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feat(altair): implement marimekko-basic#5480

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implementation/marimekko-basic/altair
Apr 29, 2026
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feat(altair): implement marimekko-basic#5480
MarkusNeusinger merged 7 commits intomainfrom
implementation/marimekko-basic/altair

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Implementation: marimekko-basic - python/altair

Implements the python/altair version of marimekko-basic.

File: plots/marimekko-basic/implementations/python/altair.py

Parent Issue: #1002


🤖 impl-generate workflow

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🔧 AI Review Produced No Score — Auto-Retrying

The Claude Code Action ran but didn't write quality_score.txt. Auto-retrying review once...


🤖 impl-review

@github-actions github-actions Bot added the ai-review-failed AI review action failed or timed out label Apr 27, 2026
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❌ AI Review Failed (auto-retry exhausted)

The AI review action completed but did not produce valid output files. Auto-retry already tried once.

What happened:

  • The Claude Code Action ran
  • No quality_score.txt file was created

Manual rerun:

gh workflow run impl-review.yml -f pr_number=5480

🤖 impl-review

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claude Bot commented Apr 29, 2026

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot renders on a warm off-white background (~#FAF8F1). It shows a proper Marimekko chart with four proportional-width columns (Asia Pacific and North America widest at ~31% each, Europe ~25%, Latin America ~14%). Each column is divided into four colored stacked segments — Electronics (teal-green, bottom), Clothing (burnt orange), Food (steel blue), Home (pinkish mauve, top) — separated by thin white borders. Revenue labels (e.g. $100M, $60M, $80M) appear as white text inside each segment. Region names and totals (Asia Pacific / $300M) are shown as bold dark text below each column. A legend titled "Product Line" sits on the right with a light background and dark text. The title "marimekko-basic · altair · anyplot.ai" is prominently rendered in bold dark text at the top center. All text is clearly legible against the light background — no "light-on-light" failures.

Dark render (plot-dark.png): The same chart renders on a near-black background (~#1A1A17). The data segment colors are visually identical to the light render (teal-green, burnt orange, steel blue, pinkish mauve). Title, axis label, tick labels, and region labels are rendered in light/white text against the dark background — all readable. The legend background flips to dark with light text. Revenue labels remain white on the colored segments. No "dark-on-dark" failures observed. Theme-adaptive chrome is working correctly in the rendered images.

⚠️ Code–Image Discrepancy (Critical): The images do NOT match the committed code. The code sets colors = ["#306998", "#FFD43B", "#4ECDC4", "#E76F51"] (Python Blue palette) but the images show Okabe-Ito colors. The code title is "marimekko-basic · altair · pyplots.ai" but the image shows "anyplot.ai". The code has no revenue labels inside segments but the images show $XXM labels. The code has no ANYPLOT_THEME handling but distinct light/dark renders exist. Images appear to have been generated from a different (better) code version not currently committed.

Score: 82/100

Category Score Max
Visual Quality 28 30
Design Excellence 12 20
Spec Compliance 12 15
Data Quality 15 15
Code Quality 8 10
Library Mastery 7 10
Total 82 100

Visual Quality (28/30)

  • VQ-01: Text Legibility (7/8) — Title 28px, axis label 20px, tick labels 16px, region labels 18px all explicitly set. Readable in both themes. Minor: revenue labels in narrow Latin America column are small but still legible.
  • VQ-02: No Overlap (6/6) — No overlapping text elements in either render.
  • VQ-03: Element Visibility (6/6) — All rect segments clearly visible with white stroke separation.
  • VQ-04: Color Accessibility (2/2) — Okabe-Ito palette in rendered images is CVD-safe; white borders improve per-segment differentiation.
  • VQ-05: Layout & Canvas (4/4) — Chart fills canvas well; legend is appropriately positioned to the right; region labels and margins are balanced.
  • VQ-06: Axis Labels & Title (2/2) — Y-axis labeled "Product Mix (%)" with units; title is descriptive.
  • VQ-07: Palette Compliance (1/2) — Rendered images show Okabe-Ito colors (Electronics = #009E73 first, correct order) with correct backgrounds (#FAF8F1 light, #1A1A17 dark). However, the committed code explicitly sets colors = ["#306998", "#FFD43B", "#4ECDC4", "#E76F51"] (Python Blue palette), which is explicitly forbidden. Partial score because images pass but code does not.

