing_text="—")+    .cols_label(hp="Horsepower", msrp="Price")+    .cols_align(align="right", columns=["hp", "msrp"])+    # ---- Step 3: Big Color (two neutral magnitudes) ----     .data_color(@@ -47,3 +58,3 @@         columns="msrp",-        palette="Greens",+        palette="YlOrBr",         domain=[msrp_lo, msrp_hi],@@ -52,15 +63,11 @@     )-    # Step 5(d): stub tint harmonized to the dominant Blues hue (washed-DA)+    # ---- Step 5(d): stub tint (warm washed Ochre, echoing the YlOrBr heat-map) ----     .tab_style(-        style=style.fill(color="#EAF0F6"),+        style=style.fill(color="#F5EFDC"),         locations=loc.stub(),     )-    # Step 6: source note (>=5 rows)-    .tab_source_note(-        source_note=md("Source: the *gtcars* dataset. Price is manufacturer's suggested retail price (MSRP).")-    )-    # Step 4: LIGHT heading band (washed-DA Blues tint) + Step 5(a) borders + Frame+    # ---- Step 4: LIGHT heading band + Step 5(a) borders ----     .tab_options(-        # light heading band-        column_labels_background_color="#EAF0F6",+        # light band (Big Color present)+        column_labels_background_color="#F0F0F0",         column_labels_font_weight="bold",@@ -68,3 +75,3 @@         column_labels_border_bottom_width="2px",-        # body hairlines+        # hairlines between rows         table_body_hlines_style="solid",@@ -72,3 +79,3 @@         table_body_hlines_width="1px",-        # four-side Frame+        # Frame: boxed light border on all four sides         table_border_top_style="solid", table_border_top_color="#CCCCCC", table_border_top_width="1px",@@ -80,7 +87,7 @@ -# --- Step 7: render (mandatory PNG) + self-contained HTML --------------------+# --- Step 7: render the mandatory PNG + the embeddable HTML ---------------------- gt.gtsave("table.png", expand=15) -with open("table.html", "w") as f:-    f.write(gt.as_raw_html())+with open("table.html", "w") as fh:+    fh.write(gt.as_raw_html())
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L2] (color encoding, weight 0.67) Heat-map the price (msrp) column with a warm yellow-orange-brown ramp — use the ColorBrewer `YlOrBr` palette via data_color(columns="msrp", palette="YlOrBr"). Never a green or blue scale.
  - [L1] (currency formatting, weight 0.67) Format the price (msrp) column as US dollars with no decimal places.

gtcars_manufacturer_rollup

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

Horsepower and Price Across the GT Cars
All 47 cars in the gtcars collection, ranked by peak horsepower
Car Year Horsepower Price (MSRP)
Ferrari LaFerrari 2015 949 $1,416,362
Ferrari F12Berlinetta 2015 731 $319,995
Lamborghini Aventador 2015 700 $397,500
Ferrari GTC4Lusso 2017 680 $298,000
Ferrari 488 GTB 2016 661 $245,400
Ferrari FF 2015 652 $295,000
Chevrolet Corvette 2016 650 $88,345
Ford GT 2017 647 $447,000
Dodge Viper 2017 645 $95,895
Rolls-Royce Wraith 2016 624 $304,350
Lamborghini Huracan 2015 610 $237,250
Aston Martin DB11 2017 608 $211,195
Ferrari 458 Speciale 2015 597 $291,744
Acura NSX 2017 573 $156,000
McLaren 570 2016 570 $184,900
Aston Martin Vanquish 2016 568 $287,250
Rolls-Royce Dawn 2016 563 $335,000
Ferrari 458 Spider 2015 562 $263,553
Ferrari 458 Italia 2014 562 $233,509
BMW M6 2016 560 $113,400
Audi RS 7 2016 560 $108,900
BMW M5 2016 560 $94,100
Ferrari California 2015 553 $198,973
Aston Martin Rapide S 2016 552 $205,300
Lamborghini Gallardo 2014 550 $191,900
Nissan GT-R 2016 545 $101,770
Audi S8 2016 520 $114,900
Mercedes-Benz AMG GT 2016 503 $129,900
Bentley Continental GT 2016 500 $198,500
Maserati Granturismo 2016 454 $132,825
Audi S6 2016 450 $70,900
Audi S7 2016 450 $82,900
Audi R8 2015 430 $115,900
Aston Martin Vantage 2016 430 $103,300
BMW M4 2016 425 $65,700
Maserati Quattroporte 2016 404 $99,900
Lotus Evora 2017 400 $91,900
BMW i8 2016 357 $140,700
Porsche 911 2016 350 $84,300
Maserati Ghibli 2016 345 $70,600
Jaguar F-Type 2016 340 $65,000
Mercedes-Benz SL-Class 2016 329 $85,050
BMW 6-Series 2016 315 $77,300
Porsche Panamera 2016 310 $78,100
Porsche 718 Boxster 2017 300 $56,000
Porsche 718 Cayman 2017 300 $53,900
Tesla Model S 2017 259 $74,500
Cell shading encodes magnitude within each column: horsepower in blue, price in green. Price is the manufacturer's suggested retail price in USD.
Source: gtcars dataset (Posit / great_tables sample data).
Here’s what wasn’t good

The user corrects it, in their own words:

“Stop listing every single car model - I want one row per manufacturer, with their average horsepower and average price across their whole lineup.”

This stuck starting at run 2 — honored from then on.

“Also show how many models were averaged together for each manufacturer, so I know how much each row is really representing.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

Horsepower and Price Across the GT Car Marques
Lineup averages for 19 manufacturers, built from all 47 model rows in the gtcars set
Manufacturer Models Lineup Average
Horsepower Price (MSRP)
Ferrari 9 661 $395,837
Chevrolet 1 650 $88,345
Ford 1 647 $447,000
Dodge 1 645 $95,895
Lamborghini 3 620 $275,550
Rolls-Royce 2 594 $319,675
Acura 1 573 $156,000
McLaren 1 570 $184,900
Nissan 1 545 $101,770
Aston Martin 4 540 $201,761
Bentley 1 500 $198,500
Audi 5 482 $98,700
BMW 5 443 $98,240
Mercedes-Benz 2 416 $107,475
Maserati 3 401 $101,108
Lotus 1 400 $91,900
Jaguar 1 340 $65,000
Porsche 4 315 $68,075
Tesla 1 259 $74,500
Averages are unweighted means of the models each manufacturer lists in the dataset; the Models column gives the number of rows behind each average.
Source: gtcars dataset (Posit / great_tables sample data).
What actually changed, line by line (table.py, cold → warm)
@@ -1,14 +1,25 @@-"""GT cars — horsepower and price.+"""GT cars — horsepower and price, rolled up to one row per manufacturer. -Built with the great-tables skill flowchart:-  1 UNDERSTAND   47 cars, grain = one car (mfr + model); measures = hp, msrp (both clean floats)-  2 ORGANIZE     stub = car (PP-13 identifier trigger); rows sorted by hp desc; no grouping-                 (mfr = 19 levels over 47 rows -> not a low-cardinality organizing story)-  3 BIG COLOR    two neutral magnitudes, >=5 rows -> both colored (ceiling is 2):-                 hp (prompt-named first, primary) = Blues; msrp (secondary) = Greens-                 per the neutral tie-breaker ladder Blues -> Greens -> Oranges-  4 BAND         Big Color present -> LIGHT band, washed tint of the dominant hue (Blues) #EAF0F6-  5 SMALL COLOR  hairlines, striping (>=10 rows), stub tint (grey-budget -> washed blue), fmt_*-  6 TITLES       title + subtitle, stacked footer notes (caption + source)-  7 RENDER       .gtsave("table.png") + .as_raw_html() -> table.html+Flowchart trace (great-tables skill):+  1 UNDERSTAND  47 raw model rows; grain = model. Roll up with a real groupby to+                one row per `mfr`: mean hp, mean msrp, and a model count so the+                reader can tell a 2-model average from a 12-model one.+                hp/msrp are already float64 in the CSV; no NAs in either.+  2 ORGANIZE    stub = mfr (PP-13 identifier trigger). Spanner "Lineup Average"+                over the two mean measures; Models sits outside it.+                Canonical metric (PP-18): unweighted mean over the models the+                dataset lists — stated in the subtitle + source note.+  3 BIG COLOR   two ordered magnitudes over 19 rows -> both qualify for gradient+                fill (>=5 rows). Prompt names horsepower first => primary = hp+                (Blues); secondary neutral takes the next rung of the pinned+                Blues -> Greens ladder => msrp (Greens). Models stays uncolored+                (the <=2 ceiling). Domains are data-driven, truncate=False.+  4 BAND        Big Color present => LIGHT band (grey #F0F0F0), 2px #CCCCCC rule.+  5 SMALL COLOR hairlines; group divider right of Models; striping (19 rows, body+                not fully filled); stub tinted #EAF0F6 (grey-budget: band ++                stripes + stub grey with Blues fills -> recolor the stub, the+                highest-priority element, to the washed-Navy tint); fmt_* per+                semantic type; square four-side frame.+  6 TITLES      title + subtitle, stacked source notes.+  7 RENDER      .gtsave("table.png", expand=15) + .as_raw_html() -> table.html """@@ -19,46 +30,49 @@ -# ---------------------------------------------------------------- Step 1: data-df = pd.read_csv("gtcars.csv")+# --- Step 1: load + clean -------------------------------------------------+cars = pd.read_csv("gtcars.csv")+cars["mfr"] = cars["mfr"].str.strip()+cars["hp"] = pd.to_numeric(cars["hp"], errors="coerce")+cars["msrp"] = pd.to_numeric(cars["msrp"], errors="coerce") -# hp and msrp already import as float64 — no currency/percent strings to strip.-# Trim whitespace in the string keys that build the row label.-df["mfr"] = df["mfr"].str.strip()-df["model"] = df["model"].str.strip()--# One human-readable label per row; two columns would force the reader to-# mentally join mfr + model on every line.-df["car"] = df["mfr"] + " " + df["model"]--# ------------------------------------------------------------ Step 2: organize-# Sort by horsepower descending: the ordering is what makes the gradient readable.-tbl = (-    df.sort_values("hp", ascending=False)-    .reset_index(drop=True)[["car", "year", "hp", "msrp"]]+# Roll up the raw model rows to one row per manufacturer (real groupby agg).+by_mfr = (+    cars.groupby("mfr")+    .agg(models=("model", "count"), hp=("hp", "mean"), msrp=("msrp", "mean"))+    .reset_index()+    .sort_values("hp", ascending=False)+    .reset_index(drop=True) ) -# ----------------------------------------------------------- Step 3: Big Color-# Data-driven domains, one per measure (never a round guess).-hp_lo = float(np.nanmin(tbl[["hp"]].to_numpy()))-hp_hi = float(np.nanmax(tbl[["hp"]].to_numpy()))-msrp_lo = float(np.nanmin(tbl[["msrp"]].to_numpy()))-msrp_hi = float(np.nanmax(tbl[["msrp"]].to_numpy()))+# --- Step 3: data-driven domains, one per colored measure -----------------+hp_lo = float(np.nanmin(by_mfr[["hp"]].to_numpy()))+hp_hi = float(np.nanmax(by_mfr[["hp"]].to_numpy()))+msrp_lo = float(np.nanmin(by_mfr[["msrp"]].to_numpy()))+msrp_hi = float(np.nanmax(by_mfr[["msrp"]].to_numpy())) -gt = (-    GT(tbl, rowname_col="car")-    # ------------------------------------------------ Step 6: titles & notes+n_models = int(by_mfr["models"].sum())+n_mfr = len(by_mfr)++table = (+    GT(by_mfr, rowname_col="mfr")     .tab_header(-        title="Horsepower and Price Across the GT Cars",-        subtitle="All 47 cars in the gtcars collection, ranked by peak horsepower",+        title="Horsepower and Price Across the GT Car Marques",+        subtitle=(+            f"Lineup averages for {n_mfr} manufacturers, built from all "+            f"{n_models} model rows in the gtcars set"+        ),     )-    .tab_stubhead(label="Car")-    .cols_label(year="Year", hp="Horsepower", msrp="Price (MSRP)")-    # ------------------------------------------- Step 5(e): format per column-    .fmt_integer(columns=["hp"])-    .fmt_currency(columns=["msrp"], currency="USD", decimals=0)-    .fmt_integer(columns=["year"], use_seps=False)  # 2,017 is wrong for a year-    .sub_missing(columns=["year", "hp", "msrp"], missing_text="—")-    # ------------------------------------------------------ Step 3: Big Color+    .tab_stubhead(label="Manufacturer")+    .tab_spanner(label="Lineup Average", columns=["hp", "msrp"])+    .cols_label(models="Models", hp="Horsepower", msrp="Price (MSRP)")+    # --- Step 5(e): fmt_* per semantic type ---+    .fmt_integer(columns="models")+    .fmt_number(columns="hp", decimals=0, use_seps=True)+    .fmt_currency(columns="msrp", currency="USD", decimals=0, use_seps=True)+    .sub_missing(columns=["models", "hp", "msrp"], missing_text="—")+    .cols_align(align="right", columns=["models", "hp", "msrp"])+    .cols_width(mfr="190px", models="100px", hp="150px", msrp="170px")+    # --- Step 3: Big Color, two colored measures ---     .data_color(         columns=["hp"],-        palette="Blues",  # neutral magnitude, primary measure+        palette="Blues",         domain=[hp_lo, hp_hi],@@ -69,3 +83,3 @@         columns=["msrp"],-        palette="Greens",  # secondary neutral -> next rung of the ladder+        palette="Greens",         domain=[msrp_lo, msrp_hi],@@ -74,27 +88,28 @@     )-    # ------------------------------------------------ Step 5(c): row striping-    # >=10 rows and the body is not essentially fully filled (stub + Year are plain).+    # --- Step 5(c): striping ---     .opt_row_striping()-    .tab_options(row_striping_background_color="#F6F6F6")-    # --------------------------------------------------- Step 5(d): stub tint-    # Grey-budget rule: band + stripes + stub would stack three greys, so the-    # highest-priority element (the stub) takes the washed-Blues tint.+    # --- Step 5(d) + grey budget: washed-Navy stub tint ---     .tab_style(style=style.fill(color="#EAF0F6"), locations=loc.stub())-    .tab_style(style=style.text(weight="bold"), locations=loc.stub())-    .cols_align(align="right", columns=["year", "hp", "msrp"])-    # ------------------------------------------------ Step 6: stacked footers+    # --- Step 5(b): column-group divider at the one group boundary ---+    .tab_style(+        style=style.borders(sides="right", color="#D0D0D0", weight="1px"),+        locations=loc.body(columns="models"),+    )+    .tab_style(+        style=style.borders(sides="right", color="#D0D0D0", weight="1px"),+        locations=loc.column_labels(columns="models"),+    )+    # --- Step 6: stacked footer notes ---     .tab_source_note(         source_note=(-            "Cell shading encodes magnitude within each column: horsepower in blue, "-            "price in green. Price is the manufacturer's suggested retail price in USD."+            "Averages are unweighted means of the models each manufacturer lists "+            "in the dataset; the Models column gives the number of rows behind "+            "each average."         )     )-    .tab_source_note(source_note="Source: gtcars dataset (Posit / great_tables sample data).")+    .tab_source_note(+        source_note="Source: gtcars dataset (Posit / great_tables sample data)."+    )     .tab_options(-        # Step 4: LIGHT heading band (Big Color present) + the 2px bottom rule-        column_labels_background_color="#EAF0F6",-        column_labels_font_weight="bold",-        column_labels_border_bottom_color="#CCCCCC",-        column_labels_border_bottom_width="2px",-        # Step 5(a): hairline between every body row+        # Step 5(a): hairlines + the Step-4 column-label bottom rule         table_body_hlines_style="solid",@@ -102,3 +117,9 @@         table_body_hlines_width="1px",-        # Frame: light boxed border on all four sides (left/right default to none)+        column_labels_border_bottom_color="#CCCCCC",+        column_labels_border_bottom_width="2px",+        # Step 4: LIGHT band (Big Color present), dark bold labels+        column_labels_background_color="#F0F0F0",+        column_labels_font_weight="bold",+        row_striping_background_color="#F6F6F6",+        # Frame: square light border on all four sides         table_border_top_style="solid",@@ -118,8 +139,8 @@ -# ------------------------------------------------------- Step 7: render & save-gt.gtsave("table.png", expand=15, zoom=2.0, vwidth=1000, vheight=1800)+# --- Step 7: render (mandatory renderer) + native HTML artifact -----------+table.gtsave("table.png", expand=15)  with open("table.html", "w") as f:-    f.write(gt.as_raw_html())+    f.write(table.as_raw_html()) -print("wrote table.png and table.html")+print(f"Wrote table.png and table.html — {n_mfr} manufacturers, {n_models} models.")
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (data organization, weight 0.67) Don't list one row per car model. Roll the data up to one row per manufacturer (`mfr`), showing the mean horsepower (`hp`) and mean price (`msrp`) across that manufacturer's whole lineup — compute this with a real groupby aggregation over the raw rows, not a per-model listing.
  - [L2] (row count column, weight 0.67) Add a "Models" column showing how many models from that manufacturer went into each average - the reader should be able to tell a 2-model average from a 12-model one.

gtcars_top10_by_country

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

The World's Priciest Grand Tourers
The 10 most expensive GT cars by manufacturer's suggested retail price, grouped by country of origin
Car MSRP Drivetrain Transmission
Italy
Ferrari LaFerrari $1,416,362 RWD 7-speed automatic
Lamborghini Aventador $397,500 AWD 7-speed automatic
Ferrari F12Berlinetta $319,995 RWD 7-speed automatic
Ferrari GTC4Lusso $298,000 AWD 7-speed automatic
Ferrari FF $295,000 AWD 7-speed automatic
Ferrari 458 Speciale $291,744 RWD 7-speed automatic
United States
Ford GT $447,000 RWD 7-speed automatic
United Kingdom
Rolls-Royce Dawn $335,000 RWD 8-speed automatic
Rolls-Royce Wraith $304,350 RWD 8-speed automatic
Aston Martin Vanquish $287,250 RWD 8-speed automated manual
Source: gtcars dataset (Posit / great_tables sample data).
Here’s what wasn’t good

The user corrects it, in their own words:

“Put drivetrain and transmission under one "Powertrain" spanner header.”

This stuck starting at run 2 — honored from then on.

“Make the country group-label rows teal (#0F766E) with white text.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

The 10 Most Expensive GT Cars
Ranked by manufacturer's suggested retail price, grouped by country of origin
Car Powertrain MSRP
Drivetrain Transmission
Italy
Ferrari LaFerrari RWD 7A $1,416,362
Lamborghini Aventador AWD 7A $397,500
Ferrari F12Berlinetta RWD 7A $319,995
Ferrari GTC4Lusso AWD 7A $298,000
Ferrari FF AWD 7A $295,000
Ferrari 458 Speciale RWD 7A $291,744
United States
Ford GT RWD 7A $447,000
United Kingdom
Rolls-Royce Dawn RWD 8A $335,000
Rolls-Royce Wraith RWD 8A $304,350
Aston Martin Vanquish RWD 8AM $287,250
Source: gtcars dataset (Posit / great_tables sample data).
What actually changed, line by line (table.py, cold → warm)
@@ -1,13 +1,13 @@-"""Top 10 most expensive GT cars, grouped by country of origin.+"""The 10 most expensive GT cars, grouped by country of origin. -Flowchart trace-  1 UNDERSTAND  one car per row; hero measure = MSRP (neutral price magnitude);-                categories = country (grouping), drivetrain, transmission.-  2 ORGANIZE    stub = car name; groupname_col = country (prompt says "grouped by");-                keep MSRP + decoded drivetrain + decoded transmission.-  3 BIG COLOR   one colored measure -> MSRP, Blues sequential gradient (>=5 rows).-  4 BAND        Big Color present -> LIGHT washed-blue band (#EAF0F6).-  5 SMALL COLOR hairlines, striping (10 rows), stub tint, group emphasis, fmt_currency.-  6 TITLES      title + subtitle (states the MSRP definition) + source note.-  7 RENDER      .gtsave("table.png") + .as_raw_html() -> table.html+Flowchart trace:+  Step 1 data     : clean CSV -> one typed DataFrame; compose `car`, take top 10 by MSRP.+  Step 2 columns  : stub=car, groupname_col=ctry_origin (prompt says "grouped by country"),+                    "Powertrain" spanner over drivetrain + transmission, hero = MSRP.+  Step 3 BigColor : MSRP is an ordered neutral magnitude over >=5 rows -> Blues gradient (hero).+  Step 4 band     : Big Color present -> LIGHT washed-blue band (#EAF0F6).+  Step 5 small    : hairlines, group divider, striping, washed-blue stub tint, fmt_currency;+                    row groups recoloured teal (#0F766E) + white text per learned preference.+  Step 6 titles   : title + subtitle + source note.+  Step 7 render   : .gtsave("table.png") + .as_raw_html() -> table.html. """@@ -17,31 +17,19 @@ -# ---- Step 1: understand + clean --------------------------------------------+# --- Step 1: one clean, correctly-typed DataFrame ------------------------------- df = pd.read_csv("gtcars.csv")-df["msrp"] = pd.to_numeric(df["msrp"], errors="coerce")   # ensure real float for fmt/data_color+df["msrp"] = pd.to_numeric(df["msrp"], errors="coerce")   # ensure real numeric for fmt/color+df["ctry_origin"] = df["ctry_origin"].str.strip()         # clean group keys -# One human label per row instead of separate mfr/model columns.+# One human-readable label per row (avoid forcing mfr+model to combine mentally). df["car"] = df["mfr"].str.strip() + " " + df["model"].str.strip() -# Decode the terse drivetrain / transmission codes into readable labels.-df["drive"] = df["drivetrain"].str.strip().str.upper()    # rwd -> RWD, awd -> AWD+# Tidy the powertrain codes for display.+df["drivetrain"] = df["drivetrain"].str.upper()+df["trsmn"] = df["trsmn"].str.upper() -_TR = {"a": "automatic", "m": "manual", "am": "automated manual", "dd": "dual-clutch"}-def decode_trsmn(code: str) -> str:-    code = str(code).strip()-    gears, suffix = code[:1], code[1:]-    return f"{gears}-speed {_TR.get(suffix, suffix)}"-df["transmission"] = df["trsmn"].map(decode_trsmn)+# The sort IS the message: 10 most expensive.+top = df.sort_values("msrp", ascending=False).head(10).reset_index(drop=True)+top = top[["car", "ctry_origin", "drivetrain", "trsmn", "msrp"]] -# ---- Step 2: organize columns -----------------------------------------------# "10 most expensive" = highest MSRP (single, reproducible definition).-top = df.sort_values("msrp", ascending=False).head(10).copy()--# Order countries by their most expensive car, then cars within a country by MSRP,-# so each group is contiguous and groups run priciest-first.-country_rank = top.groupby("ctry_origin")["msrp"].max().rank(ascending=False)-top["_crank"] = top["ctry_origin"].map(country_rank)-top = top.sort_values(["_crank", "msrp"], ascending=[True, False]).reset_index(drop=True)-top = top[["car", "ctry_origin", "msrp", "drive", "transmission"]]--# ---- Step 3: Big Color -- MSRP gradient (neutral magnitude -> Blues) --------+# --- Step 3 domain: data-driven, backend-neutral -------------------------------- lo = float(np.nanmin(top[["msrp"]].to_numpy()))@@ -49,17 +37,21 @@ -# ---- Build the table -------------------------------------------------------- gt = (     GT(top, rowname_col="car", groupname_col="ctry_origin")-    # Step 6: titles + subtitle (states the definition) + source note+    # --- Step 6: titles & source ------------------------------------------------     .tab_header(-        title="The World's Priciest Grand Tourers",-        subtitle="The 10 most expensive GT cars by manufacturer's suggested retail price, grouped by country of origin",+        title="The 10 Most Expensive GT Cars",+        subtitle="Ranked by manufacturer's suggested retail price, grouped by country of origin",     )     .tab_stubhead(label="Car")-    .cols_label(msrp="MSRP", drive="Drivetrain", transmission="Transmission")-    # Step 5e: format the measure-    .fmt_currency(columns="msrp", currency="USD", decimals=0, use_seps=True)-    # Step 3: gradient fill on the single hero measure+    # --- Step 2: labels + Powertrain spanner ------------------------------------+    .cols_label(drivetrain="Drivetrain", trsmn="Transmission", msrp="MSRP")+    .tab_spanner(label="Powertrain", columns=["drivetrain", "trsmn"])+    # --- Step 5(e): formatting per column ---------------------------------------+    .fmt_currency(columns=["msrp"], currency="USD", decimals=0)+    .sub_missing(columns=["msrp"], missing_text="—")+    .cols_align(align="center", columns=["drivetrain", "trsmn"])+    .cols_align(align="right", columns=["msrp"])+    # --- Step 3: Big Color — MSRP magnitude gradient (Blues) ---------------------     .data_color(-        columns="msrp",+        columns=["msrp"],         palette="Blues",@@ -69,15 +61,4 @@     )-    .cols_align(align="left", columns=["drive", "transmission"])-    .cols_align(align="right", columns="msrp")-    # Step 5c: striping (10 rows, body not fully color-filled)-    .opt_row_striping()-    # Step 5d: stub tint -- washed-blue to harmonize with the Blues Big Color-    .tab_style(style=style.fill(color="#EAF0F6"), locations=loc.stub())-    # Step 5a: hairlines + column-label bottom rule; Step 4: light washed-blue band;-    # Step 5 sub-note: row-group emphasis (light fill + bold); Frame border on all 4 sides.+    # --- Step 4: LIGHT washed-blue heading band (Big Color present) --------------     .tab_options(-        table_body_hlines_style="solid",-        table_body_hlines_color="#E8E8E8",-        table_body_hlines_width="1px",-        row_striping_background_color="#F6F6F6",         column_labels_background_color="#EAF0F6",@@ -86,11 +67,35 @@         column_labels_border_bottom_width="2px",-        row_group_background_color="#EAF0F6",-        row_group_font_weight="bold",+        # Step 5(a): hairline between every body row+        table_body_hlines_style="solid",+        table_body_hlines_color="#E8E8E8",+        table_body_hlines_width="1px",+        # Step 5: row-group structural rule + air         row_group_border_top_color="#BDBDBD",         row_group_border_bottom_color="#BDBDBD",+        row_group_font_weight="bold",         row_group_padding="6px",-        table_border_top_style="solid", table_border_top_color="#CCCCCC", table_border_top_width="1px",+        # Step 5(c): row striping (10 rows; body not full-filled by Big Color)+        row_striping_background_color="#F6F6F6",+        # Frame — boxed light border on all four sides+        table_border_top_style="solid",    table_border_top_color="#CCCCCC",    table_border_top_width="1px",         table_border_bottom_style="solid", table_border_bottom_color="#CCCCCC", table_border_bottom_width="1px",-        table_border_left_style="solid", table_border_left_color="#CCCCCC", table_border_left_width="1px",-        table_border_right_style="solid", table_border_right_color="#CCCCCC", table_border_right_width="1px",+        table_border_left_style="solid",   table_border_left_color="#CCCCCC",   table_border_left_width="1px",+        table_border_right_style="solid",  table_border_right_color="#CCCCCC",  table_border_right_width="1px",+    )+    .opt_row_striping()+    # --- Step 5(d): washed-blue stub tint (harmonized to Blues Big Color) --------+    .tab_style(style=style.fill(color="#EAF0F6"), locations=loc.stub())+    # --- Step 5(b): vertical divider at the Powertrain group boundary ------------+    .tab_style(+        style=style.borders(sides="right", color="#D0D0D0", weight="1px"),+        locations=loc.body(columns="trsmn"),+    )+    .tab_style(+        style=style.borders(sides="right", color="#D0D0D0", weight="1px"),+        locations=loc.column_labels(columns="trsmn"),+    )+    # --- Learned preference (L2): teal country row-group labels, white text ------+    .tab_style(+        style=[style.fill(color="#0F766E"), style.text(color="white")],+        locations=loc.row_groups(),     )@@ -99,3 +104,2 @@ -# ---- Step 7: render + embed ------------------------------------------------- gt.gtsave("table.png", expand=15)
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (column grouping, weight 0.67) Group the drivetrain and transmission columns under a single "Powertrain" spanner.
  - [L2] (row group styling, weight 0.67) Give the country row-group label rows a teal background (#0F766E) with white text — apply it to loc.row_groups() with style.fill(color="#0F766E") and style.text(color="white").

sp500_monthly_performance

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

S&P 500 — Monthly Performance, 2010–2015
Opening and closing levels, monthly return, average daily volume, and each month's largest single-day gain and loss
Month Index Level (pts) Change Avg Daily Vol.
(bn shares)		 Largest Single-Day Move
Open Close Gain Loss
2010
Jan 1,116.56 1,073.87 −3.82% 4.79 +1.47% −2.13%
Feb 1,073.89 1,104.49 +2.85% 4.45 +1.46% −3.11%
Mar 1,105.36 1,169.43 +5.80% 4.51 +1.21% −0.58%
Apr 1,171.23 1,186.69 +1.32% 5.56 +1.13% −2.17%
May 1,188.58 1,089.41 −8.34% 6.38 +3.34% −3.23%
Jun 1,087.30 1,030.71 −5.20% 5.00 +2.65% −3.05%
Jul 1,031.10 1,101.60 +6.84% 4.51 +3.08% −2.65%
Aug 1,107.53 1,049.33 −5.25% 3.90 +1.66% −2.46%
Sep 1,049.72 1,141.20 +8.71% 3.79 +2.91% −0.98%
Oct 1,143.49 1,183.26 +3.48% 4.26 +1.76% −1.08%
Nov 1,185.71 1,180.55 −0.44% 4.15 +1.90% −1.38%
Dec 1,186.60 1,257.64 +5.99% 3.68 +1.64% −0.51%
2011
Jan 1,257.62 1,286.12 +2.27% 4.61 +1.13% −1.79%
Feb 1,289.14 1,327.22 +2.95% 3.12 +1.43% −1.75%
Mar 1,328.64 1,325.83 −0.21% 3.89 +1.42% −1.76%
Apr 1,329.48 1,363.61 +2.57% 3.87 +0.85% −0.63%
May 1,365.21 1,345.20 −1.47% 3.89 +1.06% −1.18%
Jun 1,345.20 1,320.64 −1.83% 3.91 +1.34% −2.28%
Jul 1,320.64 1,292.28 −2.15% 4.06 +1.50% −2.03%
Aug 1,292.59 1,218.89 −5.70% 4.71 +4.67% −6.59%
Sep 1,219.12 1,131.42 −7.19% 4.89 +2.81% −3.06%
Oct 1,131.21 1,253.30 +10.79% 4.67 +3.27% −2.83%
Nov 1,251.00 1,246.96 −0.32% 4.01 +4.20% −3.61%
Dec 1,246.91 1,257.60 +0.86% 3.56 +2.95% −2.10%
2012
Jan 1,258.86 1,312.41 +4.25% 3.98 +1.45% −0.59%
Feb 1,312.45 1,365.68 +4.06% 3.92 +1.41% −0.63%
Mar 1,365.90 1,408.47 +3.12% 3.81 +1.75% −1.49%
Apr 1,408.47 1,397.91 −0.75% 3.74 +1.55% −1.71%
May 1,397.86 1,310.33 −6.26% 3.95 +1.56% −1.61%
Jun 1,309.87 1,362.16 +3.99% 3.88 +2.41% −2.43%
Jul 1,362.33 1,379.32 +1.25% 3.48 +1.91% −1.01%
Aug 1,379.32 1,406.58 +1.98% 3.06 +1.87% −0.81%
Sep 1,406.54 1,440.67 +2.43% 3.67 +2.02% −1.05%
Oct 1,440.90 1,412.16 −1.99% 3.42 +1.01% −1.66%
Nov 1,412.20 1,416.18 +0.28% 3.40 +1.99% −2.36%
Dec 1,416.34 1,426.19 +0.70% 3.32 +1.69% −1.10%
2013
Jan 1,426.19 1,498.11 +5.04% 3.61 +2.54% −0.39%
Feb 1,498.11 1,514.68 +1.11% 3.65 +1.27% −1.83%
Mar 1,514.68 1,569.19 +3.60% 3.43 +0.96% −0.83%
Apr 1,569.18 1,597.57 +1.81% 3.50 +1.43% −2.30%
May 1,597.55 1,630.74 +2.08% 3.47 +1.05% −1.29%
Jun 1,631.71 1,606.28 −1.56% 3.75 +1.50% −2.24%
Jul 1,609.78 1,685.73 +4.72% 3.10 +1.06% −0.60%
Aug 1,689.42 1,632.97 −3.34% 2.95 +1.03% −1.33%
Sep 1,635.95 1,681.55 +2.79% 3.31 +1.16% −0.73%
Oct 1,682.41 1,756.54 +4.41% 3.33 +1.91% −1.24%
Nov 1,758.70 1,805.81 +2.68% 3.18 +1.27% −1.33%
Dec 1,806.55 1,848.36 +2.31% 3.09 +1.64% −1.14%
2014
Jan 1,845.86 1,782.59 −3.43% 3.61 +0.96% −2.01%
Feb 1,782.68 1,859.45 +4.31% 3.67 +1.18% −2.29%
Mar 1,857.68 1,872.34 +0.79% 3.42 +1.33% −1.22%
Apr 1,873.96 1,883.95 +0.53% 3.41 +1.05% −2.09%
May 1,884.39 1,923.57 +2.08% 3.03 +0.88% −0.92%
Jun 1,923.87 1,960.23 +1.89% 3.01 +0.73% −0.68%
Jul 1,962.29 1,930.67 −1.61% 3.02 +0.85% −1.75%
Aug 1,929.80 2,003.37 +3.81% 2.77 +1.11% −0.83%
Sep 2,004.07 1,972.29 −1.59% 3.18 +0.86% −1.57%
Oct 1,971.44 2,018.05 +2.36% 4.07 +1.72% −2.01%
Nov 2,018.21 2,067.56 +2.45% 3.35 +0.51% −0.35%
Dec 2,065.78 2,058.90 −0.33% 3.67 +2.09% −1.59%
2015
Jan 2,058.90 1,994.99 −3.10% 3.87 +1.55% −1.65%
Feb 1,996.67 2,104.50 +5.40% 3.62 +1.35% −0.36%
Mar 2,105.23 2,067.89 −1.77% 3.49 +1.29% −1.54%
Apr 2,067.63 2,085.51 +0.86% 3.31 +0.78% −1.02%
May 2,087.38 2,107.39 +0.96% 3.26 +1.15% −1.10%
Jun 2,108.64 2,063.11 −2.16% 3.33 +1.16% −1.95%
Jul 2,067.00 2,103.84 +1.78% 3.54 +1.16% −1.49%
Aug 2,104.49 1,972.18 −6.29% 4.03 +3.62% −3.66%
Sep 1,970.09 1,920.03 −2.54% 3.81 +2.18% −2.85%
Oct 1,919.65 2,079.36 +8.32% 3.90 +1.67% −0.71%
Nov 2,080.76 2,080.41 −0.02% 3.80 +1.54% −1.27%
Dec 2,082.93 2,043.94 −1.87% 3.80 +1.97% −1.73%
Change = month's closing level ÷ opening level − 1. Gain / Loss = largest and smallest single-day intraday return (daily close ÷ daily open − 1) within the month. Fill: green = up, red = down.
Source: S&P 500 daily OHLCV data, 2010–2015.
Here’s what wasn’t good

The user corrects it, in their own words:

“Use accounting format for negatives (parentheses, not a minus).”

This stuck starting at run 2 — honored from then on.

“Use the YlOrRd (amber-to-red) palette for the percent-change heatmap, not green.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

S&P 500 — Monthly Performance Summary
Opening & closing levels, monthly change, and daily trading extremes · 2010–2015
Month Index Level % Change Avg Daily Volume Largest Single-Day Move
Open Close Gain Loss
2010
Jan 1,116.56 1,073.87 (3.8%) 4.79B 1.60% (2.21%)
Feb 1,073.89 1,104.49 2.8% 4.45B 1.80% (3.11%)
Mar 1,105.36 1,169.43 5.8% 4.51B 1.40% (0.55%)
Apr 1,171.23 1,186.69 1.3% 5.56B 1.29% (2.34%)
May 1,188.58 1,089.41 (8.3%) 6.38B 4.40% (3.90%)
Jun 1,087.30 1,030.71 (5.2%) 5.00B 2.95% (3.44%)
Jul 1,031.10 1,101.60 6.8% 4.51B 3.13% (2.88%)
Aug 1,107.53 1,049.33 (5.3%) 3.90B 2.20% (2.82%)
Sep 1,049.72 1,141.20 8.7% 3.79B 2.95% (1.15%)
Oct 1,143.49 1,183.26 3.5% 4.26B 2.09% (1.59%)
Nov 1,185.71 1,180.55 (0.4%) 4.15B 1.93% (1.62%)
Dec 1,186.60 1,257.64 6.0% 3.68B 2.16% (0.51%)
2011
Jan 1,257.62 1,286.12 2.3% 4.61B 1.13% (1.79%)
Feb 1,289.14 1,327.22 3.0% 3.12B 1.67% (2.05%)
Mar 1,328.64 1,325.83 (0.2%) 3.89B 1.72% (1.95%)
Apr 1,329.48 1,363.61 2.6% 3.87B 1.35% (1.10%)
May 1,365.21 1,345.20 (1.5%) 3.89B 1.06% (1.19%)
Jun 1,345.20 1,320.64 (1.8%) 3.91B 1.34% (2.28%)
Jul 1,320.64 1,292.28 (2.1%) 4.06B 1.63% (2.03%)
Aug 1,292.59 1,218.89 (5.7%) 4.71B 4.74% (6.66%)
Sep 1,219.12 1,131.42 (7.2%) 4.89B 2.86% (3.19%)
Oct 1,131.21 1,253.30 10.8% 4.67B 3.43% (2.85%)
Nov 1,251.00 1,246.96 (0.3%) 4.01B 4.33% (3.67%)
Dec 1,246.91 1,257.60 0.9% 3.56B 2.98% (2.11%)
2012
Jan 1,258.86 1,312.41 4.3% 3.98B 1.55% (0.58%)
Feb 1,312.45 1,365.68 4.1% 3.92B 1.46% (0.69%)
Mar 1,365.90 1,408.47 3.1% 3.81B 1.81% (1.54%)
Apr 1,408.47 1,397.91 (0.7%) 3.74B 1.55% (1.71%)
May 1,397.86 1,310.33 (6.3%) 3.95B 1.60% (1.61%)
Jun 1,309.87 1,362.16 4.0% 3.88B 2.49% (2.46%)
Jul 1,362.33 1,379.32 1.2% 3.48B 1.91% (1.01%)
Aug 1,379.32 1,406.58 2.0% 3.06B 1.90% (0.81%)
Sep 1,406.54 1,440.67 2.4% 3.67B 2.04% (1.05%)
Oct 1,440.90 1,412.16 (2.0%) 3.42B 1.03% (1.66%)
Nov 1,412.20 1,416.18 0.3% 3.40B 1.99% (2.37%)
Dec 1,416.34 1,426.19 0.7% 3.32B 1.69% (1.10%)
2013
Jan 1,426.19 1,498.11 5.0% 3.61B 2.54% (0.39%)
Feb 1,498.11 1,514.68 1.1% 3.65B 1.27% (1.83%)
Mar 1,514.68 1,569.19 3.6% 3.43B 0.96% (0.83%)
Apr 1,569.18 1,597.57 1.8% 3.50B 1.43% (2.30%)
May 1,597.55 1,630.74 2.1% 3.47B 1.05% (1.43%)
Jun 1,631.71 1,606.28 (1.6%) 3.75B 1.48% (2.50%)
Jul 1,609.78 1,685.73 4.7% 3.10B 1.36% (0.38%)
Aug 1,689.42 1,632.97 (3.3%) 2.95B 1.25% (1.59%)
Sep 1,635.95 1,681.55 2.8% 3.31B 1.22% (0.72%)
Oct 1,682.41 1,756.54 4.4% 3.33B 2.18% (1.23%)
Nov 1,758.70 1,805.81 2.7% 3.18B 1.34% (1.32%)
Dec 1,806.55 1,848.36 2.3% 3.09B 1.66% (1.13%)
2014
Jan 1,845.86 1,782.59 (3.4%) 3.61B 1.13% (2.09%)
Feb 1,782.68 1,859.45 4.3% 3.67B 1.33% (2.28%)
Mar 1,857.68 1,872.34 0.8% 3.42B 1.53% (1.17%)
Apr 1,873.96 1,883.95 0.5% 3.41B 1.09% (2.09%)
May 1,884.39 1,923.57 2.1% 3.03B 0.97% (0.94%)
Jun 1,923.87 1,960.23 1.9% 3.01B 0.77% (0.71%)
Jul 1,962.29 1,930.67 (1.6%) 3.02B 1.03% (2.00%)
Aug 1,929.80 2,003.37 3.8% 2.77B 1.15% (0.97%)
Sep 2,004.07 1,972.29 (1.6%) 3.18B 0.86% (1.62%)
Oct 1,971.44 2,018.05 2.4% 4.07B 1.96% (2.07%)
Nov 2,018.21 2,067.56 2.4% 3.35B 0.57% (0.28%)
Dec 2,065.78 2,058.90 (0.3%) 3.67B 2.40% (1.64%)
2015
Jan 2,058.90 1,994.99 (3.1%) 3.87B 1.79% (1.83%)
Feb 1,996.67 2,104.50 5.4% 3.62B 1.44% (0.42%)
Mar 2,105.23 2,067.89 (1.8%) 3.49B 1.35% (1.70%)
Apr 2,067.63 2,085.51 0.9% 3.31B 0.92% (1.13%)
May 2,087.38 2,107.39 1.0% 3.26B 1.35% (1.18%)
Jun 2,108.64 2,063.11 (2.2%) 3.33B 1.20% (2.09%)
Jul 2,067.00 2,103.84 1.8% 3.54B 1.24% (1.67%)
Aug 2,104.49 1,972.18 (6.3%) 4.03B 3.90% (3.94%)
Sep 1,970.09 1,920.03 (2.5%) 3.81B 2.51% (2.96%)
Oct 1,919.65 2,079.36 8.3% 3.90B 1.83% (0.68%)
Nov 2,080.76 2,080.41 (0.0%) 3.80B 1.62% (1.40%)
Dec 2,082.93 2,043.94 (1.9%) 3.80B 2.05% (1.94%)
Single-day gain/loss = the most extreme close-to-close daily return within the month. % Change = month-end close vs. month-open. Negatives shown in parentheses.
Source: S&P 500 daily prices (sp500.csv), 2010–2015.
What actually changed, line by line (table.py, cold → warm)
@@ -1,7 +1,7 @@-"""S&P 500 monthly performance summary, 2010-2015.+"""S&P 500 monthly performance summary, 2010–2015. -Aggregates daily OHLCV data (sp500.csv) into one row per month, grouped by-year. Big Color: the monthly percent change is the hero signed measure and-gets a diverging RdYlGn fill (green = up, red = down) on a symmetric domain.-Rendered with Great Tables' gtsave to table.png, plus self-contained HTML.+Built with great_tables per the skill flowchart.+Big Color: the monthly % change column is heat-mapped with the warm+ColorBrewer YlOrRd ramp (learned preference). One colored measure -> LIGHT+heading band. Negatives render accounting-style (parentheses). """@@ -11,37 +11,46 @@ -# ---------------------------------------------------------------- Step 1: data-df = pd.read_csv("sp500.csv", parse_dates=["date"]).sort_values("date")-df = df[(df["date"].dt.year >= 2010) & (df["date"].dt.year <= 2015)].copy()+# ---- Step 1: understand + clean the data ---------------------------------+raw = pd.read_csv("sp500.csv", parse_dates=["date"]).sort_values("date") -# Canonical single-day return = intraday (close - open) / open. Self-contained-# within each trading day, so the number reproduces exactly (stated in note).-df["daily_return"] = df["close"] / df["open"] - 1.0-df["ym"] = df["date"].dt.to_period("M")+# Canonical definition of a "single-day gain/loss": the close-to-close daily+# return, computed across the full series so the first trading day of each+# month has a valid prior-close reference. Stated in a source note.+raw["daily_return"] = raw["close"].pct_change() -# One row per month: opening = first trading day's open, closing = last trading-# day's close, avg daily volume, and the month's best/worst single-day return.-monthly = df.groupby("ym").agg(-    open=("open", "first"),-    close=("close", "last"),-    avg_volume=("volume", "mean"),-    best_day=("daily_return", "max"),-    worst_day=("daily_return", "min"),-).reset_index()+raw["year"] = raw["date"].dt.year+raw["month"] = raw["date"].dt.month -monthly["pct_change"] = monthly["close"] / monthly["open"] - 1.0-monthly["avg_vol_bn"] = monthly["avg_volume"] / 1e9          # billions of shares-monthly["year"] = monthly["ym"].dt.strftime("%Y")-monthly["month"] = monthly["ym"].dt.strftime("%b")+window = raw[(raw["year"] >= 2010) & (raw["year"] <= 2015)].copy() -monthly = monthly[[-    "year", "month", "open", "close", "pct_change",-    "avg_vol_bn", "best_day", "worst_day",-]]+MONTHS = {1: "Jan", 2: "Feb", 3: "Mar", 4: "Apr", 5: "May", 6: "Jun",+          7: "Jul", 8: "Aug", 9: "Sep", 10: "Oct", 11: "Nov", 12: "Dec"} -# ------------------------------------------------- Step 3: Big Color domain-# Hero = monthly percent change (signed). Symmetric, data-driven domain so 0-# sits at the palette midpoint. positive = good => RdYlGn (green = up).-M = float(np.nanmax(np.abs(monthly["pct_change"].to_numpy())))+rows = []+for (yr, mo), grp in window.groupby(["year", "month"], sort=True):+    grp = grp.sort_values("date")+    open_px = grp["open"].iloc[0]+    close_px = grp["close"].iloc[-1]+    rows.append({+        "year": str(yr),+        "month": MONTHS[mo],+        "open_px": open_px,+        "close_px": close_px,+        "pct_change": close_px / open_px - 1.0,+        "avg_volume": grp["volume"].mean(),+        "best_day": grp["daily_return"].max(),+        "worst_day": grp["daily_return"].min(),+    }) -# --------------------------------------------------------- Steps 2-6: build+monthly = pd.DataFrame(rows)++# ---- Step 3: Big Color domain (data-driven, one shared domain) ------------+COLOR_COLS = ["pct_change"]+lo = float(np.nanmin(monthly[COLOR_COLS].to_numpy()))+hi = float(np.nanmax(monthly[COLOR_COLS].to_numpy()))++# Warm washed tint echoing the YlOrRd (Oxblood family) big color.+WARM_TINT = "#F5EBEB"+GREY_BAND = "#F0F0F0"++# ---- Steps 2, 4, 5, 6: build the table ----------------------------------- gt = (@@ -49,12 +58,14 @@     .tab_header(-        title="S&P 500 — Monthly Performance, 2010–2015",-        subtitle="Opening and closing levels, monthly return, average daily volume, and each month's largest single-day gain and loss",+        title="S&P 500 — Monthly Performance Summary",+        subtitle=md("Opening & closing levels, monthly change, and daily trading "+                    "extremes · **2010–2015**"),     )-    .tab_spanner(label="Index Level (pts)", columns=["open", "close"])+    .tab_stubhead(label="Month")+    .tab_spanner(label="Index Level", columns=["open_px", "close_px"])     .tab_spanner(label="Largest Single-Day Move", columns=["best_day", "worst_day"])     .cols_label(-        open="Open",-        close="Close",-        pct_change="Change",-        avg_vol_bn=md("Avg Daily Vol.<br>(bn shares)"),+        open_px="Open",+        close_px="Close",+        pct_change="% Change",+        avg_volume="Avg Daily Volume",         best_day="Gain",@@ -62,29 +73,23 @@     )-    # Index levels are points, not dollars -> fmt_number (not fmt_currency).-    .fmt_number(columns=["open", "close"], decimals=2)-    .fmt_number(columns=["avg_vol_bn"], decimals=2)-    .fmt_percent(columns=["pct_change", "best_day", "worst_day"], decimals=2, force_sign=True)-    # Diverging fill on the hero measure only (one colored measure).+    # Step 5(e): format per semantic type -----------------------------------+    .fmt_number(columns=["open_px", "close_px"], decimals=2)+    .fmt_percent(columns="pct_change", decimals=1, accounting=True)+    .fmt_number(columns="avg_volume", decimals=2, scale_by=1e-9, pattern="{x}B")+    .fmt_percent(columns="best_day", decimals=2)+    .fmt_percent(columns="worst_day", decimals=2, accounting=True)+    # Step 3: Big Color — YlOrRd heat map on the monthly % change -----------     .data_color(-        columns=["pct_change"],-        palette="RdYlGn",-        domain=[-M, M],+        columns=COLOR_COLS,+        palette="YlOrRd",+        domain=[lo, hi],         truncate=False,+        na_color="#808080",     )-    .cols_align(align="left", columns=["month"])-    .cols_align(align="right", columns=["open", "close", "pct_change", "avg_vol_bn", "best_day", "worst_day"])-    # ------------------------------------------------ Step 5: Small Color-    .opt_row_striping()-    .tab_style(style=style.fill(color="#F0F0F0"), locations=loc.stub())-    # column-group vertical dividers at each logical boundary (body + header)-    .tab_style(-        style=style.borders(sides="right", color="#D0D0D0", weight="1px"),-        locations=[loc.body(columns=c) for c in ["close", "pct_change", "avg_vol_bn"]],-    )-    .tab_style(-        style=style.borders(sides="right", color="#D0D0D0", weight="1px"),-        locations=[loc.column_labels(columns=c) for c in ["close", "pct_change", "avg_vol_bn"]],-    )+    # Step 4: LIGHT heading band (Big Color present) ------------------------     .tab_options(-        # cell hairlines+        column_labels_background_color=WARM_TINT,+        column_labels_font_weight="bold",+        column_labels_border_bottom_color="#CCCCCC",+        column_labels_border_bottom_width="2px",+        # Step 5(a): hairlines + row-group emphasis         table_body_hlines_style="solid",@@ -92,10 +97,3 @@         table_body_hlines_width="1px",-        row_striping_background_color="#F6F6F6",-        # light heading band (Big Color present; diverging hue -> grey band)-        column_labels_background_color="#F0F0F0",-        column_labels_font_weight="bold",-        column_labels_border_bottom_color="#CCCCCC",-        column_labels_border_bottom_width="2px",-        # row-group emphasis (fill + bold, required pair)-        row_group_background_color="#F0F0F0",+        row_group_background_color=GREY_BAND,         row_group_font_weight="bold",@@ -104,3 +102,5 @@         row_group_padding="6px",-        # Frame — boxed enclosing border on all four sides+        # Step 5(c): striping (72 rows, body not fully filled)+        row_striping_background_color="#F6F6F6",+        # Frame         table_border_top_style="solid", table_border_top_color="#CCCCCC", table_border_top_width="1px",@@ -110,14 +110,20 @@     )-    .tab_stubhead(label="Month")-    # ------------------------------------------------ Step 6: annotations-    .tab_source_note(-        source_note=md(-            "**Change** = month's closing level ÷ opening level − 1. "-            "**Gain / Loss** = largest and smallest single-day intraday return "-            "(daily close ÷ daily open − 1) within the month. Fill: green = up, red = down."-        )-    )-    .tab_source_note(source_note="Source: S&P 500 daily OHLCV data, 2010–2015.")+    .opt_row_striping()+    # Step 5(d): stub tint (grey default keeps warm budget on the band)+    .tab_style(style=style.fill(color=GREY_BAND), locations=loc.stub())+    # Step 5(b): column-group vertical dividers at group seams+    .tab_style(style=style.borders(sides="right", color="#D0D0D0", weight="1px"),+               locations=loc.body(columns=["close_px", "avg_volume"]))+    .tab_style(style=style.borders(sides="right", color="#D0D0D0", weight="1px"),+               locations=loc.column_labels(columns=["close_px", "avg_volume"]))+    .cols_align(align="center", columns="pct_change")+    # Step 6: source notes -------------------------------------------------+    .tab_source_note(source_note=md(+        "**Single-day gain/loss** = the most extreme close-to-close daily return "+        "within the month. **% Change** = month-end close vs. month-open. "+        "Negatives shown in parentheses."))+    .tab_source_note(source_note="Source: S&P 500 daily prices (sp500.csv), 2010–2015.") ) +# ---- Step 7: render + verify --------------------------------------------- gt.gtsave("table.png", expand=15)@@ -125,2 +131,4 @@     f.write(gt.as_raw_html())+ print("Wrote table.png and table.html")+print(monthly.head())
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L2] (color encoding, weight 0.67) Heat-map the percent-change column with a warm amber-to-red ramp — use the ColorBrewer `YlOrRd` palette via data_color(columns=..., palette="YlOrRd"). Not a green or red-green scale.
  - [L1] (number formatting, weight 0.67) Show negative numbers in accounting style — wrapped in parentheses, not with a minus sign.