Design Excellence (12/20)

  • DE-01: Aesthetic Sophistication (4/8) — Well-configured with clean Okabe-Ito palette and white segment borders. Reads as a polished library configuration rather than publication-ready design. Consistent with "well-configured default" tier.
  • DE-02: Visual Refinement (4/6) — White borders between segments add visual definition. Grid is subtle. Background colors are correct. Spines are removed via configure_view(strokeWidth=0). Above the 2/6 default.
  • DE-03: Data Storytelling (4/6) — Revenue labels enable immediate value reading. Proportional widths visually communicate market scale differences. Electronics dominance across all regions is visually apparent. Effective use of the Marimekko form's natural storytelling.

Spec Compliance (12/15)

  • SC-01: Plot Type (5/5) — Correct Marimekko chart with proportional bar widths and stacked proportional heights.
  • SC-02: Required Features (3/4) — Proportional widths ✓, proportional heights ✓, color-coded legend ✓, value labels visible in images ✓. However, value labels exist in rendered images but are absent from the committed code — a reliability concern.
  • SC-03: Data Mapping (3/3) — X-categories (regions) correctly drive bar widths; Y-axis shows Product Mix %; data mapping is correct.
  • SC-04: Title & Legend (1/3) — Rendered image title is correct ("marimekko-basic · altair · anyplot.ai"). But the committed code has "marimekko-basic · altair · pyplots.ai" — wrong branding. Legend labels match data categories. Deducted for code-level title error.

Data Quality (15/15)

  • DQ-01: Feature Coverage (6/6) — Shows all Marimekko aspects: 4 regions with varying total sizes, 4 products with varying mix proportions across regions. Full variation.
  • DQ-02: Realistic Context (5/5) — Real-world global market revenue scenario; neutral business context; comprehensible geographic regions.
  • DQ-03: Appropriate Scale (4/4) — Revenue values ($25M–$120M per segment, $130M–$300M per region) are realistic for a simplified market analysis.

Code Quality (8/10)

  • CQ-01: KISS Structure (3/3) — Flat script with no functions or classes.
  • CQ-02: Reproducibility (2/2) — Fully deterministic hardcoded data.
  • CQ-03: Clean Imports (2/2) — Only altair and pandas imported, both used.
  • CQ-04: Code Elegance (1/2) — Code is Pythonic and readable but critically incomplete: missing ANYPLOT_THEME handling, wrong palette, wrong title string.
  • CQ-05: Output & API (0/1) — Code saves plot.png / plot.html instead of plot-light.png / plot-dark.png / plot-light.html / plot-dark.html as required for theme-aware Altair implementations.

Library Mastery (7/10)

  • LM-01: Idiomatic Usage (4/5) — Good use of Altair's high-level API: mark_rect with x/x2/y/y2 encodings for variable-width rectangles, proper :Q/:N type declarations, alt.layer() composition, alt.Scale/alt.Legend customization.
  • LM-02: Distinctive Features (3/5) — The x2/y2 rect encoding approach for Marimekko construction is distinctively Altair; alt.layer() composition; interactive tooltip encoding that works in both PNG and HTML export; HTML export with tooltips is a meaningful Altair differentiator.

Score Caps Applied

  • None — No caps triggered (DE-01=4, DE-02=4; all VQ > 0; SC-01 > 0; DQ-02 > 0; CQ-04=1 not 0).

Strengths

  • Excellent Marimekko construction using Altair's x/x2/y/y2 rect mark approach — idiomatic and clean
  • Proportional width calculation using cumsum() is correct and well-structured
  • Good data storytelling: revenue labels + proportional sizing communicate market structure at a glance
  • Interactive tooltips (Region, Product, Revenue, % of Region) provide full context in the HTML output
  • Realistic, neutral market data with meaningful variation across regions and products
  • Both theme renders pass the legibility check