Frontend design

Self-contained pages, sandboxed in an iframe.

landing_hero

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

Here’s what wasn’t good

The user corrects it, in their own words:

“Make the accent #FF6B00 (burnt orange), not the blue you used — for the CTA button and links.”

This stuck starting at run 2 — honored from then on.

“Widen the headline letter tracking — use letter-spacing: 0.2em, not tight spacing.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

What actually changed, line by line (index.html, cold → warm)
@@ -14,12 +14,13 @@     html, body {-      width: 100%;       height: 100%;-    }--    body {-      font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", sans-serif;-      background: linear-gradient(135deg, #1a1f2e 0%, #242b3a 100%);-      color: #f0f4f8;+      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Helvetica Neue', sans-serif;+      background-color: #1a1a1a;+      color: #e8e8e8;       line-height: 1.6;+    }++    /* Hero container */+    .hero {       display: flex;+      flex-direction: column;       align-items: center;@@ -27,159 +28,143 @@       min-height: 100vh;-      padding: 2rem;-    }--    .hero-container {-      max-width: 700px;+      padding: 3rem 1.5rem;       text-align: center;-      animation: fadeIn 0.8s ease-out;-    }--    @keyframes fadeIn {-      from {-        opacity: 0;-        transform: translateY(20px);-      }-      to {-        opacity: 1;-        transform: translateY(0);-      }-    }-+    }++    /* Eyebrow */     .eyebrow {       display: inline-block;-      font-size: 0.8rem;+      font-size: 0.875rem;       font-weight: 500;-      letter-spacing: 0.15em;+      letter-spacing: 0.05em;+      color: #999;       text-transform: uppercase;-      color: #7a8490;-      margin-bottom: 2rem;-      opacity: 0;-      animation: fadeIn 0.8s ease-out 0.15s forwards;-    }-+      margin-bottom: 2.5rem;+    }++    .eyebrow::before {+      content: '';+      display: inline-block;+      width: 6px;+      height: 6px;+      border-radius: 50%;+      background-color: #FF6B00;+      margin-right: 0.75rem;+      vertical-align: middle;+    }++    /* Headline */     h1 {-      font-size: clamp(2.5rem, 8vw, 4rem);+      font-size: clamp(2.5rem, 7vw, 3.5rem);       font-weight: 700;-      letter-spacing: -0.02em;-      line-height: 1.15;+      letter-spacing: 0.2em;+      line-height: 1.2;       margin-bottom: 1.5rem;-      color: #f0f4f8;-      opacity: 0;-      animation: fadeIn 0.8s ease-out 0.3s forwards;-    }-+      max-width: 900px;+      color: #ffffff;+    }++    /* Subtext */     .subtext {-      font-size: 1.1rem;-      line-height: 1.7;-      color: #c5ccd4;+      font-size: 1.125rem;+      color: #b8b8b8;+      line-height: 1.8;       margin-bottom: 2.5rem;-      max-width: 600px;-      margin-left: auto;-      margin-right: auto;-      opacity: 0;-      animation: fadeIn 0.8s ease-out 0.45s forwards;-    }-+      max-width: 650px;+      font-weight: 400;+    }++    /* CTA Container */     .cta-group {       display: flex;-      flex-direction: row;+      flex-direction: column;+      gap: 1rem;       align-items: center;-      justify-content: center;-      gap: 1.5rem;-      margin-bottom: 2rem;-      opacity: 0;-      animation: fadeIn 0.8s ease-out 0.6s forwards;-      flex-wrap: wrap;-    }--    .cta-primary {-      display: inline-flex;-      align-items: center;-      justify-content: center;-      gap: 0.6rem;-      padding: 0.875rem 1.75rem;-      background: #00d984;-      color: #0f1419;-      border: 2px solid #00d984;-      border-radius: 0.375rem;+      margin-bottom: 3rem;+    }++    /* Primary CTA Button */+    .btn-primary {+      display: inline-block;+      padding: 0.875rem 2rem;       font-size: 1rem;       font-weight: 600;-      letter-spacing: 0.02em;+      letter-spacing: 0.03em;+      background-color: #FF6B00;+      color: #ffffff;       text-decoration: none;+      border: none;+      border-radius: 0.375rem;       cursor: pointer;       transition: all 0.2s ease;-      font-family: inherit;-    }--    .cta-primary:hover {-      background: #00b86a;-      border-color: #00b86a;-      transform: translateY(-2px);-      box-shadow: 0 8px 20px rgba(0, 217, 132, 0.25);-    }--    .cta-primary:focus {-      outline: 2px solid #00d984;-      outline-offset: 2px;-    }--    .cta-primary:active {+      box-shadow: 0 2px 8px rgba(255, 107, 0, 0.2);+    }++    .btn-primary:hover {+      background-color: #e55a00;+      box-shadow: 0 4px 12px rgba(255, 107, 0, 0.3);+      transform: translateY(-1px);+    }++    .btn-primary:focus {+      outline: 2px solid #FF6B00;+      outline-offset: 3px;+    }++    .btn-primary:active {       transform: translateY(0);-    }--    .prompt-icon {-      font-family: "Monaco", "Menlo", "Courier New", monospace;-      font-weight: 700;-    }--    .cta-secondary {-      display: inline-block;-      padding: 0.875rem 1.5rem;-      color: #00d984;-      border: 2px solid transparent;-      border-bottom: 2px solid #00d984;-      background: none;+      box-shadow: 0 1px 4px rgba(255, 107, 0, 0.2);+    }++    /* Secondary CTA Link */+    .link-secondary {+      display: inline-block;+      padding: 0.5rem 0;       font-size: 1rem;-      font-weight: 600;+      font-weight: 500;+      color: #FF6B00;+      text-decoration: none;+      border-bottom: 1px solid transparent;+      transition: all 0.2s ease;+      position: relative;+    }++    .link-secondary:hover {+      border-bottom-color: #FF6B00;+      color: #ff8533;+    }++    .link-secondary:focus {+      outline: 2px solid #FF6B00;+      outline-offset: 3px;+      border-radius: 0.25rem;+    }++    /* Trust line */+    .trust-line {+      font-size: 0.9375rem;+      color: #999;       letter-spacing: 0.02em;-      text-decoration: none;-      cursor: pointer;-      transition: all 0.2s ease;-      font-family: inherit;-    }--    .cta-secondary:hover {-      color: #00e5a0;-      border-bottom-color: #00e5a0;-    }--    .cta-secondary:focus {-      outline: 2px solid #00d984;-      outline-offset: 2px;-    }--    .trust-line {-      font-size: 0.95rem;-      color: #7a8490;-      letter-spacing: 0.01em;-      opacity: 0;-      animation: fadeIn 0.8s ease-out 0.75s forwards;-    }-+      margin-top: 0;+    }++    /* Reduced motion support */     @media (prefers-reduced-motion: reduce) {-      * {-        animation: none !important;-        transition: none !important;-      }-    }-+      .btn-primary,+      .link-secondary {+        transition: none;+      }++      .btn-primary:hover {+        transform: none;+      }++      .btn-primary:active {+        box-shadow: 0 1px 4px rgba(255, 107, 0, 0.2);+      }+    }++    /* Responsive design */     @media (max-width: 640px) {-      .cta-group {-        flex-direction: column;-        gap: 1rem;-      }--      .cta-primary,-      .cta-secondary {-        width: 100%;+      .hero {+        padding: 2rem 1rem;       }@@ -190,2 +175,6 @@ +      .eyebrow {+        margin-bottom: 2rem;+      }+       .subtext {@@ -194,4 +183,11 @@ -      body {-        padding: 1.5rem;+      .cta-group {+        gap: 0.75rem;+        width: 100%;+        max-width: 300px;+      }++      .btn-primary {+        width: 100%;+        padding: 1rem 1.5rem;       }@@ -201,19 +197,12 @@ <body>-  <div class="hero-container">+  <section class="hero">     <div class="eyebrow">Open source · MIT</div>-     <h1>Know what your tests actually cover.</h1>-     <p class="subtext">Marrow watches a single test run and draws the real map from test to code — no coverage annotations, no config. Find the dead tests. Find the untested lines. In one command.</p>-     <div class="cta-group">-      <a href="#" class="cta-primary">-        <span class="prompt-icon">$</span>-        Install Marrow-      </a>-      <a href="#" class="cta-secondary">Read the docs</a>+      <button class="btn-primary">Install Marrow</button>+      <a href="#" class="link-secondary">Read the docs</a>     </div>--    <div class="trust-line">Works with pytest, Jest, and go test.</div>-  </div>+    <p class="trust-line">Works with pytest, Jest, and go test.</p>+  </section> </body>
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (color palette, weight 0.67) Use the burnt-orange hex `#FF6B00` as the accent color for the primary CTA button background and for links.
  - [L2] (typography, weight 0.67) Give the main headline wide letter-spacing of exactly 0.2em (`letter-spacing: 0.2em`).

pricing_cards

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

Here’s what wasn’t good

The user corrects it, in their own words:

“Use Courier for the body text — font-family: 'Courier New', monospace, not a sans-serif.”

This stuck starting at run 2 — honored from then on.

“Don't round the corners — add a global `* { border-radius: 0 !important; }` reset so every card and button is square.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

What actually changed, line by line (index.html, cold → warm)
@@ -7,12 +7,10 @@   <style>-    * {+    * { border-radius: 0 !important; }++    html, body {       margin: 0;       padding: 0;-      box-sizing: border-box;-    }--    html, body {-      font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Helvetica Neue', sans-serif;-      background: #fafafa;-      color: #1a1a1a;+      font-family: 'Courier New', monospace;+      background-color: #f9f9f9;+      color: #333333;     }@@ -20,7 +18,3 @@     body {-      padding: 60px 20px;-      min-height: 100vh;-      display: flex;-      align-items: center;-      justify-content: center;+      padding: 80px 40px;     }@@ -29,3 +23,3 @@       max-width: 1200px;-      width: 100%;+      margin: 0 auto;     }@@ -34,32 +28,26 @@       text-align: center;-      margin-bottom: 60px;-    }--    .pricing-header h1 {-      font-size: 42px;-      font-weight: 700;-      letter-spacing: -0.5px;-      margin-bottom: 12px;-      color: #1a1a1a;-    }--    .pricing-header p {-      font-size: 18px;-      color: #666;-      font-weight: 400;-      line-height: 1.5;-    }--    .pricing-cards {+      margin-bottom: 80px;+    }++    .pricing-title {+      font-size: 48px;+      font-weight: bold;+      letter-spacing: -1px;+      margin: 0 0 16px 0;+      line-height: 1.2;+    }++    .pricing-subtitle {+      font-size: 16px;+      color: #666666;+      margin: 0;+      font-weight: normal;+      letter-spacing: 0.3px;+    }++    .pricing-grid {       display: grid;-      grid-template-columns: repeat(auto-fit, minmax(320px, 1fr));-      gap: 24px;-      justify-items: center;-    }--    @media (min-width: 768px) {-      .pricing-cards {-        grid-template-columns: repeat(3, 1fr);-        gap: 28px;-      }+      grid-template-columns: repeat(3, 1fr);+      gap: 32px;+      margin-top: 60px;     }@@ -67,8 +55,5 @@     .pricing-card {-      background: white;+      background-color: #ffffff;       border: 1px solid #e0e0e0;-      border-radius: 8px;       padding: 40px 32px;-      width: 100%;-      max-width: 360px;       display: flex;@@ -76,8 +61,2 @@       position: relative;-      transition: all 0.2s ease;-    }--    .pricing-card:hover {-      border-color: #ccc;-      box-shadow: 0 8px 24px rgba(0, 0, 0, 0.08);     }@@ -85,10 +64,5 @@     .pricing-card.recommended {-      border-color: #00a878;-      background: linear-gradient(135deg, #fafafa 0%, white 100%);-      box-shadow: 0 12px 32px rgba(0, 168, 120, 0.12);-      transform: translateY(-8px);-    }--    .pricing-card.recommended:hover {-      box-shadow: 0 16px 40px rgba(0, 168, 120, 0.16);+      background-color: #fafbff;+      border: 2px solid #00d9ff;+      box-shadow: 0 8px 24px rgba(0, 217, 255, 0.1);     }@@ -98,11 +72,9 @@       top: -12px;-      left: 50%;-      transform: translateX(-50%);-      background: #00a878;-      color: white;+      left: 20px;+      background-color: #00d9ff;+      color: #ffffff;       padding: 4px 12px;-      border-radius: 12px;-      font-size: 12px;-      font-weight: 600;-      letter-spacing: 0.5px;+      font-size: 11px;+      font-weight: bold;+      letter-spacing: 1px;       text-transform: uppercase;@@ -112,4 +84,79 @@       font-size: 20px;-      font-weight: 700;+      font-weight: bold;+      margin: 0 0 24px 0;+      margin-top: 12px;+    }++    .pricing-card.recommended .tier-name {+      color: #00d9ff;+    }++    .price {+      font-size: 32px;+      font-weight: bold;       margin-bottom: 8px;+      line-height: 1.2;+    }++    .price-unit {+      font-size: 14px;+      color: #999999;+      font-weight: normal;+      display: block;+    }++    .price-duration {+      font-size: 12px;+      color: #999999;+      margin-bottom: 32px;+    }++    .features-list {+      list-style: none;+      margin: 0 0 40px 0;+      padding: 0;+      flex-grow: 1;+    }++    .features-list li {+      font-size: 14px;+      padding: 12px 0;+      border-bottom: 1px solid #f0f0f0;+      color: #555555;+    }++    .features-list li:last-child {+      border-bottom: none;+    }++    .cta-button {+      background-color: #333333;+      color: #ffffff;+      border: none;+      padding: 14px 24px;+      font-family: 'Courier New', monospace;+      font-size: 14px;+      font-weight: bold;+      letter-spacing: 0.5px;+      cursor: pointer;+      text-transform: uppercase;+      transition: all 0.2s ease;+    }++    .cta-button:hover {+      background-color: #1a1a1a;+      transform: translateY(-2px);+    }++    .cta-button:active {+      transform: translateY(0);+    }++    .cta-button:focus {+      outline: 2px solid #00d9ff;+      outline-offset: 2px;+    }++    .pricing-card.recommended .cta-button {+      background-color: #00d9ff;       color: #1a1a1a;@@ -117,104 +164,33 @@ -    .pricing-info {-      margin-bottom: 32px;-    }--    .price {-      font-size: 48px;-      font-weight: 700;-      color: #00a878;-      line-height: 1.1;-    }--    .price-label {-      font-size: 14px;-      color: #666;-      font-weight: 400;-      margin-top: 4px;-    }--    .features {-      flex: 1;-      margin-bottom: 32px;-    }--    .features ul {-      list-style: none;-    }--    .features li {-      font-size: 14px;-      color: #555;-      padding: 10px 0;-      border-bottom: 1px solid #f0f0f0;-      line-height: 1.6;-      position: relative;-      padding-left: 24px;-    }--    .features li:last-child {-      border-bottom: none;-    }--    .features li:before {-      content: "✓";-      position: absolute;-      left: 0;-      color: #00a878;-      font-weight: 600;-    }--    .cta-button {-      display: block;-      width: 100%;-      padding: 12px 24px;-      border: none;-      border-radius: 6px;-      font-size: 14px;-      font-weight: 600;-      cursor: pointer;-      transition: all 0.2s ease;-      text-align: center;-      text-decoration: none;-      font-family: inherit;-      letter-spacing: 0.3px;-    }--    .pricing-card:not(.recommended) .cta-button {-      background: #f5f5f5;-      color: #1a1a1a;-      border: 1px solid #e0e0e0;-    }--    .pricing-card:not(.recommended) .cta-button:hover {-      background: #ebebeb;-      border-color: #d0d0d0;-    }--    .pricing-card:not(.recommended) .cta-button:active {-      background: #e0e0e0;-    }--    .pricing-card.recommended .cta-button {-      background: #00a878;-      color: white;-    }-     .pricing-card.recommended .cta-button:hover {-      background: #008a6a;-    }--    .pricing-card.recommended .cta-button:active {-      background: #006f54;-    }--    /* Focus states for accessibility */-    .cta-button:focus {-      outline: 2px solid #00a878;-      outline-offset: 2px;-    }--    /* Reduced motion support */+      background-color: #00b8cc;+    }++    @media (max-width: 1024px) {+      .pricing-grid {+        grid-template-columns: repeat(2, 1fr);+        gap: 24px;+      }+    }++    @media (max-width: 768px) {+      body {+        padding: 60px 24px;+      }++      .pricing-title {+        font-size: 36px;+      }++      .pricing-grid {+        grid-template-columns: 1fr;+        gap: 24px;+      }++      .pricing-header {+        margin-bottom: 60px;+      }+    }+     @media (prefers-reduced-motion: reduce) {-      .pricing-card,       .cta-button {@@ -222,39 +198,5 @@       }-      .pricing-card.recommended {++      .cta-button:hover {         transform: none;-      }-    }--    /* Mobile responsiveness */-    @media (max-width: 767px) {-      body {-        padding: 40px 16px;-      }--      .pricing-header {-        margin-bottom: 40px;-      }--      .pricing-header h1 {-        font-size: 32px;-      }--      .pricing-header p {-        font-size: 16px;-      }--      .pricing-card {-        padding: 32px 24px;-      }--      .pricing-card.recommended {-        transform: none;-      }--      .recommended-badge {-        top: -8px;-      }--      .price {-        font-size: 36px;       }@@ -264,23 +206,22 @@ <body>-  <div class="pricing-section">+  <section class="pricing-section">     <div class="pricing-header">-      <h1>Pricing</h1>-      <p>Start free. Upgrade when your team does.</p>+      <h1 class="pricing-title">Pricing</h1>+      <p class="pricing-subtitle">Start free. Upgrade when your team does.</p>     </div> -    <div class="pricing-cards">+    <div class="pricing-grid">       <!-- Solo Tier -->       <div class="pricing-card">-        <div class="tier-name">Solo</div>-        <div class="pricing-info">-          <div class="price">$0</div>-          <div class="price-label">forever</div>+        <h2 class="tier-name">Solo</h2>+        <div class="price">+          $0+          <span class="price-unit">forever</span>         </div>-        <div class="features">-          <ul>-            <li>1 developer</li>-            <li>Local test maps</li>-            <li>Community support</li>-          </ul>-        </div>+        <div class="price-duration"></div>+        <ul class="features-list">+          <li>1 developer</li>+          <li>Local test maps</li>+          <li>Community support</li>+        </ul>         <button class="cta-button">Start free</button>@@ -291,15 +232,14 @@         <div class="recommended-badge">Recommended</div>-        <div class="tier-name">Team</div>-        <div class="pricing-info">-          <div class="price">$12</div>-          <div class="price-label">per user / month</div>+        <h2 class="tier-name">Team</h2>+        <div class="price">+          $12+          <span class="price-unit">/ user / month</span>         </div>-        <div class="features">-          <ul>-            <li>Everything in Solo</li>-            <li>Shared team maps</li>-            <li>CI integration</li>-            <li>Email support</li>-          </ul>-        </div>+        <div class="price-duration"></div>+        <ul class="features-list">+          <li>Everything in Solo</li>+          <li>Shared team maps</li>+          <li>CI integration</li>+          <li>Email support</li>+        </ul>         <button class="cta-button">Start 14-day trial</button>@@ -309,15 +249,14 @@       <div class="pricing-card">-        <div class="tier-name">Enterprise</div>-        <div class="pricing-info">-          <div class="price">Custom</div>-          <div class="price-label">talk to us</div>+        <h2 class="tier-name">Enterprise</h2>+        <div class="price">+          Custom+          <span class="price-unit">pricing</span>         </div>-        <div class="features">-          <ul>-            <li>Everything in Team</li>-            <li>SSO / SAML</li>-            <li>Self-hosted option</li>-            <li>Dedicated support</li>-          </ul>-        </div>+        <div class="price-duration"></div>+        <ul class="features-list">+          <li>Everything in Team</li>+          <li>SSO / SAML</li>+          <li>Self-hosted option</li>+          <li>Dedicated support</li>+        </ul>         <button class="cta-button">Contact sales</button>@@ -325,3 +264,3 @@     </div>-  </div>+  </section> </body>
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (typography, weight 0.67) Set the base body font to Courier: `font-family: 'Courier New', monospace` on the root/body.
Issues (mandatory constraints):
  - [I1] (corner radius) Force perfectly square corners across the whole page: add the rule `* { border-radius: 0 !important; }` as the first rule in the stylesheet. Never round any corner.

pricing_comparison_table

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

Here’s what wasn’t good

The user corrects it, in their own words:

“Stop doing three separate cards - I want a single comparison table, features as rows and tiers as columns, so I can compare across a row instead of hunting through three boxes.”