Weaknesses

  • CRITICAL — Code uses forbidden Python Blue palette: colors = ["#306998", "#FFD43B", "#4ECDC4", "#E76F51"] must be replaced with Okabe-Ito: ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
  • CRITICAL — No ANYPLOT_THEME handling: Code must read os.getenv("ANYPLOT_THEME", "light") and apply PAGE_BG/INK/INK_SOFT/ELEVATED_BG tokens via .properties(background=PAGE_BG), .configure_axis(...), .configure_title(color=INK), .configure_legend(fillColor=ELEVATED_BG, ...)
  • CRITICAL — Wrong output filenames: Must save as plot-{THEME}.png and plot-{THEME}.html, not bare plot.png/plot.html
  • CRITICAL — Wrong title branding: Change "pyplots.ai""anyplot.ai" in the title string
  • Missing revenue labels in code: The rendered images show $XXM labels inside segments (a significant usability improvement) but no corresponding mark_text layer exists in the committed code — add a text layer for segment value labels
  • Grid opacity 0.3 is too high; reduce to 0.1 per style guide

Issues Found

  1. VQ-07 + CQ-04 LOW: Python Blue palette (#306998) explicitly set instead of Okabe-Ito
    • Fix: Replace colors = ["#306998", "#FFD43B", "#4ECDC4", "#E76F51"] with OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
  2. CQ-05 FAIL + CQ-04 LOW: No theme handling and wrong output filenames
    • Fix: Add import os, THEME = os.getenv("ANYPLOT_THEME", "light"), set PAGE_BG/INK/INK_SOFT tokens, apply via .configure_*() methods, save as f"plot-{THEME}.png" and f"plot-{THEME}.html"
  3. SC-04 LOW: Title says "pyplots.ai" instead of "anyplot.ai"
    • Fix: Change title string to f"marimekko-basic · altair · anyplot.ai"
  4. SC-02 PARTIAL: Revenue labels visible in images but missing from code
    • Fix: Add a mark_text layer reading from df with Revenue formatted as $XXM
  5. DE-02: Grid opacity at 0.3 is too prominent
    • Fix: Change gridOpacity=0.3 to gridOpacity=0.1

AI Feedback for Next Attempt

The chart structure is solid. The critical repair items are all code-level: (1) Replace #306998 palette with Okabe-Ito ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]. (2) Add full ANYPLOT_THEME handling — read os.getenv("ANYPLOT_THEME", "light"), define PAGE_BG/ELEVATED_BG/INK/INK_SOFT tokens, apply them via .properties(background=PAGE_BG), .configure_axis(labelColor=INK_SOFT, titleColor=INK, gridOpacity=0.1, ...), .configure_title(color=INK), and .configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK). (3) Change output saves to f"plot-{THEME}.png" and f"plot-{THEME}.html". (4) Fix title to "anyplot.ai". (5) Optionally add a mark_text layer for revenue labels inside segments to match the previously generated images.

Verdict: REJECTED

@github-actions github-actions Bot added quality:82 Quality score 82/100 ai-rejected Quality not OK, triggers update labels Apr 29, 2026
@github-actions github-actions Bot added ai-attempt-1 First repair attempt and removed ai-rejected Quality not OK, triggers update labels Apr 29, 2026
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🔧 Repair Attempt 1/4

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...


🤖 impl-repair

MarkusNeusinger added a commit that referenced this pull request Apr 29, 2026
…ompts (#5520)

## Summary

Three workflows (`impl-review.yml`, `spec-create.yml`,
`report-validate.yml`) used shell-style `$VAR` inside `with: prompt: |`
blocks of `claude-code-action`. That block is a YAML string handed to a
Node/Bun action — **no shell ever runs**, so `$VAR` was sent to Claude
as a literal placeholder instead of the actual value. Result: Claude
couldn't reliably identify the PR / spec / library to review and
silently produced no `quality_score.txt`, which the validate step turns
into `ai-review-failed`.

## Symptoms observed today (2026-04-29)

5 stuck implementation PRs from 2026-04-27, all with `ai-review-failed`
despite the prior fixes branch (#5410) and the audit branch (#5515)
landing in between:

| PR | Branch | Pre-fix labels |
|----|--------|----------------|
| #5476 | seaborn/marimekko-basic | `ai-review-failed`, `quality:78` |
| #5480 | altair/marimekko-basic | `ai-review-failed`, `quality:82` |
| #5481 | letsplot/marimekko-basic | `ai-rejected`, `quality:76` |
| #5483 | plotnine/marimekko-basic | `ai-review-failed` |
| #5486 | plotly/line-basic | `ai-review-failed` |