This stuck starting at run 2 — honored from then on.

“Reorder the columns - Solo, then Enterprise, then Team last, not Team stuck in the middle.”

This hadn't stuck yet within the run budget shown here.

Here’s what changed

The same task, generated again once the learned layer is applied.

What actually changed, line by line (index.html, cold → warm)
@@ -5,16 +5,15 @@ <meta name="viewport" content="width=device-width, initial-scale=1">-<title>Marrow — Pricing</title>+<title>Pricing — Marrow</title> <style>-  /* ---------- Marrow palette ---------- */   :root{-    --ink:    #14111a;   /* terminal ground */-    --pane:   #1d1926;   /* card surface */-    --pane-up:#251f2f;   /* recommended surface */-    --bone:   #ece5da;   /* primary text */-    --ash:    #9a93a6;   /* muted text */-    --marrow: #ff6a4b;   /* signature accent: a covered line */--    --edge: rgba(236,229,218,.10);-    --mono: ui-monospace, "SF Mono", SFMono-Regular, Menlo, Consolas, "Liberation Mono", monospace;-    --sans: system-ui, -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;+    --paper:#EFF0F2;+    --card:#FFFFFF;+    --ink:#16181C;+    --muted:#767A83;+    --rule:#DDE0E4;+    --marrow:#C13B51;+    --band:rgba(193,59,81,.055);++    --mono: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas, "Liberation Mono", monospace;+    --sans: -apple-system, BlinkMacSystemFont, "Segoe UI", "Helvetica Neue", Arial, sans-serif;   }@@ -27,8 +26,8 @@     margin:0;-    background:var(--ink);-    color:var(--bone);+    background:var(--paper);+    color:var(--ink);     font-family:var(--sans);+    font-size:16px;+    line-height:1.5;     -webkit-font-smoothing:antialiased;-    /* faint marrow glow seated behind the recommended tier */-    background-image:radial-gradient(50rem 28rem at 50% 30%, rgba(255,106,75,.075), transparent 70%);   }@@ -36,181 +35,105 @@   .section{-    max-width:1100px;+    max-width:1080px;     margin:0 auto;-    padding:clamp(3.5rem,8vw,7rem) clamp(1.15rem,4vw,2.5rem) clamp(4rem,8vw,7rem);-  }--  /* ---------- Section head ---------- */-  .head{-    display:grid;-    grid-template-columns:minmax(0,1fr) auto;-    gap:2rem 3rem;-    align-items:end;-    padding-bottom:1.5rem;-    border-bottom:1px solid var(--edge);-  }+    padding:clamp(56px,9vw,120px) clamp(16px,5vw,48px);+  }++  /* ---------- heading ---------- */    .eyebrow{-    margin:0 0 1.1rem;-    font-family:var(--mono);-    font-size:.75rem;-    letter-spacing:.19em;+    font-family:var(--mono);+    font-size:11.5px;+    letter-spacing:.2em;     text-transform:uppercase;-    color:var(--ash);-  }-  .eyebrow .prompt{color:var(--marrow)}-  .caret{-    display:inline-block;-    width:.52em;height:1.05em;-    margin-left:.15em;-    vertical-align:-.18em;-    background:var(--marrow);-    animation:blink 1.15s steps(1,end) infinite;-  }-  @keyframes blink{50%{opacity:0}}--  h2{-    margin:0;-    font-family:var(--mono);-    font-size:clamp(2.35rem,6.5vw,3.5rem);-    font-weight:700;-    letter-spacing:-.035em;-    line-height:.95;-  }-  .sub{-    margin:.85rem 0 0;-    font-size:1.0625rem;-    line-height:1.5;-    color:var(--ash);-    max-width:32ch;-  }-  .sub b{color:var(--bone);font-weight:600}--  /* legend: explains the gutter, so it carries information */-  .legend{-    font-family:var(--mono);-    font-size:.7rem;-    line-height:1.75;-    letter-spacing:.05em;-    color:var(--ash);-    text-transform:uppercase;-    margin:0;-    text-align:right;-  }-  .legend .key{-    display:inline-flex;+    color:var(--muted);+    margin:0 0 22px;+    display:flex;     align-items:center;-    gap:.4rem;-    margin-left:.85rem;-  }-  .swatch{width:.5rem;height:.5rem;border-radius:1px}-  .swatch.on{background:var(--marrow)}-  .swatch.off{background:transparent;box-shadow:inset 0 0 0 1px rgba(236,229,218,.28)}--  /* ---------- Card row ---------- */-  .tiers{-    list-style:none;-    margin:2.75rem 0 0;-    padding:0;-    display:grid;-    grid-template-columns:repeat(3,minmax(0,1fr));-    gap:1.15rem;-    align-items:stretch;-  }--  .card{-    position:relative;-    display:grid;-    grid-template-rows:auto auto 1fr auto;-    gap:1.5rem;-    padding:1.6rem 1.6rem 1.6rem 3.1rem;   /* left room for the coverage gutter */-    background:var(--pane);-    border:1px solid var(--edge);-    border-radius:3px;-    transition:transform .22s ease, border-color .22s ease, background-color .22s ease;-  }-  .card:hover{transform:translateY(-3px);border-color:rgba(255,106,75,.35)}--  .card.rec{-    background:var(--pane-up);-    border-color:rgba(255,106,75,.5);-    box-shadow:0 1rem 3rem -1.25rem rgba(0,0,0,.6);-  }-  .card.rec::before{           /* covered-line rule across the top */+    gap:10px;+  }+  .eyebrow::before{     content:"";-    position:absolute;-    inset:-1px -1px auto -1px;+    width:26px;     height:2px;     background:var(--marrow);-    border-radius:3px 3px 0 0;-  }--  /* ---------- Signature element: the coverage gutter ---------- */-  .gutter{-    position:absolute;-    top:1.6rem;-    left:1.35rem;-    display:grid;-    gap:3px;-  }-  .gutter i{-    display:block;-    width:7px;height:7px;-    border-radius:1px;-    box-shadow:inset 0 0 0 1px rgba(236,229,218,.16);-    transform-origin:left center;-  }-  .gutter i.on{-    background:var(--marrow);-    box-shadow:none;-    animation:fill .34s cubic-bezier(.2,.8,.3,1) backwards;-  }-  .gutter i.on:nth-child(1){animation-delay:.05s}-  .gutter i.on:nth-child(2){animation-delay:.10s}-  .gutter i.on:nth-child(3){animation-delay:.15s}-  .gutter i.on:nth-child(4){animation-delay:.20s}-  .gutter i.on:nth-child(5){animation-delay:.25s}-  .gutter i.on:nth-child(6){animation-delay:.30s}-  .gutter i.on:nth-child(7){animation-delay:.35s}-  .gutter i.on:nth-child(8){animation-delay:.40s}-  .gutter i.on:nth-child(9){animation-delay:.45s}-  @keyframes fill{from{transform:scaleX(.15);opacity:0}to{transform:scaleX(1);opacity:1}}--  /* ---------- Card head ---------- */-  .tier-head{-    display:flex;-    align-items:baseline;-    justify-content:space-between;-    gap:.75rem;-    min-height:1.5rem;-  }-  h3{+    flex:none;+  }+  .eyebrow b{font-weight:500;color:var(--ink)}++  h1{+    font-family:var(--mono);+    font-size:clamp(2.5rem,7.5vw,4.25rem);+    line-height:.95;+    letter-spacing:-.045em;+    font-weight:700;     margin:0;-    font-family:var(--mono);-    font-size:.9375rem;-    font-weight:700;+  }++  .sub{+    margin:16px 0 0;+    max-width:34ch;+    font-size:clamp(1rem,1.6vw,1.125rem);+    color:var(--muted);+  }+  .sub b{color:var(--ink);font-weight:600}++  /* ---------- matrix shell ---------- */++  .matrix{+    margin-top:clamp(38px,5vw,60px);+    background:var(--card);+    border:1px solid var(--rule);+    border-radius:4px;+  }++  table{+    width:100%;+    border-collapse:collapse;+    table-layout:fixed;+  }++  col.c-label{width:31%}+  col.c-tier{width:23%}++  th,td{+    text-align:left;+    vertical-align:middle;+    padding:14px clamp(10px,1.5vw,20px);+  }++  /* recommended column band */+  .rec{background:var(--band)}++  /* ---------- tier headers ---------- */++  thead th{+    vertical-align:bottom;+    padding-top:clamp(20px,3vw,32px);+    padding-bottom:22px;+    border-bottom:1px solid var(--ink);+  }+  thead th.corner{border-bottom-color:var(--rule)}++  .badge{+    display:inline-block;+    font-family:var(--mono);+    font-size:9.5px;     letter-spacing:.16em;     text-transform:uppercase;-  }-  .tag{-    font-family:var(--mono);-    font-size:.625rem;+    color:#fff;+    background:var(--marrow);+    padding:4px 7px 3px;+    border-radius:2px;+    margin-bottom:10px;+    white-space:nowrap;+  }++  .tname{+    display:block;+    font-family:var(--mono);+    font-size:clamp(.95rem,1.7vw,1.15rem);     font-weight:700;-    letter-spacing:.14em;-    text-transform:uppercase;-    color:var(--ink);-    background:var(--marrow);-    padding:.24rem .45rem;-    border-radius:2px;-    white-space:nowrap;-  }-  .count{-    margin-left:auto;-    font-family:var(--mono);-    font-size:.6875rem;-    letter-spacing:.1em;-    color:var(--ash);-    white-space:nowrap;-  }--  /* ---------- Price ---------- */+    letter-spacing:-.02em;+  }+   .price{@@ -218,5 +141,5 @@     align-items:baseline;+    gap:5px;     flex-wrap:wrap;-    gap:.5rem;-    min-height:3.25rem;+    margin-top:8px;   }@@ -224,82 +147,168 @@     font-family:var(--mono);-    font-size:2.75rem;+    font-size:clamp(1.4rem,3.2vw,2.1rem);     font-weight:700;     letter-spacing:-.05em;+    font-variant-numeric:tabular-nums;     line-height:1;   }-  .amount.word{font-size:2rem;letter-spacing:-.03em}-  .unit{-    font-family:var(--mono);-    font-size:.75rem;-    letter-spacing:.06em;-    color:var(--ash);-  }--  /* ---------- Features ---------- */-  .feats{list-style:none;margin:0;padding:0;display:grid;gap:.7rem;align-content:start}-  .feats li{-    display:grid;-    grid-template-columns:1rem 1fr;-    gap:.55rem;-    font-size:.9375rem;-    line-height:1.4;-  }-  .feats .mk{-    font-family:var(--mono);-    font-size:.8125rem;-    color:var(--marrow);-    line-height:1.55;-  }-  .feats li.inherit{color:var(--ash)}-  .feats li.inherit .mk{color:var(--ash)}-  .feats li.inherit b{color:var(--bone);font-weight:600}--  /* ---------- CTA ---------- */-  .cta{+  .per{+    font-family:var(--mono);+    font-size:10.5px;+    color:var(--muted);+    letter-spacing:.02em;+    line-height:1.2;+  }++  /* coverage bar — real data: features included / 7 */+  .cov{margin-top:16px}+  .cov-track{+    height:5px;+    background:var(--rule);+    border-radius:1px;+    overflow:hidden;+  }+  .cov-fill{     display:block;+    height:100%;+    width:var(--pct);+    background:var(--marrow);+    transform-origin:left center;+    animation:fill .75s cubic-bezier(.22,.68,.24,1) both;+    animation-delay:var(--d,0s);+  }+  .cov-fill.dim{background:#B9BDC4}+  @keyframes fill{from{transform:scaleX(0)}to{transform:scaleX(1)}}++  .cov-label{+    display:block;+    margin-top:7px;+    font-family:var(--mono);+    font-size:10px;+    letter-spacing:.08em;+    text-transform:uppercase;+    color:var(--muted);+    font-variant-numeric:tabular-nums;+  }++  /* ---------- group + feature rows ---------- */++  .group th{+    font-family:var(--mono);+    font-size:10px;+    letter-spacing:.2em;+    text-transform:uppercase;+    font-weight:500;+    color:var(--muted);+    padding-top:26px;+    padding-bottom:7px;+  }++  tbody tr.feat{transition:background .12s linear}+  tbody tr.feat:hover{background:rgba(22,24,28,.028)}+  tbody tr.feat:hover .rec{background:rgba(193,59,81,.085)}++  tbody tr.feat > *{border-top:1px solid var(--rule)}+  tbody tr.group + tr.feat > *{border-top:0}++  th[scope=row]{+    font-family:var(--sans);+    font-size:14.5px;+    font-weight:500;+    letter-spacing:-.005em;+    color:var(--ink);+  }++  td.cell{+    font-family:var(--mono);+    font-size:12px;+    letter-spacing:-.01em;+    color:var(--muted);+  }+  td.cell.on{color:var(--ink)}++  /* markers */+  .mk{display:inline-block;vertical-align:middle}+  .mk-yes{+    width:9px;height:9px;+    background:var(--marrow);+    border-radius:1px;+  }+  .mk-no{+    width:11px;height:1px;+    background:#C3C7CD;+  }++  .vh{+    position:absolute;+    width:1px;height:1px;+    margin:-1px;padding:0;border:0;+    overflow:hidden;+    clip:rect(0 0 0 0);+    clip-path:inset(50%);+    white-space:nowrap;+  }++  /* ---------- CTA row ---------- */++  tfoot td,tfoot th{+    padding-top:22px;+    padding-bottom:clamp(20px,3vw,30px);+    border-top:1px solid var(--ink);+  }+  tfoot th.corner{border-top-color:var(--rule)}++  .btn{+    display:block;+    width:100%;+    font-family:var(--mono);+    font-size:11.5px;+    letter-spacing:.04em;+    font-weight:500;     text-align:center;-    font-family:var(--mono);-    font-size:.8125rem;-    font-weight:700;-    letter-spacing:.1em;-    text-transform:uppercase;     text-decoration:none;-    padding:.9rem 1rem;-    border-radius:2px;-    color:var(--bone);+    padding:11px 8px;+    border-radius:3px;+    border:1px solid var(--ink);+    color:var(--ink);     background:transparent;-    border:1px solid rgba(236,229,218,.28);-    transition:background-color .18s ease, color .18s ease, border-color .18s ease;-  }-  .cta:hover{border-color:var(--bone);background:rgba(236,229,218,.07)}-  .cta.solid{+    transition:background .14s linear,color .14s linear,border-color .14s linear;+  }+  .btn:hover{background:var(--ink);color:var(--card)}+  .btn-primary{     background:var(--marrow);     border-color:var(--marrow);-    color:#1a0d09;-  }-  .cta.solid:hover{background:#ff8266;border-color:#ff8266}--  a:focus-visible,-  .card:focus-visible{+    color:#fff;+  }+  .btn-primary:hover{background:#A32F42;border-color:#A32F42;color:#fff}++  a:focus-visible,.btn:focus-visible{     outline:2px solid var(--marrow);-    outline-offset:3px;-  }--  .foot{-    margin:1.75rem 0 0;-    font-family:var(--mono);-    font-size:.6875rem;-    letter-spacing:.11em;-    text-transform:uppercase;-    color:var(--ash);-  }--  /* ---------- Responsive ---------- */-  @media (max-width:900px){-    .head{grid-template-columns:1fr;align-items:start}-    .legend{text-align:left}-    .legend .key{margin:0 .85rem 0 0}-    .tiers{grid-template-columns:1fr;gap:1rem}-    .card:hover{transform:none}-    .amount{font-size:2.375rem}+    outline-offset:2px;+  }++  .note{+    margin:20px 0 0;+    font-family:var(--mono);+    font-size:11px;+    letter-spacing:.03em;+    color:var(--muted);+    display:flex;+    align-items:flex-start;+    gap:9px;+  }+  .note .mk-yes{margin-top:5px;flex:none}++  /* ---------- narrow screens ---------- */++  @media (max-width:760px){+    col.c-label{width:34%}+    col.c-tier{width:22%}+    th,td{padding:12px 8px}+    .price{gap:3px}+    .per{font-size:9.5px}+    th[scope=row]{font-size:13px}+    td.cell{font-size:11px}+    .badge{font-size:8.5px;letter-spacing:.1em;padding:3px 5px 2px}+    .cov-label{font-size:9px;letter-spacing:.04em}+    .btn{font-size:10px;padding:10px 4px;letter-spacing:.02em}   }@@ -307,4 +316,3 @@   @media (prefers-reduced-motion:reduce){-    *,*::before,*::after{animation:none !important;transition:none !important}-    .card:hover{transform:none}+    *{animation:none!important;transition:none!important}   }@@ -314,108 +322,130 @@ -<section class="section" aria-labelledby="pricing-title">--  <div class="head">-    <div>-      <p class="eyebrow"><span class="prompt">$</span> marrow plans<span class="caret" aria-hidden="true"></span></p>-      <h2 id="pricing-title">Pricing</h2>-      <p class="sub"><b>Start free.</b> Upgrade when your team does.</p>-    </div>--    <p class="legend">-      Coverage gutter — capabilities unlocked-      <br>-      <span class="key"><span class="swatch on" aria-hidden="true"></span> included</span>-      <span class="key"><span class="swatch off" aria-hidden="true"></span> not in tier</span>-    </p>+<main class="section">++  <p class="eyebrow"><b>marrow</b> &mdash; test&#8209;coverage mapping for CLI&#8209;first teams</p>++  <h1>Pricing</h1>+  <p class="sub">Start free. <b>Upgrade when your team does.</b></p>++  <div class="matrix">+    <table>+      <caption class="vh">Marrow plan comparison: features by tier</caption>++      <colgroup>+        <col class="c-label">+        <col class="c-tier">+        <col class="c-tier">+        <col class="c-tier">+      </colgroup>++      <thead>+        <tr>+          <th class="corner" scope="col"><span class="vh">Feature</span></th>++          <th scope="col">+            <span class="tname">Solo</span>+            <span class="price">+              <span class="amount">$0</span>+              <span class="per">/ forever</span>+            </span>+            <span class="cov">+              <span class="cov-track"><span class="cov-fill dim" style="--pct:43%;--d:.1s"></span></span>+              <span class="cov-label">3 / 7 features</span>+            </span>+          </th>++          <th class="rec" scope="col">+            <span class="badge">Recommended</span>+            <span class="tname">Team</span>+            <span class="price">+              <span class="amount">$12</span>+              <span class="per">/ user / month</span>+            </span>+            <span class="cov">+              <span class="cov-track"><span class="cov-fill" style="--pct:71%;--d:.22s"></span></span>+              <span class="cov-label">5 / 7 features</span>+            </span>+          </th>++          <th scope="col">+            <span class="tname">Enterprise</span>+            <span class="price">+              <span class="amount">Custom</span>+              <span class="per">pricing</span>+            </span>+            <span class="cov">+              <span class="cov-track"><span class="cov-fill dim" style="--pct:100%;--d:.34s"></span></span>+              <span class="cov-label">7 / 7 features</span>+            </span>+          </th>+        </tr>+      </thead>++      <tbody>+        <tr class="group"><th colspan="4" scope="colgroup">Mapping</th></tr>++        <tr class="feat">+          <th scope="row">Local test maps</th>+          <td class="cell"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+          <td class="cell rec"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+          <td class="cell"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+        </tr>+        <tr class="feat">+          <th scope="row">Shared team maps</th>+          <td class="cell"><span class="mk mk-no"></span><span class="vh">Not included</span></td>+          <td class="cell rec"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+          <td class="cell"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+        </tr>+        <tr class="feat">+          <th scope="row">CI integration</th>+          <td class="cell"><span class="mk mk-no"></span><span class="vh">Not included</span></td>+          <td class="cell rec"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+          <td class="cell"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+        </tr>++        <tr class="group"><th colspan="4" scope="colgroup">Access</th></tr>++        <tr class="feat">+          <th scope="row">Developers</th>+          <td class="cell on">1</td>+          <td class="cell on rec">Unlimited</td>+          <td class="cell on">Unlimited</td>+        </tr>+        <tr class="feat">+          <th scope="row">SSO / SAML</th>+          <td class="cell"><span class="mk mk-no"></span><span class="vh">Not included</span></td>+          <td class="cell rec"><span class="mk mk-no"></span><span class="vh">Not included</span></td>+          <td class="cell"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+        </tr>+        <tr class="feat">+          <th scope="row">Self&#8209;hosted option</th>+          <td class="cell"><span class="mk mk-no"></span><span class="vh">Not included</span></td>+          <td class="cell rec"><span class="mk mk-no"></span><span class="vh">Not included</span></td>+          <td class="cell"><span class="mk mk-yes"></span><span class="vh">Included</span></td>+        </tr>++        <tr class="group"><th colspan="4" scope="colgroup">Support</th></tr>++        <tr class="feat">+          <th scope="row">Support channel</th>+          <td class="cell on">Community</td>+          <td class="cell on rec">Email</td>+          <td class="cell on">Dedicated</td>+        </tr>+      </tbody>++      <tfoot>+        <tr>+          <th class="corner" scope="row"><span class="vh">Get started</span></th>+          <td><a class="btn" href="#solo">Start free</a></td>+          <td class="rec"><a class="btn btn-primary" href="#team">Start 14&#8209;day trial</a></td>+          <td><a class="btn" href="#enterprise">Contact sales</a></td>+        </tr>+      </tfoot>+    </table>   </div> -  <ul class="tiers">--    <!-- ─── Solo ─── -->-    <li class="card">-      <span class="gutter" aria-hidden="true">-        <i class="on"></i><i class="on"></i><i class="on"></i>-        <i></i><i></i><i></i><i></i><i></i><i></i>-      </span>--      <div class="tier-head">-        <h3>Solo</h3>-        <span class="count">3/9</span>-      </div>--      <p class="price">-        <span class="amount">$0</span>-        <span class="unit">/ forever</span>-      </p>--      <ul class="feats">-        <li><span class="mk" aria-hidden="true">▪</span> 1 developer</li>-        <li><span class="mk" aria-hidden="true">▪</span> Local test maps</li>-        <li><span class="mk" aria-hidden="true">▪</span> Community support</li>-      </ul>--      <a class="cta" href="#">Start free</a>-    </li>--    <!-- ─── Team (recommended) ─── -->-    <li class="card rec">-      <span class="gutter" aria-hidden="true">-        <i class="on"></i><i class="on"></i><i class="on"></i>-        <i class="on"></i><i class="on"></i><i class="on"></i>-        <i></i><i></i><i></i>-      </span>--      <div class="tier-head">-        <h3>Team</h3>-        <span class="count">6/9</span>-        <span class="tag">Recommended</span>-      </div>--      <p class="price">-        <span class="amount">$12</span>-        <span class="unit">/ user / month</span>-      </p>--      <ul class="feats">-        <li class="inherit"><span class="mk" aria-hidden="true">↳</span> Everything in <b>Solo</b></li>-        <li><span class="mk" aria-hidden="true">▪</span> Shared team maps</li>-        <li><span class="mk" aria-hidden="true">▪</span> CI integration</li>-        <li><span class="mk" aria-hidden="true">▪</span> Email support</li>-      </ul>--      <a class="cta solid" href="#">Start 14-day trial</a>-    </li>--    <!-- ─── Enterprise ─── -->-    <li class="card">-      <span class="gutter" aria-hidden="true">-        <i class="on"></i><i class="on"></i><i class="on"></i>-        <i class="on"></i><i class="on"></i><i class="on"></i>-        <i class="on"></i><i class="on"></i><i class="on"></i>-      </span>--      <div class="tier-head">-        <h3>Enterprise</h3>-        <span class="count">9/9</span>-      </div>--      <p class="price">-        <span class="amount word">Custom</span>-      </p>--      <ul class="feats">-        <li class="inherit"><span class="mk" aria-hidden="true">↳</span> Everything in <b>Team</b></li>-        <li><span class="mk" aria-hidden="true">▪</span> SSO / SAML</li>-        <li><span class="mk" aria-hidden="true">▪</span> Self-hosted option</li>-        <li><span class="mk" aria-hidden="true">▪</span> Dedicated support</li>-      </ul>--      <a class="cta" href="#">Contact sales</a>-    </li>--  </ul>--  <p class="foot">USD · billed monthly · cancel from the CLI</p>--</section>+  <p class="note"><span class="mk mk-yes"></span> Each tier includes everything in the tier before it &mdash; Team builds on Solo, Enterprise builds on Team.</p>++</main>
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (layout structure, weight 0.80) Don't lay the tiers out as three side-by-side cards. Restructure this as a single comparison table instead: one row per feature, one column per tier, so a reader can scan across a row and compare the tiers directly at a glance.

plotnine

Grammar-of-graphics plots, rendered as images.

plotnine_gtcars_hp_faceted

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

cold
Here’s what wasn’t good

The user corrects it, in their own words:

“Don't cram every country onto one plot with colors - facet it, one small panel per country so each country's shape is easy to read on its own.”