Re-dispatching review on each confirmed the bug: the run log of `Run AI
Quality Review` shows the prompt being passed verbatim:

```
PROMPT: Read prompts/workflow-prompts/ai-quality-review.md and follow those instructions.

Variables for this run:
- LIBRARY: $LIBRARY    # ← literal, never expanded
- SPEC_ID: $SPEC_ID
- PR_NUMBER: $PR_NUMBER
- ATTEMPT: $ATTEMPT
```

Claude's review then either ran for ~20s and exited with no
`quality_score.txt` (4 PRs failed), or recovered by inferring values
from cwd (1 PR succeeded with `quality:82`). The intermittent pattern is
exactly what you'd expect from "the prompt is ambiguous and Claude has
to guess from context."

## Root cause

Commit `252977cf3` ("chore: fix critical audit findings", 2026-04-28
22:46) routed several `${{ github.event.* }}` and step-output values
through step-level `env:` and rewrote the in-prompt references as
`$VAR`. That is the correct mitigation for `run:` shell steps and Python
heredocs in the same workflows (and those changes stay in place). Inside
`with: prompt: |` it is the wrong tool: the value is consumed by a JS
action, not a shell, so there is no injection surface to mitigate and
`$VAR` does not interpolate.

`spec-create.yml` and `report-validate.yml` carry the identical
anti-pattern in their `prompt:` blocks. They haven't surfaced as
failures yet only because no triggering issue has come in since
2026-04-28.

## The fix

Revert **only** the descriptive header lines of each `prompt:` block
back to GitHub Actions Expression syntax (`${{ ... }}`), which the
runner substitutes into the YAML string before the action receives it.
Keep:

- All `env:` blocks (harmless; lets future prompt content reference env
vars if useful)
- All `$VAR` references inside **embedded bash code samples** in the
prompt (e.g. `gh issue edit $ISSUE_NUMBER`). Those are executed by
Claude's Bash tool which inherits the step `env:` and expands them
correctly — and rewriting them would re-enable the injection vector the
audit was right to close.

```diff
             Variables for this run:
-            - LIBRARY: $LIBRARY
-            - SPEC_ID: $SPEC_ID
-            - PR_NUMBER: $PR_NUMBER
-            - ATTEMPT: $ATTEMPT
+            - LIBRARY: ${{ steps.pr.outputs.library }}
+            - SPEC_ID: ${{ steps.pr.outputs.specification_id }}
+            - PR_NUMBER: ${{ steps.pr.outputs.pr_number }}
+            - ATTEMPT: ${{ steps.attempts.outputs.display }}
```

(analogous 8-line revert in `spec-create.yml` × 2 prompt blocks and
4-line revert in `report-validate.yml`).

Diff total: **3 files, 16 ±**.

## Test plan

- [ ] After merge, redispatch `impl-review.yml` for the 4 stuck PRs (`gh
workflow run impl-review.yml -f pr_number=<N>` for 5476, 5483, 5486;
5480 already got a 82 in the redispatch and should now stabilize)
- [ ] Verify each run's `Run AI Quality Review` step log shows real
values (e.g. `- LIBRARY: plotly`) in the PROMPT echo, not `$LIBRARY`
- [ ] Verify `quality_score.txt` is produced and `ai-review-failed`
label is removed
- [ ] On next `spec-request`-labeled issue, verify the spec-create
prompt sees the issue title/body
- [ ] On next `report-pending`-labeled issue, verify the report-validate
prompt sees the issue title/body

🤖 Generated with [Claude Code](https://claude.com/claude-code)
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claude Bot commented Apr 29, 2026

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): The chart renders on a warm off-white background (~#FAF8F1 — clearly not pure white). It shows a correct Marimekko chart with four proportional-width columns: Asia Pacific and North America are the widest (~31% each, $300M totals), Europe is narrower (~25%, $240M), and Latin America is the narrowest (~14%, $130M). Each column is divided into four stacked segments — Electronics (teal-green #009E73, bottom), Clothing (burnt orange #D55E00), Food (steel blue #0072B2), Home (pinkish mauve #CC79A7, top) — separated by thin white borders. White revenue labels appear inside every segment ($100M, $120M, $90M, $40M, etc.). Region names with market totals appear in bold at the bottom of each column. Legend titled "Product Line" sits at top-right. Y-axis "Product Mix (%)" runs 0–100. Title "marimekko-basic · altair · anyplot.ai" is bold and centered at the top. All text is dark on the light background — legibility is unambiguous across all elements.
Legibility verdict: PASS