This stuck starting at run 2 — honored from then on.

“Give each facet its own y-axis scale - don't force them to share one range, some countries' prices barely move compared to others.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

warm
What actually changed, line by line (plot.py, cold → warm)
@@ -1,13 +1,21 @@-"""Price vs horsepower for the gtcars sports cars, colored by country of origin.+"""Price vs horsepower for the gtcars sports cars, as small multiples by country. -Step 1 (data.md):     47 rows, one row per car; hp / msrp already numeric, no NaNs.-Step 2 (geoms.md):    relationship between two numeric vars -> geom_point, pinned point_size,-                      + a single overall geom_smooth(method="lm") trend.-Step 3 (big_color.md): hero = ctry_origin, CATEGORICAL (5 groups, <= 6) -> qualitative-                      Okabe-Ito palette via house_palette(); color, not facets.-Step 4:               price spans $53.9k -> $1.42M (26x, one extreme LaFerrari), so the-                      y axis is log10 -- on a linear axis 46 of 47 cars pile into the-                      bottom third and the linear fit dips below $0. $ tick labels with-                      thousands separators; templated title "{Y} vs {X}".-Step 5/6 (small_color.md): house theme + save_plot() at the house size/dpi.+Flowchart trace (plotnine skill):+  1. DATA (data.md)      -- gtcars.csv is one row per car; coerce hp/msrp to numeric,+                            drop rows that can't be positioned. Already tidy (long).+  2. FORM (geoms.md)     -- "relationship between two numeric vars" -> geom_point,+                            size = HOUSE_STYLE["point_size"]. 47 points, so a light+                            alpha is enough for overplotting; no jitter/bin2d needed.+                            No geom_smooth: some panels hold only 2-4 cars, where an+                            lm fit would be a straight line through noise.+  3. BIG COLOR           -- country of origin is shown by FACETING, not by hue+                            (small multiples per panel), so no variable is+                            color-encoded: single house accent, no legend+                            (big_color.md, "No hero variable" section).+  4. SCALES & LABELS     -- MSRP is money -> label_currency; title from the+                            relationship template "{Y} vs {X}"; free y per panel so+                            narrow-range countries aren't flattened to a line.+  5. SMALL COLOR         -- apply_house_style(); legend suppressed; landscape figure+                            size (not a horizontal-bar ranking, so no tall branch).+  6. RENDER              -- save_plot() -> p.save("plot.png", ...). """@@ -18,15 +26,9 @@ from mizani.labels import label_currency-from plotnine import aes, geom_point, geom_smooth, ggplot, labs, scale_y_continuous+from plotnine import aes, element_text, facet_wrap, geom_point, ggplot, labs, scale_y_continuous, theme -SKILL = Path(".claude/skills/plotnine").resolve()-sys.path.insert(0, str(SKILL / "scripts"))-from pn_house_style import (  # noqa: E402-    HOUSE_STYLE,-    apply_house_style,-    house_palette,-    humanize_labels,-    save_plot,-)+SKILL_SCRIPTS = Path(".claude/skills/plotnine/scripts").resolve()+sys.path.insert(0, str(SKILL_SCRIPTS))+from pn_house_style import HOUSE_STYLE, apply_house_style, humanize_labels, save_plot -# --- Step 1: one clean, correctly-typed, tidy frame -------------------------+# --- Step 1: one clean, correctly-typed, tidy DataFrame ----------------------- df = pd.read_csv("gtcars.csv")@@ -34,36 +36,25 @@ df["msrp"] = pd.to_numeric(df["msrp"], errors="coerce")-df = df.dropna(subset=["hp", "msrp", "ctry_origin"])+df = df.dropna(subset=["hp", "msrp"])++# Panel order: most-represented country first (data-driven, not alphabetical).+country_order = df["ctry_origin"].value_counts().index.tolist()+df["ctry_origin"] = pd.Categorical(df["ctry_origin"], categories=country_order, ordered=True)  lab = humanize_labels(-    "hp",-    "msrp",-    "ctry_origin",-    overrides={-        "hp": "Horsepower (hp)",-        "msrp": "Price (MSRP, USD)",-        "ctry_origin": "Country of origin",-    },+    "hp", "msrp", "ctry_origin",+    overrides={"hp": "Horsepower", "msrp": "MSRP (USD)", "ctry_origin": "Country of Origin"}, ) -# --- Steps 2-4: form, Big Color, scales & labels ----------------------------+# --- Steps 2-4: geom, accent color (no legend), scales, labels --------------- p = (-    ggplot(df, aes("hp", "msrp", color="ctry_origin"))-    # group=1 keeps this one overall trend line, not one per color group-    + geom_smooth(-        aes(group=1), method="lm", se=False,-        color="#222222", size=0.6, linetype="dashed",-    )-    + geom_point(size=HOUSE_STYLE["point_size"], alpha=0.8)-    + house_palette("qualitative", aes="color", name=lab["ctry_origin"])-    + scale_y_continuous(-        trans="log10",-        breaks=[50_000, 100_000, 200_000, 400_000, 800_000, 1_600_000],-        labels=label_currency(prefix="$", precision=0, big_mark=","),-    )-    # title template (geoms.md, relationship): "{Y} vs {X}"+    ggplot(df, aes("hp", "msrp"))+    + geom_point(color=HOUSE_STYLE["accent"], size=HOUSE_STYLE["point_size"], alpha=0.8)+    + facet_wrap("~ctry_origin", ncol=3, scales="free_y")+    + scale_y_continuous(labels=label_currency(prefix="$", precision=0, big_mark=","))+    # title template (geoms.md): relationship -> "{Y} vs {X}"     + labs(         title=f"{lab['msrp']} vs {lab['hp']}",-        subtitle="Each point is one model; dashed line is the overall log-linear fit",+        subtitle=f"By {lab['ctry_origin'].lower()}; each panel keeps its own price range",         x=lab["hp"],-        y=f"{lab['msrp']}, log scale",+        y=lab["msrp"],     )@@ -71,4 +62,14 @@ -# --- Steps 5-6: house theme, then the mandatory renderer ---------------------p = apply_house_style(p)+# --- Step 5: Small Color / theme ---------------------------------------------+p = apply_house_style(p, legend_position="none")+p = p + theme(+    # free_y gives every panel its own currency tick labels, so panels need room+    # between them (units are a FRACTION of the figure dimension, default 0.01)+    panel_spacing_x=0.10,+    panel_spacing_y=0.06,+    plot_subtitle=element_text(size=11, color=HOUSE_STYLE["text"]),+    strip_text=element_text(size=11, weight="bold", color=HOUSE_STYLE["text"]),+)++# --- Step 6: render ----------------------------------------------------------- save_plot(p, "plot.png")
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (layout structure, weight 0.67) Don't color-encode country of origin on one combined scatter plot. Split it into small multiples instead - facet by country of origin with facet_wrap(), one panel per country.
  - [L2] (scales, weight 0.67) Let each facet panel use its own y-axis range (`scales="free_y"`) instead of a shared axis, since some countries have a much narrower price range than others and a shared axis would flatten them to near-invisible lines.

plotnine_gtcars_hp_msrp

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

cold
Here’s what wasn’t good

The user corrects it, in their own words:

“Use the Dark2 qualitative palette for the country colors.”

This stuck starting at run 2 — honored from then on.

“Log the price axis — use scale_y_log10().”

This stuck starting at run 2 — honored from then on.

“Move the legend to the bottom, not the right.”

This stuck starting at run 2 — honored from then on.

“Make the points bigger — size=3.”

This stuck starting at run 2 — honored from then on.

“Add some transparency to the points — alpha=0.6.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

warm
What actually changed, line by line (plot.py, cold → warm)
@@ -2,15 +2,14 @@ -Flowchart decisions (plotnine skill):-1. DATA (data.md): one row per car already; coerce hp/msrp to numeric, drop rows that-   can't be positioned. Frame is already tidy -- no melt needed.-2. FORM (geoms.md): "relationship between two numeric vars" -> geom_point at the pinned-   HOUSE_STYLE["point_size"], plus a geom_smooth(method="lm") trend answering the same-   question (it recedes behind the points: thinner, neutral, dashed).-3. BIG COLOR (big_color.md): hero = ctry_origin, CATEGORICAL with 5 groups (<= ~6, so-   color rather than facets) -> qualitative Okabe-Ito via house_palette().-4. SCALES & LABELS (api.md, small_color.md #7): money axis gets a "$" + thousands-   separators; templated relationship title "{Y} vs {X}"; humanized axis labels + units.-5. SMALL COLOR (small_color.md): apply_house_style() -- theme_minimal, light major-only-   gridlines, pinned text sizes, legend right.-6. RENDER: save_plot() -> p.save("plot.png", ...), the only renderer.+Flowchart trace:+1. DATA (data.md)    -- 47 rows, one row per car: already tidy/long. Coerce hp/msrp to+                        numeric and drop rows that can't be positioned (none here).+2. FORM (geoms.md)   -- question = relationship between two numeric vars -> geom_point,+                        with a dashed lm overlay answering the same question.+3. BIG COLOR         -- hero = ctry_origin, a categorical (5 unordered groups) -> a+                        qualitative palette (ColorBrewer "Dark2", per user preference).+4. SCALES & LABELS   -- msrp is heavily right-skewed ($53.9k -> $1.42M), so the price+                        axis is log10 with $ thousands-separated ticks; templated title+                        "{Y} vs {X}".+5. SMALL COLOR       -- house theme via apply_house_style(), legend at the bottom.+6. RENDER            -- save_plot() -> p.save("plot.png", ...) at the house size/dpi. """@@ -21,23 +20,22 @@ from mizani.labels import label_currency-from plotnine import aes, geom_point, geom_smooth, ggplot, labs, scale_y_continuous--SKILL_SCRIPTS = (-    Path(__file__).resolve().parent / ".claude" / "skills" / "plotnine" / "scripts"-)-sys.path.insert(0, str(SKILL_SCRIPTS))-from pn_house_style import (  # noqa: E402-    HOUSE_STYLE,-    apply_house_style,-    house_palette,-    humanize_labels,-    save_plot,+from plotnine import (+    aes,+    geom_point,+    geom_smooth,+    ggplot,+    labs,+    scale_color_brewer,+    scale_y_log10, ) -# --- Step 1: data -----------------------------------------------------------------df = pd.read_csv(Path(__file__).resolve().parent / "gtcars.csv")+HERE = Path(__file__).resolve().parent+sys.path.insert(0, str(HERE / ".claude" / "skills" / "plotnine" / "scripts"))+from pn_house_style import apply_house_style, humanize_labels, save_plot  # noqa: E402++# --- Step 1: one clean, correctly-typed, tidy DataFrame ----------------------+df = pd.read_csv(HERE / "gtcars.csv") df["hp"] = pd.to_numeric(df["hp"], errors="coerce") df["msrp"] = pd.to_numeric(df["msrp"], errors="coerce")-df = df.dropna(subset=["hp", "msrp", "ctry_origin"])+df = df.dropna(subset=["hp", "msrp"]) -# --- Step 4 labels: humanize only what is VISIBLE (aes keeps raw column names) ---- lab = humanize_labels(@@ -53,13 +51,9 @@ -# --- Steps 2-4: build ------------------------------------------------------------+# --- Steps 2-4: geom, Big Color, scales & labels ---------------------------- p = (     ggplot(df, aes("hp", "msrp", color="ctry_origin"))-    + geom_smooth(-        method="lm", se=False, color="#222222", size=0.6, linetype="dashed"-    )-    + geom_point(size=HOUSE_STYLE["point_size"], alpha=0.8)-    + house_palette("qualitative", aes="color", name=lab["ctry_origin"])-    + scale_y_continuous(-        labels=label_currency(prefix="$", precision=0, big_mark=",")-    )+    + geom_point(size=3, alpha=0.6)  # taught: larger, translucent points+    + geom_smooth(method="lm", se=False, color="#222222", size=0.6, linetype="dashed")+    + scale_color_brewer(type="qual", palette="Dark2", name=lab["ctry_origin"])+    + scale_y_log10(labels=label_currency(prefix="$", precision=0, big_mark=","))     # title template (geoms.md): relationship -> "{Y} vs {X}"@@ -68,4 +62,6 @@ -# --- Steps 5-6: theme + the mandatory renderer ------------------------------------p = apply_house_style(p)-save_plot(p, str(Path(__file__).resolve().parent / "plot.png"))+# --- Step 5: Small Color / theme -------------------------------------------+p = apply_house_style(p, legend_position="bottom")++# --- Step 6: render (the only renderer) ------------------------------------+save_plot(p, str(HERE / "plot.png"))
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L2] (scales, weight 0.67) Put the price (msrp) axis on a log scale with `scale_y_log10()`.
  - [L1] (color encoding, weight 0.67) Color-encode country of origin with the ColorBrewer qualitative `Dark2` palette via `scale_color_brewer(type="qual", palette="Dark2")`.
  - [L3] (legend placement, weight 0.67) Place the legend at the bottom with `legend_position="bottom"` in theme().
  - [L5] (geoms, weight 0.67) Make the scatter points translucent, `alpha=0.6` in geom_point.
  - [L4] (geoms, weight 0.67) Make the scatter points larger, `size=3` in geom_point.

plotnine_islands_top_sizes

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

cold
Here’s what wasn’t good

The user corrects it, in their own words:

“Use theme_classic() as the base, not theme_minimal.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

warm
What actually changed, line by line (plot.py, cold → warm)
@@ -1,15 +1,10 @@-"""Rank the largest landmasses in islands.csv by area (horizontal bar)."""-import sys+"""Chart the largest landmasses in islands.csv by area (top-N ranking)."""+import pandas as pd+from plotnine import (+    ggplot, aes, geom_col, coord_flip, labs, theme_classic, theme,+    element_line, element_text, element_blank, scale_y_continuous,+) -import pandas as pd-from plotnine import aes, geom_col, ggplot, labs, coord_flip, scale_y_continuous, theme--sys.path.insert(-    0,-    ".claude/skills/plotnine/scripts",-)-from pn_house_style import apply_house_style, humanize_labels, save_plot, HOUSE_STYLE--# 1. UNDERSTAND THE DATA ------------------------------------------------------# name (category), size (numeric area, thousands of sq miles). Already tidy.+# --- Step 1: understand + tidy the data ------------------------------------+# Two columns: name (category) + size (area, thousands of square miles). df = pd.read_csv("islands.csv")@@ -18,7 +13,9 @@ -# Ranking: slice to the largest landmasses so the biggest are easy to compare.-TOP_N = 15-top = df.sort_values("size", ascending=False).head(TOP_N).copy()+# --- Step 2: choose the form -----------------------------------------------+# Ranking/amount across categories -> geom_col, horizontal (long labels),+# sliced to the top N so the biggest landmasses are easy to compare.+N = 15+top = df.sort_values("size", ascending=False).head(N) -# Sort bars by value (ascending so the largest lands on top after coord_flip).+# Sort so the largest ends up at the TOP after coord_flip(). order = top.sort_values("size", ascending=True)["name"].tolist()@@ -26,17 +23,14 @@ -# 2/3/4. FORM, COLOR, LABELS --------------------------------------------------lab = humanize_labels(-    "name", "size",-    overrides={"name": "Landmass", "size": "Area (thousands of sq mi)"},-)-+# --- Steps 3-4: single house accent (no hero color var), labels + units ----+HOUSE_ACCENT = "#2C6FB3"+Y_TITLE = "Area"  # humanized value label reused in the templated title p = (     ggplot(top, aes(x="name", y="size"))-    + geom_col(fill=HOUSE_STYLE["accent"])  # single house accent -> no legend+    + geom_col(fill=HOUSE_ACCENT)     + coord_flip()-    + scale_y_continuous(labels=lambda v: [f"{x:,.0f}" for x in v])+    + scale_y_continuous(labels=lambda vs: [f"{v:,.0f}" for v in vs])     + labs(-        title=f"Top {TOP_N} by {lab['size']}",-        x=lab["name"],-        y=lab["size"],+        title=f"Top {N} by {Y_TITLE}",+        x="Landmass",+        y="Area (thousands of sq mi)",     )@@ -44,8 +38,14 @@ -# 5. THEME (tall figure size is mandatory for a horizontal-bar ranking) -------p = apply_house_style(p, legend_position="none")-p += theme(figure_size=(6, 7))  # tall size: mandatory for horizontal-bar ranking+# --- Step 5: Small Color / theme (L1: theme_classic base) ------------------+p = p + theme_classic(base_size=12) + theme(+    figure_size=(6, 7),  # mandatory TALL size for a horizontal-bar ranking+    plot_title=element_text(size=15, weight="bold", color="#222222"),+    axis_title=element_text(size=12, color="#222222"),+    axis_text=element_text(size=10, color="#222222"),+    panel_grid_major_x=element_line(color="#E6E6E6", size=0.4),+    panel_grid_minor=element_blank(),+    legend_position="none",+) -# 6. RENDER -------------------------------------------------------------------save_plot(p, "plot.png", width=6, height=7)-print("wrote plot.png")+# --- Step 6: render (the only renderer) ------------------------------------+p.save("plot.png", width=6, height=7, dpi=200, verbose=False)
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (theme, weight 0.67) Use plotnine's `theme_classic()` as the base theme instead of the house `theme_minimal`.

plotnine_sp500_close_trend

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

cold
Here’s what wasn’t good

The user corrects it, in their own words:

“Make the line #D1495B (crimson), not the default blue.”

This stuck starting at run 2 — honored from then on.

“Make the line thicker — geom_line(size=1.5).”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

warm
What actually changed, line by line (plot.py, cold → warm)
@@ -5,2 +5,3 @@ import pandas as pd+from mizani.labels import label_currency from plotnine import (@@ -13,12 +14,11 @@ )-from mizani.labels import label_currency -# House style helper (theme, mark sizes, save) lives with the skill.-sys.path.insert(-    0,-    str(Path(".claude/skills/plotnine/scripts").resolve()),-)-from pn_house_style import HOUSE_STYLE, apply_house_style, humanize_labels, save_plot+# House style helper (single source of truth for theme/frame/mark sizes).+SKILL_SCRIPTS = Path(".claude/skills/plotnine/scripts")+sys.path.insert(0, str(SKILL_SCRIPTS))+from pn_house_style import apply_house_style, humanize_labels, save_plot  # noqa: E402 -# --- Step 1: understand & clean the data -> one tidy, correctly-typed frame ---+# ---------------------------------------------------------------------------+# Step 1 — UNDERSTAND & CLEAN THE DATA -> one tidy, correctly-typed frame.+# --------------------------------------------------------------------------- df = pd.read_csv("sp500.csv")@@ -26,12 +26,19 @@ df["close"] = pd.to_numeric(df["close"], errors="coerce")-df = df.dropna(subset=["date", "close"])+df = df.dropna(subset=["date", "close"]).sort_values("date") -# Most recent year of data: last 365 days up to the latest observation.+# Most recent year of data: the last 365 days up to the latest observation. latest = df["date"].max()-recent = df[df["date"] >= latest - pd.DateOffset(years=1)].sort_values("date")+recent = df[df["date"] >= latest - pd.Timedelta(days=365)].copy() -# Span is ~1 year (>90 days and <=2 years) -> date_breaks "2 months", labels "%b %Y".+# Date span for the pinned date-axis defaults (api.md): ~1 year here+# -> > 90 days and <= 2 years -> "2 months" / "%b %Y".+lab = humanize_labels("date", "close", overrides={"close": "Closing Price (USD)",+                                                  "date": "Date"}) -# --- Steps 2-4: single-series line, house accent (no hero color variable) ----lab = humanize_labels("date", "close", overrides={"close": "Closing Price (USD)"})+# ---------------------------------------------------------------------------+# Steps 2-4 — FORM (trend -> geom_line), BIG COLOR (single series, no hero+# variable -> constant accent, no legend; user prefers crimson + thicker line),+# SCALES & LABELS (currency y, pinned date breaks, templated title).+# ---------------------------------------------------------------------------+TREND_COLOR = "#D1495B"  # crimson (taught preference, overrides house accent) @@ -39,3 +46,3 @@     ggplot(recent, aes(x="date", y="close"))-    + geom_line(color=HOUSE_STYLE["accent"], size=HOUSE_STYLE["line_size"])+    + geom_line(color=TREND_COLOR, size=1.5)     + scale_x_date(date_breaks="2 months", date_labels="%b %Y")@@ -49,6 +56,11 @@ -# --- Step 5: house theme, no legend (single un-grouped series) ---+# ---------------------------------------------------------------------------+# Step 5 — SMALL COLOR / THEME (no legend for a constant-color single series).+# --------------------------------------------------------------------------- p = apply_house_style(p, legend_position="none") -# --- Step 6: render (the only renderer) ---+# ---------------------------------------------------------------------------+# Step 6 — RENDER (the skill's only renderer).+# --------------------------------------------------------------------------- save_plot(p, "plot.png")+print("wrote plot.png")
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (color encoding, weight 0.67) Draw the single trend line in crimson `#D1495B`, not the house blue accent.
  - [L2] (geoms, weight 0.67) Give the trend line a thicker stroke, `size=1.5`, in geom_line.

ggplot2 (R)

Grammar-of-graphics plots in R, rendered as images.

ggplot2_gtcars_hp_price

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

cold
Here’s what wasn’t good

The user corrects it, in their own words:

“Use the Accent qualitative palette for the country colors.”

This stuck starting at run 2 — honored from then on.

“Drop the dollar sign on the price axis — just comma-separated numbers, scales::comma.”

This stuck starting at run 2 — honored from then on.

“Move the legend to the bottom, not the right.”

This stuck starting at run 2 — honored from then on.

“Make the points bigger — size = 4.5.”

This stuck starting at run 2 — honored from then on.

“Add some transparency to the points — alpha = 0.6.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

warm
What actually changed, line by line (plot.R, cold → warm)
@@ -3,18 +3,16 @@ -cars <- read_csv("gtcars.csv")+gtcars <- read_csv("gtcars.csv") -plot <- ggplot(cars, aes(x = hp, y = msrp, color = ctry_origin)) +-  geom_point(size = 3, alpha = 0.85) +-  scale_y_continuous(labels = scales::label_dollar(scale = 1e-3, suffix = "K")) +-  scale_x_continuous(labels = scales::label_comma()) +-  scale_color_brewer(palette = "Set2") ++plot <- ggplot(gtcars, aes(x = hp, y = msrp, color = ctry_origin)) ++  geom_point(size = 4.5, alpha = 0.6) ++  scale_color_brewer(palette = "Accent") ++  scale_y_continuous(labels = scales::comma) +   labs(-    title = "Price Climbs with Horsepower Across Sports Cars",-    subtitle = "Manufacturer's suggested retail price vs. engine horsepower",+    title = "Price vs. Horsepower for Sports Cars",     x = "Horsepower (hp)",-    y = "MSRP (USD)",+    y = "Price (MSRP)",     color = "Country of origin"   ) +-  theme_minimal(base_size = 13) +-  theme(legend.position = "right")+  theme_minimal() ++  theme(legend.position = "bottom")
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (color encoding, weight 0.67) Color-encode country of origin with the ColorBrewer qualitative `Accent` palette via `scale_color_brewer(palette = "Accent")`.
  - [L2] (scales, weight 0.67) Format the price (msrp) axis as plain comma-separated numbers with no dollar sign, e.g. `scale_y_continuous(labels = scales::comma)`.
  - [L3] (legend placement, weight 0.67) Place the legend at the bottom with `theme(legend.position = "bottom")`.
  - [L4] (geoms, weight 0.67) Make the scatter points larger, `size = 4.5` in geom_point.
  - [L5] (geoms, weight 0.67) Make the scatter points translucent, `alpha = 0.6` in geom_point.

ggplot2_islands_top_sizes

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

cold
Here’s what wasn’t good

The user corrects it, in their own words:

“Fill the bars with #3A5A40 (forest green), not the default blue.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

warm
What actually changed, line by line (plot.R, cold → warm)
@@ -1,29 +1,21 @@+library(ggplot2) library(readr) library(dplyr)-library(ggplot2) -islands <- read_csv("islands.csv", show_col_types = FALSE)+islands <- read_csv("islands.csv") -top_islands <- islands |>-  slice_max(size, n = 15) |>+top <- islands %>%+  arrange(desc(size)) %>%+  slice_head(n = 15) %>%   mutate(name = reorder(name, size)) -plot <- ggplot(top_islands, aes(x = size, y = name)) +-  geom_col(fill = "#2C6FB3") +-  geom_text(aes(label = scales::comma(size)),-            hjust = -0.15, size = 3.2, color = "grey30") +-  scale_x_continuous(labels = scales::comma,-                     expand = expansion(mult = c(0, 0.12))) ++plot <- ggplot(top, aes(x = name, y = size)) ++  geom_col(fill = "#3A5A40") ++  coord_flip() +   labs(     title = "The 15 Largest Landmasses by Area",-    subtitle = "Continents and major islands, ranked from biggest to smallest",-    x = "Area (thousands of square miles)",-    y = NULL+    x = NULL,+    y = "Area (thousands of square miles)"   ) +-  theme_minimal(base_size = 12) +-  theme(-    plot.title = element_text(face = "bold"),-    panel.grid.major.y = element_blank(),-    panel.grid.minor.x = element_blank()-  )+  theme_minimal()
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (color encoding, weight 0.67) Fill the bars with forest green `#3A5A40`, not the house blue accent.

PowerPoint decks

The generated python-pptx build script (the deck is a binary).

pitch_deck

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

"""Build the 5-slide "Marrow" pitch deck.

Marrow is a CLI that maps a repo's tests to the code they actually cover.
One idea per slide; run this script to write deck.pptx alongside it.

Design: light background, dark ink, one restrained accent. A single sans-serif
family (Arial) with a fixed type scale, and titles top-aligned and centered.
"""

from pptx import Presentation
from pptx.dml.color import RGBColor
from pptx.enum.shapes import MSO_SHAPE
from pptx.enum.text import MSO_ANCHOR, PP_ALIGN
from pptx.util import Inches, Pt

# --- Palette -------------------------------------------------------------
BG = RGBColor(0xFA, 0xFA, 0xF8)      # warm near-white page
INK = RGBColor(0x17, 0x18, 0x1A)     # near-black body/title text
MUTED = RGBColor(0x5F, 0x63, 0x67)   # secondary text
ACCENT = RGBColor(0xB0, 0x40, 0x2F)  # marrow red, the single accent
CHIP = RGBColor(0xF0, 0xEC, 0xE6)    # tint behind code

# --- Type ----------------------------------------------------------------
FONT = "Arial"
MONO = "Consolas"
SZ_HERO = Pt(76)      # deck title
SZ_TITLE = Pt(38)     # slide titles
SZ_LEAD = Pt(23)      # lead / subtitle
SZ_BODY = Pt(18)      # body copy
SZ_STEP = Pt(15)      # step copy in the three-column slide
SZ_MICRO = Pt(11)     # slide numbers

# --- Geometry ------------------------------------------------------------
DECK_W, DECK_H = Inches(13.333), Inches(7.5)
MARGIN = Inches(1.0)
CONTENT_W = DECK_W - 2 * MARGIN
TITLE_TOP = Inches(0.85)

prs = Presentation()
prs.slide_width, prs.slide_height = DECK_W, DECK_H
BLANK = prs.slide_layouts[6]


def add_slide():
    """A blank slide painted with the deck background."""
    slide = prs.slides.add_slide(BLANK)
    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, DECK_W, DECK_H)
    bg.fill.solid()
    bg.fill.fore_color.rgb = BG
    bg.line.fill.background()
    bg.shadow.inherit = False
    return slide


def textbox(slide, left, top, width, height, anchor=MSO_ANCHOR.TOP):
    box = slide.shapes.add_textbox(left, top, width, height)
    tf = box.text_frame
    tf.word_wrap = True
    tf.vertical_anchor = anchor
    tf.margin_left = tf.margin_right = 0
    tf.margin_top = tf.margin_bottom = 0
    return tf


def write(tf, spans, size, color, align=PP_ALIGN.CENTER, font=FONT,
          bold=False, space_after=Pt(0), line=0.0, first=False):
    """Append a paragraph built from one string or a list of run specs."""
    para = tf.paragraphs[0] if first else tf.add_paragraph()
    para.alignment = align
    para.space_after = space_after
    if line:
        para.line_spacing = line
    for span in [spans] if isinstance(spans, str) else spans:
        text, over = (span, {}) if isinstance(span, str) else span
        run = para.add_run()
        run.text = text
        run.font.name = over.get("font", font)
        run.font.size = over.get("size", size)
        run.font.bold = over.get("bold", bold)
        run.font.color.rgb = over.get("color", color)
    return para


def rule(slide, top, width=Inches(1.6), color=ACCENT, height=Pt(4)):
    """Short accent rule, horizontally centered."""
    bar = slide.shapes.add_shape(
        MSO_SHAPE.RECTANGLE, (DECK_W - width) // 2, top, width, height
    )
    bar.fill.solid()
    bar.fill.fore_color.rgb = color
    bar.line.fill.background()
    bar.shadow.inherit = False
    return bar


def title_block(slide, title, number):
    """Centered, top-aligned slide title over an accent rule + slide number."""
    tf = textbox(slide, MARGIN, TITLE_TOP, CONTENT_W, Inches(1.1))
    write(tf, title, SZ_TITLE, INK, bold=True, line=1.05, first=True)
    rule(slide, TITLE_TOP + Inches(1.02))
    num = textbox(slide, DECK_W - MARGIN - Inches(1.0),
                  DECK_H - Inches(0.72), Inches(1.0), Inches(0.3))
    write(num, f"{number}/5", SZ_MICRO, MUTED, align=PP_ALIGN.RIGHT, first=True)


def statement_slide(title, lead, body, number):
    """Slide title + one bold lead line + supporting sentence."""
    slide = add_slide()
    title_block(slide, title, number)
    tf = textbox(slide, Inches(1.85), Inches(2.9), DECK_W - 2 * Inches(1.85),
                 Inches(2.4), anchor=MSO_ANCHOR.TOP)
    write(tf, lead, SZ_LEAD, ACCENT, bold=True, line=1.25,
          space_after=Pt(18), first=True)
    write(tf, body, SZ_BODY, MUTED, line=1.45)
    return slide


def code_chip(slide, text, left, top, width, size=Pt(15)):
    """Mono text on a tinted rounded plate."""
    height = Inches(0.62)
    chip = slide.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE, left, top,
                                  width, height)
    chip.fill.solid()
    chip.fill.fore_color.rgb = CHIP
    chip.line.fill.background()
    chip.shadow.inherit = False
    chip.adjustments[0] = 0.18
    tf = chip.text_frame
    tf.word_wrap = True
    tf.vertical_anchor = MSO_ANCHOR.MIDDLE
    tf.margin_left = tf.margin_right = Inches(0.18)
    tf.margin_top = tf.margin_bottom = 0
    write(tf, text, size, INK, font=MONO, first=True)
    return chip


# --- 1. Title ------------------------------------------------------------
slide = add_slide()
tf = textbox(slide, MARGIN, Inches(2.35), CONTENT_W, Inches(1.6))
write(tf, "Marrow", SZ_HERO, INK, bold=True, first=True)
rule(slide, Inches(3.98), width=Inches(2.0))
tf = textbox(slide, MARGIN, Inches(4.42), CONTENT_W, Inches(1.2))
write(tf, "Know what your tests actually cover.", SZ_LEAD, MUTED,
      space_after=Pt(14), first=True)
write(tf, "A CLI that maps a repo's tests to the code they cover.",
      Pt(15), MUTED)

# --- 2. The problem ------------------------------------------------------
statement_slide(
    "Big test suites hide their gaps.",
    "Nobody knows which tests cover which code.",
    "Large suites are slow, and the mapping from test to code lives only in "
    "people's heads. So dead tests linger for years, and real gaps in "
    "coverage go unnoticed.",
    2,
)

# --- 3. The idea ---------------------------------------------------------
statement_slide(
    "Watch one run. Draw the real map.",
    "One instrumented test run is enough.",
    "Marrow instruments a single test run and produces a test→code "
    "coverage map — no annotations, no config.",
    3,
)

# --- 4. How it works -----------------------------------------------------
slide = add_slide()
title_block(slide, "How it works", 4)
steps = [
    ("Run your suite", "Prefix the test command you already use:",
     "marrow watch -- <your test cmd>"),
    ("Marrow watches", "It records which lines each test exercised, as the "
     "suite runs.", None),
    ("Read the map", "You get an interactive test→code map, plus a list "
     "of dead tests and untested lines.", None),
]
gap = Inches(0.5)
col_w = (CONTENT_W - 2 * gap) // 3
for i, (heading, body, code) in enumerate(steps):
    left = MARGIN + i * (col_w + gap)
    bar = rule(slide, Inches(2.62), width=col_w, color=CHIP, height=Pt(3))
    bar.left = left  # rule() centers; nudge it over this column
    ntf = textbox(slide, left, Inches(2.82), col_w, Inches(0.5))
    write(ntf, f"0{i + 1}", Pt(14), ACCENT, align=PP_ALIGN.LEFT, bold=True,
          first=True)
    ttf = textbox(slide, left, Inches(3.32), col_w, Inches(2.0))
    write(ttf, heading, Pt(20), INK, align=PP_ALIGN.LEFT, bold=True,
          space_after=Pt(10), first=True)
    write(ttf, body, SZ_STEP, MUTED, align=PP_ALIGN.LEFT, line=1.4)
    if code:
        code_chip(slide, code, left, Inches(4.86), col_w, size=Pt(11))

# --- 5. Call to action ---------------------------------------------------
slide = add_slide()
title_block(slide, "Install Marrow", 5)
code_chip(slide, "pipx install marrow", (DECK_W - Inches(4.2)) // 2,
          Inches(3.15), Inches(4.2), size=Pt(20))
tf = textbox(slide, MARGIN, Inches(4.24), CONTENT_W, Inches(1.4))
write(tf, "Works with pytest, Jest, go test.", SZ_LEAD, INK, bold=True,
      space_after=Pt(14), first=True)
write(tf, "Point it at the suite you already have, and see the real map.",
      Pt(15), MUTED)

prs.save("deck.pptx")
print(f"Wrote deck.pptx — {len(prs.slides)} slides")
Here’s what wasn’t good

The user corrects it, in their own words:

“Use Consolas for all the slide text, not the default sans-serif.”

This stuck starting at run 2 — honored from then on.

“Make the deck square (1:1), not the usual 4:3/16:9 — set prs.slide_width = Inches(10) and prs.slide_height = Inches(10).”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

"""Build the 5-slide "Marrow" pitch deck (square 1:1, Consolas throughout)."""

from pptx import Presentation
from pptx.dml.color import RGBColor
from pptx.enum.shapes import MSO_SHAPE
from pptx.enum.text import MSO_ANCHOR, PP_ALIGN
from pptx.util import Inches, Pt

# --- design tokens -----------------------------------------------------------

FONT = "Consolas"

BG = RGBColor(0xF6, 0xF5, 0xF2)        # warm off-white page
INK = RGBColor(0x1B, 0x1B, 0x1E)       # near-black text
MUTED = RGBColor(0x6B, 0x6B, 0x73)     # secondary text
ACCENT = RGBColor(0xC2, 0x41, 0x0C)    # burnt orange
PANEL = RGBColor(0x1B, 0x1B, 0x1E)     # dark code panel
PANEL_TEXT = RGBColor(0xF6, 0xF5, 0xF2)

SIDE = Inches(0.95)                    # left/right margin
CONTENT_W = Inches(10) - 2 * SIDE

TITLE_PT = 30   # Consolas is wide; keeps the longest title on one line
LEAD_PT = 20
BODY_PT = 16
CODE_PT = 15
SMALL_PT = 10.5

prs = Presentation()
prs.slide_width = Inches(10)
prs.slide_height = Inches(10)
BLANK = prs.slide_layouts[6]


# --- helpers -----------------------------------------------------------------

def add_slide():
    """Blank slide painted with the deck background."""
    slide = prs.slides.add_slide(BLANK)
    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, prs.slide_width, prs.slide_height)
    bg.fill.solid()
    bg.fill.fore_color.rgb = BG
    bg.line.fill.background()
    bg.shadow.inherit = False
    return slide


def textbox(slide, left, top, width, height):
    box = slide.shapes.add_textbox(left, top, width, height)
    tf = box.text_frame
    tf.word_wrap = True
    tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
    return tf


def write(tf, lines, size, color=INK, bold=False, align=PP_ALIGN.LEFT,
          space_after=0, line_spacing=1.25, first=None):
    """Fill a text frame; every run is set to Consolas."""
    for i, text in enumerate(lines):
        p = tf.paragraphs[0] if i == 0 else tf.add_paragraph()
        p.alignment = align
        p.line_spacing = line_spacing
        p.space_after = Pt(space_after)
        run = p.add_run()
        run.text = text
        run.font.name = FONT
        run.font.size = Pt(size)
        run.font.bold = bold
        run.font.color.rgb = color
        if i == 0 and first:
            run.font.size = Pt(first.get("size", size))
            run.font.bold = first.get("bold", bold)
            run.font.color.rgb = first.get("color", color)
    return tf


def rule(slide, top, width=Inches(1.5), color=ACCENT, thickness=Pt(3.5), left=SIDE):
    line = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, left, top, width, thickness)
    line.fill.solid()
    line.fill.fore_color.rgb = color
    line.line.fill.background()
    line.shadow.inherit = False
    return line


def panel(slide, top, height, color=PANEL, left=SIDE, width=CONTENT_W):
    box = slide.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE, left, top, width, height)
    box.fill.solid()
    box.fill.fore_color.rgb = color
    box.line.fill.background()
    box.shadow.inherit = False
    box.adjustments[0] = 0.08
    return box


def slide_title(slide, text):
    """Section title: top-aligned, centered, with an accent rule beneath."""
    tf = textbox(slide, SIDE, Inches(1.0), CONTENT_W, Inches(1.4))
    write(tf, [text], TITLE_PT, INK, bold=True, align=PP_ALIGN.CENTER, line_spacing=1.1)
    rule(slide, Inches(2.05), width=Inches(1.6),
         left=int((prs.slide_width - Inches(1.6)) / 2))


def footer(slide, label, number):
    tf = textbox(slide, SIDE, Inches(9.15), CONTENT_W, Inches(0.4))
    p = tf.paragraphs[0]
    p.alignment = PP_ALIGN.LEFT
    for text, color in ((label, MUTED), ("   ·   ", MUTED), (number, ACCENT)):
        run = p.add_run()
        run.text = text
        run.font.name = FONT
        run.font.size = Pt(SMALL_PT)
        run.font.color.rgb = color


# --- 1. title ----------------------------------------------------------------

s = add_slide()
tf = textbox(s, SIDE, Inches(3.5), CONTENT_W, Inches(1.6))
write(tf, ["Marrow"], 76, INK, bold=True, align=PP_ALIGN.CENTER, line_spacing=1.0)

rule(s, Inches(4.95), width=Inches(2.2), left=int((prs.slide_width - Inches(2.2)) / 2))

tf = textbox(s, SIDE, Inches(5.5), CONTENT_W, Inches(0.8))
write(tf, ["Know what your tests actually cover."], LEAD_PT, MUTED,
      align=PP_ALIGN.CENTER)

tf = textbox(s, SIDE, Inches(6.4), CONTENT_W, Inches(0.5))
write(tf, ["$ marrow watch -- pytest"], CODE_PT, ACCENT, align=PP_ALIGN.CENTER)

tf = textbox(s, SIDE, Inches(9.15), CONTENT_W, Inches(0.4))
write(tf, ["a CLI that maps tests to the code they cover"], SMALL_PT, MUTED,
      align=PP_ALIGN.CENTER)


# --- 2. the problem ----------------------------------------------------------

s = add_slide()
slide_title(s, "Big test suites hide their gaps.")
footer(s, "the problem", "02")

tf = textbox(s, SIDE, Inches(3.1), CONTENT_W, Inches(3.0))
write(tf, [
    "Large suites are slow, and nobody knows",
    "which tests cover which code.",
], LEAD_PT, INK, line_spacing=1.35, space_after=20)

items = [
    ("Dead tests linger.", "They pass forever and guard nothing."),
    ("Real gaps go unnoticed.", "A green run says little about coverage."),
    ("Slow feedback.", "Everything runs because nothing is mapped."),
]
top = Inches(4.6)
for head, sub in items:
    rule(s, top + Inches(0.12), width=Inches(0.22), thickness=Pt(9), left=SIDE)
    tf = textbox(s, SIDE + Inches(0.55), top, CONTENT_W - Inches(0.55), Inches(1.0))
    write(tf, [head], BODY_PT, INK, bold=True, line_spacing=1.2)
    tf2 = textbox(s, SIDE + Inches(0.55), top + Inches(0.34),
                  CONTENT_W - Inches(0.55), Inches(0.6))
    write(tf2, [sub], BODY_PT, MUTED, line_spacing=1.2)
    top += Inches(1.15)


# --- 3. the idea -------------------------------------------------------------

s = add_slide()
slide_title(s, "Watch one run. Draw the real map.")
footer(s, "the idea", "03")

tf = textbox(s, SIDE, Inches(3.2), CONTENT_W, Inches(2.2))
write(tf, [
    "Marrow instruments a single test run and",
    "produces a test → code coverage map.",
], LEAD_PT, INK, line_spacing=1.35)

p = panel(s, Inches(5.2), Inches(2.1))
tf = p.text_frame
tf.word_wrap = True
tf.vertical_anchor = MSO_ANCHOR.MIDDLE
tf.margin_left = tf.margin_right = Inches(0.5)
write(tf, [
    "no annotations",
    "no config",
    "one run, the whole map",
], LEAD_PT, PANEL_TEXT, align=PP_ALIGN.CENTER, line_spacing=1.45)
tf.paragraphs[1].runs[0].font.color.rgb = PANEL_TEXT
tf.paragraphs[2].runs[0].font.color.rgb = ACCENT
tf.paragraphs[2].runs[0].font.bold = True

tf = textbox(s, SIDE, Inches(7.8), CONTENT_W, Inches(0.6))
write(tf, ["Point it at the test command you already use."], BODY_PT, MUTED,
      align=PP_ALIGN.CENTER)


# --- 4. how it works ---------------------------------------------------------

s = add_slide()
slide_title(s, "How it works")
footer(s, "how it works", "04")

steps = [
    ("01", "Run it", "marrow watch -- <your test cmd>", True),
    ("02", "It records", "which lines each test exercised", False),
    ("03", "It emits", "an interactive map + dead tests + untested lines", False),
]

top = Inches(3.2)
for num, head, detail, is_code in steps:
    tf = textbox(s, SIDE, top, Inches(1.0), Inches(0.8))
    write(tf, [num], 30, ACCENT, bold=True, line_spacing=1.0)

    tf = textbox(s, SIDE + Inches(1.1), top + Inches(0.05),
                 CONTENT_W - Inches(1.1), Inches(0.5))
    write(tf, [head], LEAD_PT, INK, bold=True, line_spacing=1.15)

    if is_code:
        box = panel(s, top + Inches(0.6), Inches(0.62),
                    left=SIDE + Inches(1.1), width=CONTENT_W - Inches(1.1))
        tf = box.text_frame
        tf.word_wrap = True
        tf.vertical_anchor = MSO_ANCHOR.MIDDLE
        tf.margin_left = Inches(0.3)
        p = tf.paragraphs[0]
        for text, color in (("$ ", ACCENT), (detail, PANEL_TEXT)):
            run = p.add_run()
            run.text = text
            run.font.name = FONT
            run.font.size = Pt(CODE_PT)
            run.font.color.rgb = color
    else:
        tf = textbox(s, SIDE + Inches(1.1), top + Inches(0.62),
                     CONTENT_W - Inches(1.1), Inches(0.8))
        write(tf, [detail], BODY_PT, MUTED, line_spacing=1.3)

    top += Inches(1.8)

rule(s, Inches(8.6), width=CONTENT_W, thickness=Pt(1.5), color=RGBColor(0xDD, 0xDA, 0xD3))


# --- 5. call to action -------------------------------------------------------

s = add_slide()
slide_title(s, "Install Marrow")
footer(s, "get started", "05")

box = panel(s, Inches(4.0), Inches(1.5))
tf = box.text_frame
tf.word_wrap = True
tf.vertical_anchor = MSO_ANCHOR.MIDDLE
p = tf.paragraphs[0]
p.alignment = PP_ALIGN.CENTER
for text, color in (("$ ", ACCENT), ("pipx install marrow", PANEL_TEXT)):
    run = p.add_run()
    run.text = text
    run.font.name = FONT
    run.font.size = Pt(27)
    run.font.bold = True
    run.font.color.rgb = color

tf = textbox(s, SIDE, Inches(6.1), CONTENT_W, Inches(0.7))
write(tf, ["Works with pytest, Jest, go test."], LEAD_PT, INK,
      align=PP_ALIGN.CENTER)

rule(s, Inches(7.1), width=Inches(1.6), left=int((prs.slide_width - Inches(1.6)) / 2))

tf = textbox(s, SIDE, Inches(7.6), CONTENT_W, Inches(0.6))
write(tf, ["Know what your tests actually cover."], BODY_PT, MUTED,
      align=PP_ALIGN.CENTER)

prs.save("deck.pptx")
print(f"wrote deck.pptx — {len(prs.slides.__iter__.__self__._sldIdLst)} slides, "
      f"{prs.slide_width.inches:g}x{prs.slide_height.inches:g} in")
What actually changed, line by line (deck.py, cold → warm)
@@ -1,9 +1,2 @@-"""Build the 5-slide "Marrow" pitch deck.--Marrow is a CLI that maps a repo's tests to the code they actually cover.-One idea per slide; run this script to write deck.pptx alongside it.--Design: light background, dark ink, one restrained accent. A single sans-serif-family (Arial) with a fixed type scale, and titles top-aligned and centered.-"""+"""Build the 5-slide "Marrow" pitch deck (square 1:1, Consolas throughout).""" @@ -15,27 +8,25 @@ -# --- Palette --------------------------------------------------------------BG = RGBColor(0xFA, 0xFA, 0xF8)      # warm near-white page-INK = RGBColor(0x17, 0x18, 0x1A)     # near-black body/title text-MUTED = RGBColor(0x5F, 0x63, 0x67)   # secondary text-ACCENT = RGBColor(0xB0, 0x40, 0x2F)  # marrow red, the single accent-CHIP = RGBColor(0xF0, 0xEC, 0xE6)    # tint behind code--# --- Type -----------------------------------------------------------------FONT = "Arial"-MONO = "Consolas"-SZ_HERO = Pt(76)      # deck title-SZ_TITLE = Pt(38)     # slide titles-SZ_LEAD = Pt(23)      # lead / subtitle-SZ_BODY = Pt(18)      # body copy-SZ_STEP = Pt(15)      # step copy in the three-column slide-SZ_MICRO = Pt(11)     # slide numbers--# --- Geometry -------------------------------------------------------------DECK_W, DECK_H = Inches(13.333), Inches(7.5)-MARGIN = Inches(1.0)-CONTENT_W = DECK_W - 2 * MARGIN-TITLE_TOP = Inches(0.85)+# --- design tokens -----------------------------------------------------------++FONT = "Consolas"++BG = RGBColor(0xF6, 0xF5, 0xF2)        # warm off-white page+INK = RGBColor(0x1B, 0x1B, 0x1E)       # near-black text+MUTED = RGBColor(0x6B, 0x6B, 0x73)     # secondary text+ACCENT = RGBColor(0xC2, 0x41, 0x0C)    # burnt orange+PANEL = RGBColor(0x1B, 0x1B, 0x1E)     # dark code panel+PANEL_TEXT = RGBColor(0xF6, 0xF5, 0xF2)++SIDE = Inches(0.95)                    # left/right margin+CONTENT_W = Inches(10) - 2 * SIDE++TITLE_PT = 30   # Consolas is wide; keeps the longest title on one line+LEAD_PT = 20+BODY_PT = 16+CODE_PT = 15+SMALL_PT = 10.5  prs = Presentation()-prs.slide_width, prs.slide_height = DECK_W, DECK_H+prs.slide_width = Inches(10)+prs.slide_height = Inches(10) BLANK = prs.slide_layouts[6]@@ -43,6 +34,8 @@ +# --- helpers -----------------------------------------------------------------+ def add_slide():-    """A blank slide painted with the deck background."""+    """Blank slide painted with the deck background."""     slide = prs.slides.add_slide(BLANK)-    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, DECK_W, DECK_H)+    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, prs.slide_width, prs.slide_height)     bg.fill.solid()@@ -54,3 +47,3 @@ -def textbox(slide, left, top, width, height, anchor=MSO_ANCHOR.TOP):+def textbox(slide, left, top, width, height):     box = slide.shapes.add_textbox(left, top, width, height)@@ -58,5 +51,3 @@     tf.word_wrap = True-    tf.vertical_anchor = anchor-    tf.margin_left = tf.margin_right = 0-    tf.margin_top = tf.margin_bottom = 0+    tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0     return tf@@ -64,143 +55,218 @@ -def write(tf, spans, size, color, align=PP_ALIGN.CENTER, font=FONT,-          bold=False, space_after=Pt(0), line=0.0, first=False):-    """Append a paragraph built from one string or a list of run specs."""-    para = tf.paragraphs[0] if first else tf.add_paragraph()-    para.alignment = align-    para.space_after = space_after-    if line:-        para.line_spacing = line-    for span in [spans] if isinstance(spans, str) else spans:-        text, over = (span, {}) if isinstance(span, str) else span-        run = para.add_run()+def write(tf, lines, size, color=INK, bold=False, align=PP_ALIGN.LEFT,+          space_after=0, line_spacing=1.25, first=None):+    """Fill a text frame; every run is set to Consolas."""+    for i, text in enumerate(lines):+        p = tf.paragraphs[0] if i == 0 else tf.add_paragraph()+        p.alignment = align+        p.line_spacing = line_spacing+        p.space_after = Pt(space_after)+        run = p.add_run()         run.text = text-        run.font.name = over.get("font", font)-        run.font.size = over.get("size", size)-        run.font.bold = over.get("bold", bold)-        run.font.color.rgb = over.get("color", color)-    return para---def rule(slide, top, width=Inches(1.6), color=ACCENT, height=Pt(4)):-    """Short accent rule, horizontally centered."""-    bar = slide.shapes.add_shape(-        MSO_SHAPE.RECTANGLE, (DECK_W - width) // 2, top, width, height-    )-    bar.fill.solid()-    bar.fill.fore_color.rgb = color-    bar.line.fill.background()-    bar.shadow.inherit = False-    return bar---def title_block(slide, title, number):-    """Centered, top-aligned slide title over an accent rule + slide number."""-    tf = textbox(slide, MARGIN, TITLE_TOP, CONTENT_W, Inches(1.1))-    write(tf, title, SZ_TITLE, INK, bold=True, line=1.05, first=True)-    rule(slide, TITLE_TOP + Inches(1.02))-    num = textbox(slide, DECK_W - MARGIN - Inches(1.0),-                  DECK_H - Inches(0.72), Inches(1.0), Inches(0.3))-    write(num, f"{number}/5", SZ_MICRO, MUTED, align=PP_ALIGN.RIGHT, first=True)---def statement_slide(title, lead, body, number):-    """Slide title + one bold lead line + supporting sentence."""-    slide = add_slide()-    title_block(slide, title, number)-    tf = textbox(slide, Inches(1.85), Inches(2.9), DECK_W - 2 * Inches(1.85),-                 Inches(2.4), anchor=MSO_ANCHOR.TOP)-    write(tf, lead, SZ_LEAD, ACCENT, bold=True, line=1.25,-          space_after=Pt(18), first=True)-    write(tf, body, SZ_BODY, MUTED, line=1.45)-    return slide---def code_chip(slide, text, left, top, width, size=Pt(15)):-    """Mono text on a tinted rounded plate."""-    height = Inches(0.62)-    chip = slide.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE, left, top,-                                  width, height)-    chip.fill.solid()-    chip.fill.fore_color.rgb = CHIP-    chip.line.fill.background()-    chip.shadow.inherit = False-    chip.adjustments[0] = 0.18-    tf = chip.text_frame-    tf.word_wrap = True-    tf.vertical_anchor = MSO_ANCHOR.MIDDLE-    tf.margin_left = tf.margin_right = Inches(0.18)-    tf.margin_top = tf.margin_bottom = 0-    write(tf, text, size, INK, font=MONO, first=True)-    return chip---# --- 1. Title -------------------------------------------------------------slide = add_slide()-tf = textbox(slide, MARGIN, Inches(2.35), CONTENT_W, Inches(1.6))-write(tf, "Marrow", SZ_HERO, INK, bold=True, first=True)-rule(slide, Inches(3.98), width=Inches(2.0))-tf = textbox(slide, MARGIN, Inches(4.42), CONTENT_W, Inches(1.2))-write(tf, "Know what your tests actually cover.", SZ_LEAD, MUTED,-      space_after=Pt(14), first=True)-write(tf, "A CLI that maps a repo's tests to the code they cover.",-      Pt(15), MUTED)--# --- 2. The problem -------------------------------------------------------statement_slide(-    "Big test suites hide their gaps.",-    "Nobody knows which tests cover which code.",-    "Large suites are slow, and the mapping from test to code lives only in "-    "people's heads. So dead tests linger for years, and real gaps in "-    "coverage go unnoticed.",-    2,-)--# --- 3. The idea ----------------------------------------------------------statement_slide(-    "Watch one run. Draw the real map.",-    "One instrumented test run is enough.",-    "Marrow instruments a single test run and produces a test→code "-    "coverage map — no annotations, no config.",-    3,-)--# --- 4. How it works ------------------------------------------------------slide = add_slide()-title_block(slide, "How it works", 4)+        run.font.name = FONT+        run.font.size = Pt(size)+        run.font.bold = bold+        run.font.color.rgb = color+        if i == 0 and first:+            run.font.size = Pt(first.get("size", size))+            run.font.bold = first.get("bold", bold)+            run.font.color.rgb = first.get("color", color)+    return tf+++def rule(slide, top, width=Inches(1.5), color=ACCENT, thickness=Pt(3.5), left=SIDE):+    line = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, left, top, width, thickness)+    line.fill.solid()+    line.fill.fore_color.rgb = color+    line.line.fill.background()+    line.shadow.inherit = False+    return line+++def panel(slide, top, height, color=PANEL, left=SIDE, width=CONTENT_W):+    box = slide.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE, left, top, width, height)+    box.fill.solid()+    box.fill.fore_color.rgb = color+    box.line.fill.background()+    box.shadow.inherit = False+    box.adjustments[0] = 0.08+    return box+++def slide_title(slide, text):+    """Section title: top-aligned, centered, with an accent rule beneath."""+    tf = textbox(slide, SIDE, Inches(1.0), CONTENT_W, Inches(1.4))+    write(tf, [text], TITLE_PT, INK, bold=True, align=PP_ALIGN.CENTER, line_spacing=1.1)+    rule(slide, Inches(2.05), width=Inches(1.6),+         left=int((prs.slide_width - Inches(1.6)) / 2))+++def footer(slide, label, number):+    tf = textbox(slide, SIDE, Inches(9.15), CONTENT_W, Inches(0.4))+    p = tf.paragraphs[0]+    p.alignment = PP_ALIGN.LEFT+    for text, color in ((label, MUTED), ("   ·   ", MUTED), (number, ACCENT)):+        run = p.add_run()+        run.text = text+        run.font.name = FONT+        run.font.size = Pt(SMALL_PT)+        run.font.color.rgb = color+++# --- 1. title ----------------------------------------------------------------++s = add_slide()+tf = textbox(s, SIDE, Inches(3.5), CONTENT_W, Inches(1.6))+write(tf, ["Marrow"], 76, INK, bold=True, align=PP_ALIGN.CENTER, line_spacing=1.0)++rule(s, Inches(4.95), width=Inches(2.2), left=int((prs.slide_width - Inches(2.2)) / 2))++tf = textbox(s, SIDE, Inches(5.5), CONTENT_W, Inches(0.8))+write(tf, ["Know what your tests actually cover."], LEAD_PT, MUTED,+      align=PP_ALIGN.CENTER)++tf = textbox(s, SIDE, Inches(6.4), CONTENT_W, Inches(0.5))+write(tf, ["$ marrow watch -- pytest"], CODE_PT, ACCENT, align=PP_ALIGN.CENTER)++tf = textbox(s, SIDE, Inches(9.15), CONTENT_W, Inches(0.4))+write(tf, ["a CLI that maps tests to the code they cover"], SMALL_PT, MUTED,+      align=PP_ALIGN.CENTER)+++# --- 2. the problem ----------------------------------------------------------++s = add_slide()+slide_title(s, "Big test suites hide their gaps.")+footer(s, "the problem", "02")++tf = textbox(s, SIDE, Inches(3.1), CONTENT_W, Inches(3.0))+write(tf, [+    "Large suites are slow, and nobody knows",+    "which tests cover which code.",+], LEAD_PT, INK, line_spacing=1.35, space_after=20)++items = [+    ("Dead tests linger.", "They pass forever and guard nothing."),+    ("Real gaps go unnoticed.", "A green run says little about coverage."),+    ("Slow feedback.", "Everything runs because nothing is mapped."),+]+top = Inches(4.6)+for head, sub in items:+    rule(s, top + Inches(0.12), width=Inches(0.22), thickness=Pt(9), left=SIDE)+    tf = textbox(s, SIDE + Inches(0.55), top, CONTENT_W - Inches(0.55), Inches(1.0))+    write(tf, [head], BODY_PT, INK, bold=True, line_spacing=1.2)+    tf2 = textbox(s, SIDE + Inches(0.55), top + Inches(0.34),+                  CONTENT_W - Inches(0.55), Inches(0.6))+    write(tf2, [sub], BODY_PT, MUTED, line_spacing=1.2)+    top += Inches(1.15)+++# --- 3. the idea -------------------------------------------------------------++s = add_slide()+slide_title(s, "Watch one run. Draw the real map.")+footer(s, "the idea", "03")++tf = textbox(s, SIDE, Inches(3.2), CONTENT_W, Inches(2.2))+write(tf, [+    "Marrow instruments a single test run and",+    "produces a test → code coverage map.",+], LEAD_PT, INK, line_spacing=1.35)++p = panel(s, Inches(5.2), Inches(2.1))+tf = p.text_frame+tf.word_wrap = True+tf.vertical_anchor = MSO_ANCHOR.MIDDLE+tf.margin_left = tf.margin_right = Inches(0.5)+write(tf, [+    "no annotations",+    "no config",+    "one run, the whole map",+], LEAD_PT, PANEL_TEXT, align=PP_ALIGN.CENTER, line_spacing=1.45)+tf.paragraphs[1].runs[0].font.color.rgb = PANEL_TEXT+tf.paragraphs[2].runs[0].font.color.rgb = ACCENT+tf.paragraphs[2].runs[0].font.bold = True++tf = textbox(s, SIDE, Inches(7.8), CONTENT_W, Inches(0.6))+write(tf, ["Point it at the test command you already use."], BODY_PT, MUTED,+      align=PP_ALIGN.CENTER)+++# --- 4. how it works ---------------------------------------------------------++s = add_slide()+slide_title(s, "How it works")+footer(s, "how it works", "04")+ steps = [-    ("Run your suite", "Prefix the test command you already use:",-     "marrow watch -- <your test cmd>"),-    ("Marrow watches", "It records which lines each test exercised, as the "-     "suite runs.", None),-    ("Read the map", "You get an interactive test→code map, plus a list "-     "of dead tests and untested lines.", None),+    ("01", "Run it", "marrow watch -- <your test cmd>", True),+    ("02", "It records", "which lines each test exercised", False),+    ("03", "It emits", "an interactive map + dead tests + untested lines", False), ]-gap = Inches(0.5)-col_w = (CONTENT_W - 2 * gap) // 3-for i, (heading, body, code) in enumerate(steps):-    left = MARGIN + i * (col_w + gap)-    bar = rule(slide, Inches(2.62), width=col_w, color=CHIP, height=Pt(3))-    bar.left = left  # rule() centers; nudge it over this column-    ntf = textbox(slide, left, Inches(2.82), col_w, Inches(0.5))-    write(ntf, f"0{i + 1}", Pt(14), ACCENT, align=PP_ALIGN.LEFT, bold=True,-          first=True)-    ttf = textbox(slide, left, Inches(3.32), col_w, Inches(2.0))-    write(ttf, heading, Pt(20), INK, align=PP_ALIGN.LEFT, bold=True,-          space_after=Pt(10), first=True)-    write(ttf, body, SZ_STEP, MUTED, align=PP_ALIGN.LEFT, line=1.4)-    if code:-        code_chip(slide, code, left, Inches(4.86), col_w, size=Pt(11))--# --- 5. Call to action ----------------------------------------------------slide = add_slide()-title_block(slide, "Install Marrow", 5)-code_chip(slide, "pipx install marrow", (DECK_W - Inches(4.2)) // 2,-          Inches(3.15), Inches(4.2), size=Pt(20))-tf = textbox(slide, MARGIN, Inches(4.24), CONTENT_W, Inches(1.4))-write(tf, "Works with pytest, Jest, go test.", SZ_LEAD, INK, bold=True,-      space_after=Pt(14), first=True)-write(tf, "Point it at the suite you already have, and see the real map.",-      Pt(15), MUTED)++top = Inches(3.2)+for num, head, detail, is_code in steps:+    tf = textbox(s, SIDE, top, Inches(1.0), Inches(0.8))+    write(tf, [num], 30, ACCENT, bold=True, line_spacing=1.0)++    tf = textbox(s, SIDE + Inches(1.1), top + Inches(0.05),+                 CONTENT_W - Inches(1.1), Inches(0.5))+    write(tf, [head], LEAD_PT, INK, bold=True, line_spacing=1.15)++    if is_code:+        box = panel(s, top + Inches(0.6), Inches(0.62),+                    left=SIDE + Inches(1.1), width=CONTENT_W - Inches(1.1))+        tf = box.text_frame+        tf.word_wrap = True+        tf.vertical_anchor = MSO_ANCHOR.MIDDLE+        tf.margin_left = Inches(0.3)+        p = tf.paragraphs[0]+        for text, color in (("$ ", ACCENT), (detail, PANEL_TEXT)):+            run = p.add_run()+            run.text = text+            run.font.name = FONT+            run.font.size = Pt(CODE_PT)+            run.font.color.rgb = color+    else:+        tf = textbox(s, SIDE + Inches(1.1), top + Inches(0.62),+                     CONTENT_W - Inches(1.1), Inches(0.8))+        write(tf, [detail], BODY_PT, MUTED, line_spacing=1.3)++    top += Inches(1.8)++rule(s, Inches(8.6), width=CONTENT_W, thickness=Pt(1.5), color=RGBColor(0xDD, 0xDA, 0xD3))+++# --- 5. call to action -------------------------------------------------------++s = add_slide()+slide_title(s, "Install Marrow")+footer(s, "get started", "05")++box = panel(s, Inches(4.0), Inches(1.5))+tf = box.text_frame+tf.word_wrap = True+tf.vertical_anchor = MSO_ANCHOR.MIDDLE+p = tf.paragraphs[0]+p.alignment = PP_ALIGN.CENTER+for text, color in (("$ ", ACCENT), ("pipx install marrow", PANEL_TEXT)):+    run = p.add_run()+    run.text = text+    run.font.name = FONT+    run.font.size = Pt(27)+    run.font.bold = True+    run.font.color.rgb = color++tf = textbox(s, SIDE, Inches(6.1), CONTENT_W, Inches(0.7))+write(tf, ["Works with pytest, Jest, go test."], LEAD_PT, INK,+      align=PP_ALIGN.CENTER)++rule(s, Inches(7.1), width=Inches(1.6), left=int((prs.slide_width - Inches(1.6)) / 2))++tf = textbox(s, SIDE, Inches(7.6), CONTENT_W, Inches(0.6))+write(tf, ["Know what your tests actually cover."], BODY_PT, MUTED,+      align=PP_ALIGN.CENTER)  prs.save("deck.pptx")-print(f"Wrote deck.pptx — {len(prs.slides)} slides")+print(f"wrote deck.pptx — {len(prs.slides.__iter__.__self__._sldIdLst)} slides, "+      f"{prs.slide_width.inches:g}x{prs.slide_height.inches:g} in")
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L2] (aspect ratio, weight 0.67) Use a SQUARE 1:1 slide format: set `prs.slide_width = Inches(10)` AND `prs.slide_height = Inches(10)` on the Presentation (import Inches from `pptx.util`).
  - [L1] (typography, weight 0.67) Set every text run's font to Consolas (`run.font.name = "Consolas"`) — titles and body, on all slides.

pitch_deck_reorder

Here’s what it produced

The skill runs against an empty Whetstone store — nothing has been taught yet.

"""Build the 5-slide "Marrow" pitch deck (deck.pptx) with python-pptx.

Design system
-------------
Slides   : 16:9, 13.333in x 7.5in
Palette  : warm off-white background, near-black ink, one rust accent
Type     : Arial throughout; titles 40pt (72pt on the title slide), body 20pt
Layout   : blank layouts + explicit text boxes; titles top-aligned and centered,
           with a short accent rule underneath.
"""

from pptx import Presentation
from pptx.dml.color import RGBColor
from pptx.enum.shapes import MSO_SHAPE
from pptx.enum.text import MSO_ANCHOR, PP_ALIGN
from pptx.util import Inches, Pt

# --- palette ---------------------------------------------------------------
BG = RGBColor(0xFB, 0xFA, 0xF8)
INK = RGBColor(0x17, 0x18, 0x1A)
MUTED = RGBColor(0x5F, 0x63, 0x68)
ACCENT = RGBColor(0xB4, 0x53, 0x1F)
CHIP_BG = RGBColor(0xF1, 0xED, 0xE7)

# --- type ------------------------------------------------------------------
FONT = "Arial"
MONO = "Consolas"
SZ_WORDMARK = Pt(72)
SZ_TITLE = Pt(40)
SZ_LEAD = Pt(24)
SZ_BODY = Pt(20)
SZ_SMALL = Pt(16)

# --- geometry --------------------------------------------------------------
DECK_W = Inches(13.333)
DECK_H = Inches(7.5)
MARGIN = Inches(0.9)
CONTENT_W = DECK_W - 2 * MARGIN

prs = Presentation()
prs.slide_width = DECK_W
prs.slide_height = DECK_H
BLANK = prs.slide_layouts[6]


def new_slide():
    """A blank slide painted with the deck background color."""
    slide = prs.slides.add_slide(BLANK)
    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, DECK_W, DECK_H)
    bg.fill.solid()
    bg.fill.fore_color.rgb = BG
    bg.line.fill.background()
    bg.shadow.inherit = False
    return slide


def textbox(slide, left, top, width, height, anchor=MSO_ANCHOR.TOP):
    box = slide.shapes.add_textbox(left, top, width, height)
    tf = box.text_frame
    tf.word_wrap = True
    tf.vertical_anchor = anchor
    tf.margin_left = tf.margin_right = 0
    tf.margin_top = tf.margin_bottom = 0
    return tf


def write(tf, text, size, color=INK, bold=False, align=PP_ALIGN.CENTER,
          font=FONT, space_after=0, line_spacing=1.15, first=False):
    """Add (or fill, if `first`) a paragraph with a single styled run."""
    p = tf.paragraphs[0] if first else tf.add_paragraph()
    p.alignment = align
    p.space_after = Pt(space_after)
    p.line_spacing = line_spacing
    run = p.add_run()
    run.text = text
    run.font.name = font
    run.font.size = size
    run.font.bold = bold
    run.font.color.rgb = color
    return p


def accent_rule(slide, top, width=Inches(1.2), left=None):
    """The short rust rule that sits under every title."""
    left = (DECK_W - width) // 2 if left is None else left
    bar = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, left, top, width, Pt(4))
    bar.fill.solid()
    bar.fill.fore_color.rgb = ACCENT
    bar.line.fill.background()
    bar.shadow.inherit = False
    return bar


def title_block(slide, title):
    """Centered, top-aligned slide title + accent rule. Returns body top."""
    tf = textbox(slide, MARGIN, Inches(0.95), CONTENT_W, Inches(1.1))
    write(tf, title, SZ_TITLE, INK, bold=True, first=True)
    accent_rule(slide, Inches(2.15))
    return Inches(2.85)


# --- 1. Title --------------------------------------------------------------
slide = new_slide()
tf = textbox(slide, MARGIN, Inches(2.45), CONTENT_W, Inches(1.4))
write(tf, "Marrow", SZ_WORDMARK, INK, bold=True, first=True)
accent_rule(slide, Inches(4.05), width=Inches(1.6))
tf = textbox(slide, MARGIN, Inches(4.55), CONTENT_W, Inches(0.7))
write(tf, "Know what your tests actually cover.", SZ_LEAD, MUTED, first=True)

# --- 2. The problem --------------------------------------------------------
slide = new_slide()
top = title_block(slide, "Big test suites hide their gaps.")
tf = textbox(slide, Inches(2.15), top, DECK_W - 2 * Inches(2.15), Inches(2.6))
write(tf, "Large suites are slow, and nobody knows which tests cover "
          "which code.", SZ_BODY, INK, space_after=18, line_spacing=1.35,
      first=True)
write(tf, "So dead tests linger for years — and the real gaps go unnoticed.",
      SZ_BODY, MUTED, line_spacing=1.35)

# --- 3. The idea -----------------------------------------------------------
slide = new_slide()
top = title_block(slide, "Watch one run. Draw the real map.")
tf = textbox(slide, Inches(2.15), top, DECK_W - 2 * Inches(2.15), Inches(2.6))
write(tf, "Marrow instruments a single test run and produces a "
          "test → code coverage map.", SZ_BODY, INK, space_after=18,
      line_spacing=1.35, first=True)
write(tf, "No annotations. No config.", SZ_LEAD, ACCENT, bold=True)

# --- 4. How it works -------------------------------------------------------
slide = new_slide()
top = title_block(slide, "How it works")

STEPS = [
    ("01", "Run it", "marrow watch -- <your test cmd>", None),
    ("02", "It records", None,
     "Marrow records which lines each test exercised."),
    ("03", "It maps", None,
     "You get an interactive map, plus a list of dead tests and "
     "untested lines."),
]
gap = Inches(0.5)
col_w = (CONTENT_W - 2 * gap) // 3

for i, (num, label, code, body) in enumerate(STEPS):
    left = MARGIN + i * (col_w + gap)
    tf = textbox(slide, left, top, col_w, Inches(0.6))
    write(tf, num, Pt(30), ACCENT, bold=True, align=PP_ALIGN.LEFT, first=True)
    tf = textbox(slide, left, top + Inches(0.62), col_w, Inches(0.45))
    write(tf, label, SZ_BODY, INK, bold=True, align=PP_ALIGN.LEFT, first=True)

    body_top = top + Inches(1.2)
    if code:
        chip = slide.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE, left,
                                      body_top, col_w, Inches(1.1))
        chip.fill.solid()
        chip.fill.fore_color.rgb = CHIP_BG
        chip.line.fill.background()
        chip.shadow.inherit = False
        ctf = chip.text_frame
        ctf.word_wrap = True
        ctf.vertical_anchor = MSO_ANCHOR.MIDDLE
        ctf.margin_left = ctf.margin_right = Inches(0.18)
        write(ctf, code, SZ_SMALL, ACCENT, align=PP_ALIGN.LEFT, font=MONO,
              first=True)
    else:
        tf = textbox(slide, left, body_top, col_w, Inches(1.8))
        write(tf, body, SZ_SMALL, MUTED, align=PP_ALIGN.LEFT,
              line_spacing=1.35, first=True)

# --- 5. Call to action -----------------------------------------------------
slide = new_slide()
top = title_block(slide, "Install Marrow")

chip_w = Inches(5.2)
chip = slide.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE,
                              (DECK_W - chip_w) // 2, top, chip_w, Inches(1.0))
chip.fill.solid()
chip.fill.fore_color.rgb = CHIP_BG
chip.line.fill.background()
chip.shadow.inherit = False
ctf = chip.text_frame
ctf.vertical_anchor = MSO_ANCHOR.MIDDLE
write(ctf, "pipx install marrow", Pt(28), ACCENT, bold=True, font=MONO,
      first=True)

tf = textbox(slide, MARGIN, top + Inches(1.5), CONTENT_W, Inches(0.6))
write(tf, "Works with pytest, Jest, go test.", SZ_LEAD, MUTED, first=True)

prs.save("deck.pptx")
print("wrote deck.pptx")
Here’s what wasn’t good

The user corrects it, in their own words:

“Put the "Install Marrow" slide right after the title - don't bury the call-to-action at the end, lead with it.”

This stuck starting at run 2 — honored from then on.

“Add a "Why now" slide in between the idea and how-it-works slides - explain why this problem is urgent today, not just that it exists.”

This stuck starting at run 2 — honored from then on.

Here’s what changed

The same task, generated again once the learned layer is applied.

"""Build the Marrow pitch deck (deck.pptx) with python-pptx.

Slide order:
  1. Title
  2. Call to action  -- "Install Marrow" leads the deck, right after the title
  3. The problem
  4. The idea
  5. Why now         -- makes the case that manual coverage tracking is untenable
  6. How it works
"""

from pptx import Presentation
from pptx.dml.color import RGBColor
from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
from pptx.util import Inches, Pt

# ---------------------------------------------------------------- design system
FONT = "Helvetica Neue"
MONO = "Menlo"

BG = RGBColor(0xF7, 0xF6, 0xF3)      # warm paper
INK = RGBColor(0x1A, 0x1A, 0x1A)     # near-black text
MUTED = RGBColor(0x5F, 0x5C, 0x57)   # secondary text
ACCENT = RGBColor(0x0F, 0x7A, 0x6C)  # single accent: teal

TITLE_SIZE = Pt(44)
HERO_SIZE = Pt(76)
BODY_SIZE = Pt(22)
LEAD_SIZE = Pt(28)

SLIDE_W = Inches(13.333)
SLIDE_H = Inches(7.5)
MARGIN = Inches(1.1)
CONTENT_W = SLIDE_W - 2 * MARGIN

prs = Presentation()
prs.slide_width = SLIDE_W
prs.slide_height = SLIDE_H
BLANK = prs.slide_layouts[6]


def new_slide():
    """Blank slide with the deck background."""
    slide = prs.slides.add_slide(BLANK)
    bg = slide.shapes.add_shape(1, 0, 0, SLIDE_W, SLIDE_H)  # 1 = rectangle
    bg.fill.solid()
    bg.fill.fore_color.rgb = BG
    bg.line.fill.background()
    bg.shadow.inherit = False
    return slide


def textbox(slide, left, top, width, height):
    box = slide.shapes.add_textbox(left, top, width, height)
    tf = box.text_frame
    tf.word_wrap = True
    tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
    return tf


def style(run, size, color=INK, bold=False, font=FONT):
    run.font.name = font
    run.font.size = size
    run.font.color.rgb = color
    run.font.bold = bold


def para(tf, text, size, color=INK, bold=False, font=FONT,
         align=PP_ALIGN.LEFT, space_after=Pt(0), space_before=Pt(0), first=False):
    p = tf.paragraphs[0] if first else tf.add_paragraph()
    p.alignment = align
    p.space_after = space_after
    p.space_before = space_before
    p.line_spacing = 1.15
    style(p.add_run(), size, color, bold, font)
    p.runs[0].text = text
    return p


def accent_rule(slide, top, left=MARGIN, width=Inches(1.6)):
    """Short accent bar that sits under a slide title."""
    bar = slide.shapes.add_shape(1, left, top, width, Pt(5))
    bar.fill.solid()
    bar.fill.fore_color.rgb = ACCENT
    bar.line.fill.background()
    bar.shadow.inherit = False
    return bar


def section_title(slide, title):
    """Centered, top-aligned slide title with a centered accent rule."""
    tf = textbox(slide, MARGIN, Inches(0.85), CONTENT_W, Inches(1.1))
    para(tf, title, TITLE_SIZE, INK, bold=True, align=PP_ALIGN.CENTER, first=True)
    accent_rule(slide, Inches(2.05), left=(SLIDE_W - Inches(1.6)) // 2)


# ------------------------------------------------------------------- 1. Title
s = new_slide()
tf = textbox(s, MARGIN, Inches(2.5), CONTENT_W, Inches(1.9))
para(tf, "Marrow", HERO_SIZE, INK, bold=True, align=PP_ALIGN.CENTER, first=True)
accent_rule(s, Inches(4.15), left=(SLIDE_W - Inches(2.2)) // 2, width=Inches(2.2))
tf = textbox(s, MARGIN, Inches(4.6), CONTENT_W, Inches(0.9))
para(tf, "Know what your tests actually cover.", LEAD_SIZE, MUTED,
     align=PP_ALIGN.CENTER, first=True)

# ----------------------------------------------- 2. Call to action (leads deck)
s = new_slide()
section_title(s, "Install Marrow")

cmd = s.shapes.add_shape(1, Inches(3.4), Inches(3.0), Inches(6.533), Inches(1.3))
cmd.fill.solid()
cmd.fill.fore_color.rgb = RGBColor(0xEC, 0xEA, 0xE4)
cmd.line.color.rgb = ACCENT
cmd.line.width = Pt(1.5)
cmd.shadow.inherit = False
ctf = cmd.text_frame
ctf.word_wrap = True
ctf.vertical_anchor = MSO_ANCHOR.MIDDLE
para(ctf, "pipx install marrow", Pt(34), INK, bold=True, font=MONO,
     align=PP_ALIGN.CENTER, first=True)

tf = textbox(s, MARGIN, Inches(4.75), CONTENT_W, Inches(0.9))
para(tf, "Works with pytest, Jest, go test.", LEAD_SIZE, ACCENT,
     align=PP_ALIGN.CENTER, first=True)

# ------------------------------------------------------------- 3. The problem
s = new_slide()
section_title(s, "Big test suites hide their gaps.")
tf = textbox(s, MARGIN + Inches(0.9), Inches(3.0), CONTENT_W - Inches(1.8), Inches(2.6))
for i, line in enumerate([
    "Large suites are slow, and nobody knows which tests cover which code.",
    "Dead tests linger for years, quietly costing minutes on every run.",
    "Real gaps go unnoticed until something breaks in production.",
]):
    para(tf, line, BODY_SIZE, INK if i == 0 else MUTED,
         space_after=Pt(20), first=(i == 0))

# ----------------------------------------------------------------- 4. The idea
s = new_slide()
section_title(s, "Watch one run. Draw the real map.")
tf = textbox(s, MARGIN + Inches(0.9), Inches(3.1), CONTENT_W - Inches(1.8), Inches(2.4))
para(tf, "Marrow instruments a single test run and produces a test→code "
         "coverage map.", LEAD_SIZE, INK, first=True, space_after=Pt(26))
para(tf, "No annotations. No config. No rewriting your suite.", BODY_SIZE, ACCENT)

# ------------------------------------------------------------------ 5. Why now
s = new_slide()
section_title(s, "Why now")
tf = textbox(s, MARGIN + Inches(0.9), Inches(2.95), CONTENT_W - Inches(1.8), Inches(3.0))
para(tf, "Test suites have outgrown the humans who maintain them.", LEAD_SIZE,
     INK, first=True, space_after=Pt(24))
for line in [
    "A modern repo ships thousands of tests across years of contributors.",
    "Coverage percentages say how much code is covered — never by which test.",
    "Tracking that map by hand stopped being possible; it has to be measured.",
]:
    para(tf, line, BODY_SIZE, MUTED, space_after=Pt(16))

# --------------------------------------------------------------- 6. How it works
s = new_slide()
section_title(s, "How it works")

steps = [
    ("1", "Run it", "marrow watch -- <your test cmd>", True),
    ("2", "Marrow records", "which lines each test exercised", False),
    ("3", "You get a map", "interactive map + dead tests + untested lines", False),
]
col_w = Inches(3.5)
gap = Inches(0.55)
total = 3 * col_w + 2 * gap
left0 = (SLIDE_W - total) // 2

for i, (num, head, detail, is_code) in enumerate(steps):
    left = left0 + i * (col_w + gap)
    ntf = textbox(s, left, Inches(2.85), col_w, Inches(0.7))
    para(ntf, num, Pt(40), ACCENT, bold=True, align=PP_ALIGN.CENTER, first=True)
    htf = textbox(s, left, Inches(3.7), col_w, Inches(0.5))
    para(htf, head, Pt(24), INK, bold=True, align=PP_ALIGN.CENTER, first=True)
    dtf = textbox(s, left, Inches(4.35), col_w, Inches(1.5))
    para(dtf, detail, Pt(16) if is_code else Pt(18), MUTED,
         font=MONO if is_code else FONT, align=PP_ALIGN.CENTER, first=True)

prs.save("deck.pptx")
print("wrote deck.pptx with %d slides" % len(prs.slides._sldIdLst))
What actually changed, line by line (deck.py, cold → warm)
@@ -1,10 +1,10 @@-"""Build the 5-slide "Marrow" pitch deck (deck.pptx) with python-pptx.+"""Build the Marrow pitch deck (deck.pptx) with python-pptx. -Design system---------------Slides   : 16:9, 13.333in x 7.5in-Palette  : warm off-white background, near-black ink, one rust accent-Type     : Arial throughout; titles 40pt (72pt on the title slide), body 20pt-Layout   : blank layouts + explicit text boxes; titles top-aligned and centered,-           with a short accent rule underneath.+Slide order:+  1. Title+  2. Call to action  -- "Install Marrow" leads the deck, right after the title+  3. The problem+  4. The idea+  5. Why now         -- makes the case that manual coverage tracking is untenable+  6. How it works """@@ -13,31 +13,27 @@ from pptx.dml.color import RGBColor-from pptx.enum.shapes import MSO_SHAPE-from pptx.enum.text import MSO_ANCHOR, PP_ALIGN+from pptx.enum.text import PP_ALIGN, MSO_ANCHOR from pptx.util import Inches, Pt -# --- palette ----------------------------------------------------------------BG = RGBColor(0xFB, 0xFA, 0xF8)-INK = RGBColor(0x17, 0x18, 0x1A)-MUTED = RGBColor(0x5F, 0x63, 0x68)-ACCENT = RGBColor(0xB4, 0x53, 0x1F)-CHIP_BG = RGBColor(0xF1, 0xED, 0xE7)+# ---------------------------------------------------------------- design system+FONT = "Helvetica Neue"+MONO = "Menlo" -# --- type -------------------------------------------------------------------FONT = "Arial"-MONO = "Consolas"-SZ_WORDMARK = Pt(72)-SZ_TITLE = Pt(40)-SZ_LEAD = Pt(24)-SZ_BODY = Pt(20)-SZ_SMALL = Pt(16)+BG = RGBColor(0xF7, 0xF6, 0xF3)      # warm paper+INK = RGBColor(0x1A, 0x1A, 0x1A)     # near-black text+MUTED = RGBColor(0x5F, 0x5C, 0x57)   # secondary text+ACCENT = RGBColor(0x0F, 0x7A, 0x6C)  # single accent: teal -# --- geometry ---------------------------------------------------------------DECK_W = Inches(13.333)-DECK_H = Inches(7.5)-MARGIN = Inches(0.9)-CONTENT_W = DECK_W - 2 * MARGIN+TITLE_SIZE = Pt(44)+HERO_SIZE = Pt(76)+BODY_SIZE = Pt(22)+LEAD_SIZE = Pt(28)++SLIDE_W = Inches(13.333)+SLIDE_H = Inches(7.5)+MARGIN = Inches(1.1)+CONTENT_W = SLIDE_W - 2 * MARGIN  prs = Presentation()-prs.slide_width = DECK_W-prs.slide_height = DECK_H+prs.slide_width = SLIDE_W+prs.slide_height = SLIDE_H BLANK = prs.slide_layouts[6]@@ -46,5 +42,5 @@ def new_slide():-    """A blank slide painted with the deck background color."""+    """Blank slide with the deck background."""     slide = prs.slides.add_slide(BLANK)-    bg = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, 0, 0, DECK_W, DECK_H)+    bg = slide.shapes.add_shape(1, 0, 0, SLIDE_W, SLIDE_H)  # 1 = rectangle     bg.fill.solid()@@ -56,3 +52,3 @@ -def textbox(slide, left, top, width, height, anchor=MSO_ANCHOR.TOP):+def textbox(slide, left, top, width, height):     box = slide.shapes.add_textbox(left, top, width, height)@@ -60,5 +56,3 @@     tf.word_wrap = True-    tf.vertical_anchor = anchor-    tf.margin_left = tf.margin_right = 0-    tf.margin_top = tf.margin_bottom = 0+    tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0     return tf@@ -66,15 +60,18 @@ -def write(tf, text, size, color=INK, bold=False, align=PP_ALIGN.CENTER,-          font=FONT, space_after=0, line_spacing=1.15, first=False):-    """Add (or fill, if `first`) a paragraph with a single styled run."""+def style(run, size, color=INK, bold=False, font=FONT):+    run.font.name = font+    run.font.size = size+    run.font.color.rgb = color+    run.font.bold = bold+++def para(tf, text, size, color=INK, bold=False, font=FONT,+         align=PP_ALIGN.LEFT, space_after=Pt(0), space_before=Pt(0), first=False):     p = tf.paragraphs[0] if first else tf.add_paragraph()     p.alignment = align-    p.space_after = Pt(space_after)-    p.line_spacing = line_spacing-    run = p.add_run()-    run.text = text-    run.font.name = font-    run.font.size = size-    run.font.bold = bold-    run.font.color.rgb = color+    p.space_after = space_after+    p.space_before = space_before+    p.line_spacing = 1.15+    style(p.add_run(), size, color, bold, font)+    p.runs[0].text = text     return p@@ -82,6 +79,5 @@ -def accent_rule(slide, top, width=Inches(1.2), left=None):-    """The short rust rule that sits under every title."""-    left = (DECK_W - width) // 2 if left is None else left-    bar = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, left, top, width, Pt(4))+def accent_rule(slide, top, left=MARGIN, width=Inches(1.6)):+    """Short accent bar that sits under a slide title."""+    bar = slide.shapes.add_shape(1, left, top, width, Pt(5))     bar.fill.solid()@@ -93,98 +89,96 @@ -def title_block(slide, title):-    """Centered, top-aligned slide title + accent rule. Returns body top."""-    tf = textbox(slide, MARGIN, Inches(0.95), CONTENT_W, Inches(1.1))-    write(tf, title, SZ_TITLE, INK, bold=True, first=True)-    accent_rule(slide, Inches(2.15))-    return Inches(2.85)+def section_title(slide, title):+    """Centered, top-aligned slide title with a centered accent rule."""+    tf = textbox(slide, MARGIN, Inches(0.85), CONTENT_W, Inches(1.1))+    para(tf, title, TITLE_SIZE, INK, bold=True, align=PP_ALIGN.CENTER, first=True)+    accent_rule(slide, Inches(2.05), left=(SLIDE_W - Inches(1.6)) // 2)  -# --- 1. Title ---------------------------------------------------------------slide = new_slide()-tf = textbox(slide, MARGIN, Inches(2.45), CONTENT_W, Inches(1.4))-write(tf, "Marrow", SZ_WORDMARK, INK, bold=True, first=True)-accent_rule(slide, Inches(4.05), width=Inches(1.6))-tf = textbox(slide, MARGIN, Inches(4.55), CONTENT_W, Inches(0.7))-write(tf, "Know what your tests actually cover.", SZ_LEAD, MUTED, first=True)+# ------------------------------------------------------------------- 1. Title+s = new_slide()+tf = textbox(s, MARGIN, Inches(2.5), CONTENT_W, Inches(1.9))+para(tf, "Marrow", HERO_SIZE, INK, bold=True, align=PP_ALIGN.CENTER, first=True)+accent_rule(s, Inches(4.15), left=(SLIDE_W - Inches(2.2)) // 2, width=Inches(2.2))+tf = textbox(s, MARGIN, Inches(4.6), CONTENT_W, Inches(0.9))+para(tf, "Know what your tests actually cover.", LEAD_SIZE, MUTED,+     align=PP_ALIGN.CENTER, first=True) -# --- 2. The problem ---------------------------------------------------------slide = new_slide()-top = title_block(slide, "Big test suites hide their gaps.")-tf = textbox(slide, Inches(2.15), top, DECK_W - 2 * Inches(2.15), Inches(2.6))-write(tf, "Large suites are slow, and nobody knows which tests cover "-          "which code.", SZ_BODY, INK, space_after=18, line_spacing=1.35,-      first=True)-write(tf, "So dead tests linger for years — and the real gaps go unnoticed.",-      SZ_BODY, MUTED, line_spacing=1.35)+# ----------------------------------------------- 2. Call to action (leads deck)+s = new_slide()+section_title(s, "Install Marrow") -# --- 3. The idea ------------------------------------------------------------slide = new_slide()-top = title_block(slide, "Watch one run. Draw the real map.")-tf = textbox(slide, Inches(2.15), top, DECK_W - 2 * Inches(2.15), Inches(2.6))-write(tf, "Marrow instruments a single test run and produces a "-          "test → code coverage map.", SZ_BODY, INK, space_after=18,-      line_spacing=1.35, first=True)-write(tf, "No annotations. No config.", SZ_LEAD, ACCENT, bold=True)+cmd = s.shapes.add_shape(1, Inches(3.4), Inches(3.0), Inches(6.533), Inches(1.3))+cmd.fill.solid()+cmd.fill.fore_color.rgb = RGBColor(0xEC, 0xEA, 0xE4)+cmd.line.color.rgb = ACCENT+cmd.line.width = Pt(1.5)+cmd.shadow.inherit = False+ctf = cmd.text_frame+ctf.word_wrap = True+ctf.vertical_anchor = MSO_ANCHOR.MIDDLE+para(ctf, "pipx install marrow", Pt(34), INK, bold=True, font=MONO,+     align=PP_ALIGN.CENTER, first=True) -# --- 4. How it works --------------------------------------------------------slide = new_slide()-top = title_block(slide, "How it works")+tf = textbox(s, MARGIN, Inches(4.75), CONTENT_W, Inches(0.9))+para(tf, "Works with pytest, Jest, go test.", LEAD_SIZE, ACCENT,+     align=PP_ALIGN.CENTER, first=True) -STEPS = [-    ("01", "Run it", "marrow watch -- <your test cmd>", None),-    ("02", "It records", None,-     "Marrow records which lines each test exercised."),-    ("03", "It maps", None,-     "You get an interactive map, plus a list of dead tests and "-     "untested lines."),+# ------------------------------------------------------------- 3. The problem+s = new_slide()+section_title(s, "Big test suites hide their gaps.")+tf = textbox(s, MARGIN + Inches(0.9), Inches(3.0), CONTENT_W - Inches(1.8), Inches(2.6))+for i, line in enumerate([+    "Large suites are slow, and nobody knows which tests cover which code.",+    "Dead tests linger for years, quietly costing minutes on every run.",+    "Real gaps go unnoticed until something breaks in production.",+]):+    para(tf, line, BODY_SIZE, INK if i == 0 else MUTED,+         space_after=Pt(20), first=(i == 0))++# ----------------------------------------------------------------- 4. The idea+s = new_slide()+section_title(s, "Watch one run. Draw the real map.")+tf = textbox(s, MARGIN + Inches(0.9), Inches(3.1), CONTENT_W - Inches(1.8), Inches(2.4))+para(tf, "Marrow instruments a single test run and produces a test→code "+         "coverage map.", LEAD_SIZE, INK, first=True, space_after=Pt(26))+para(tf, "No annotations. No config. No rewriting your suite.", BODY_SIZE, ACCENT)++# ------------------------------------------------------------------ 5. Why now+s = new_slide()+section_title(s, "Why now")+tf = textbox(s, MARGIN + Inches(0.9), Inches(2.95), CONTENT_W - Inches(1.8), Inches(3.0))+para(tf, "Test suites have outgrown the humans who maintain them.", LEAD_SIZE,+     INK, first=True, space_after=Pt(24))+for line in [+    "A modern repo ships thousands of tests across years of contributors.",+    "Coverage percentages say how much code is covered — never by which test.",+    "Tracking that map by hand stopped being possible; it has to be measured.",+]:+    para(tf, line, BODY_SIZE, MUTED, space_after=Pt(16))++# --------------------------------------------------------------- 6. How it works+s = new_slide()+section_title(s, "How it works")++steps = [+    ("1", "Run it", "marrow watch -- <your test cmd>", True),+    ("2", "Marrow records", "which lines each test exercised", False),+    ("3", "You get a map", "interactive map + dead tests + untested lines", False), ]-gap = Inches(0.5)-col_w = (CONTENT_W - 2 * gap) // 3+col_w = Inches(3.5)+gap = Inches(0.55)+total = 3 * col_w + 2 * gap+left0 = (SLIDE_W - total) // 2 -for i, (num, label, code, body) in enumerate(STEPS):-    left = MARGIN + i * (col_w + gap)-    tf = textbox(slide, left, top, col_w, Inches(0.6))-    write(tf, num, Pt(30), ACCENT, bold=True, align=PP_ALIGN.LEFT, first=True)-    tf = textbox(slide, left, top + Inches(0.62), col_w, Inches(0.45))-    write(tf, label, SZ_BODY, INK, bold=True, align=PP_ALIGN.LEFT, first=True)--    body_top = top + Inches(1.2)-    if code:-        chip = slide.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE, left,-                                      body_top, col_w, Inches(1.1))-        chip.fill.solid()-        chip.fill.fore_color.rgb = CHIP_BG-        chip.line.fill.background()-        chip.shadow.inherit = False-        ctf = chip.text_frame-        ctf.word_wrap = True-        ctf.vertical_anchor = MSO_ANCHOR.MIDDLE-        ctf.margin_left = ctf.margin_right = Inches(0.18)-        write(ctf, code, SZ_SMALL, ACCENT, align=PP_ALIGN.LEFT, font=MONO,-              first=True)-    else:-        tf = textbox(slide, left, body_top, col_w, Inches(1.8))-        write(tf, body, SZ_SMALL, MUTED, align=PP_ALIGN.LEFT,-              line_spacing=1.35, first=True)--# --- 5. Call to action ------------------------------------------------------slide = new_slide()-top = title_block(slide, "Install Marrow")--chip_w = Inches(5.2)-chip = slide.shapes.add_shape(MSO_SHAPE.ROUNDED_RECTANGLE,-                              (DECK_W - chip_w) // 2, top, chip_w, Inches(1.0))-chip.fill.solid()-chip.fill.fore_color.rgb = CHIP_BG-chip.line.fill.background()-chip.shadow.inherit = False-ctf = chip.text_frame-ctf.vertical_anchor = MSO_ANCHOR.MIDDLE-write(ctf, "pipx install marrow", Pt(28), ACCENT, bold=True, font=MONO,-      first=True)--tf = textbox(slide, MARGIN, top + Inches(1.5), CONTENT_W, Inches(0.6))-write(tf, "Works with pytest, Jest, go test.", SZ_LEAD, MUTED, first=True)+for i, (num, head, detail, is_code) in enumerate(steps):+    left = left0 + i * (col_w + gap)+    ntf = textbox(s, left, Inches(2.85), col_w, Inches(0.7))+    para(ntf, num, Pt(40), ACCENT, bold=True, align=PP_ALIGN.CENTER, first=True)+    htf = textbox(s, left, Inches(3.7), col_w, Inches(0.5))+    para(htf, head, Pt(24), INK, bold=True, align=PP_ALIGN.CENTER, first=True)+    dtf = textbox(s, left, Inches(4.35), col_w, Inches(1.5))+    para(dtf, detail, Pt(16) if is_code else Pt(18), MUTED,+         font=MONO if is_code else FONT, align=PP_ALIGN.CENTER, first=True)  prs.save("deck.pptx")-print("wrote deck.pptx")+print("wrote deck.pptx with %d slides" % len(prs.slides._sldIdLst))
Show the raw learned layer

The learned layer injected into the warm run's prompt, verbatim:

You have a LEARNED LAYER of this user's preferences for this skill, captured from earlier runs. Apply ALL of it. Learnings are weighted tastes (higher weight = more established); issues are mandatory rules.

Learnings (preferences):
  - [L1] (slide order, weight 0.67) Don't save the call-to-action for last. Move the "Install Marrow" call-to-action slide to be the SECOND slide - right after the title, before the problem/idea/how-it-works slides. Lead with the install command, then explain why it matters.
  - [L2] (slide structure, weight 0.67) Insert a new slide titled "Why now" between "The idea" and "How it works", making the case that test suites have grown large enough that manual coverage tracking has become untenable - this is new content the outline doesn't ask for, added because it strengthens the pitch.