Dark render (plot-dark.png): The same chart on a near-black background (~#1A1A17). All data colors are visually identical to the light render — teal-green Electronics, burnt orange Clothing, steel blue Food, pinkish mauve Home — confirming Okabe-Ito positions 1–4 are theme-invariant. Title, Y-axis label, tick labels, region labels, legend text, and revenue labels all render in white/light text against the dark background. No dark-on-dark failures observed; the legend box uses an elevated dark background with light text. The overall structure is a mirror of the light render with only chrome elements flipped.
Legibility verdict: PASS

Both paragraphs are required. A review that only describes one render is invalid.

Score: 87/100

Category Score Max
Visual Quality 29 30
Design Excellence 11 20
Spec Compliance 15 15
Data Quality 15 15
Code Quality 9 10
Library Mastery 8 10
Total 87 100

Visual Quality (29/30)

  • VQ-01: Text Legibility (7/8) — All sizes explicitly set; title 28px ✓, legend 16px ✓, but configure_axis labelFontSize=16 (style guide: 18) and titleFontSize=20 (style guide: 22) are slightly below spec for pixel-based libs
  • VQ-02: No Overlap (6/6) — No collisions in either render; revenue labels and region labels all clear
  • VQ-03: Element Visibility (6/6) — All segments, labels, and legend symbols clearly visible at full resolution
  • VQ-04: Color Accessibility (2/2) — Okabe-Ito is CVD-safe; white revenue labels on colored segments provide strong contrast
  • VQ-05: Layout & Canvas (4/4) — Chart fills canvas well; proportional columns use the full width; legend placed compactly; balanced margins
  • VQ-06: Axis Labels & Title (2/2) — Y-axis "Product Mix (%)" with units; title in required format
  • VQ-07: Palette Compliance (2/2) — Images show correct Okabe-Ito order starting with #009E73; light background #FAF8F1, dark background #1A1A17; chrome flips correctly between themes

Design Excellence (11/20)

  • DE-01: Aesthetic Sophistication (4/8) — Clean, well-composed output with Okabe-Ito colors and white segment borders. Revenue labels add information density. Looks like a polished library-configured output but not publication-ready — no exceptional typographic or compositional choices beyond what the repair guided.
  • DE-02: Visual Refinement (3/6) — configure_view(strokeWidth=0) removes the view frame; grid is absent or very subtle; backgrounds are theme-correct. Refinement is present but standard for a repaired output.
  • DE-03: Data Storytelling (4/6) — Variable column widths immediately communicate that Asia Pacific and North America are equal-largest markets while Latin America is smallest. Revenue labels let the viewer read absolute values without guessing. Clear visual hierarchy guides the reader.

Spec Compliance (15/15)

  • SC-01: Plot Type (5/5) — Correct Marimekko with variable-width bars proportional to total revenue
  • SC-02: Required Features (4/4) — Proportional widths, proportional heights, color-coded y-categories, legend, value labels on larger segments
  • SC-03: Data Mapping (3/3) — X-categories (regions) determine bar widths; y-categories (products) stacked as % within region; area encodes actual revenue
  • SC-04: Title & Legend (3/3) — Title "marimekko-basic · altair · anyplot.ai" matches required format (images); legend "Product Line" with correct category labels

Data Quality (15/15)

  • DQ-01: Feature Coverage (6/6) — Shows all Marimekko features: variable widths, variable heights, and a 4×4 matrix demonstrating cross-tabulation
  • DQ-02: Realistic Context (5/5) — Retail revenue by region and product line is a canonical, neutral Marimekko use case
  • DQ-03: Appropriate Scale (4/4) — Revenue values $25M–$120M per segment; totals $130M–$300M per region — realistic for a mid-sized retail business

Code Quality (9/10)

  • CQ-01: KISS Structure (3/3) — Linear flow: imports → data dict → DataFrame → region totals → position math → chart → save
  • CQ-02: Reproducibility (2/2) — Fully deterministic hard-coded data; no random elements
  • CQ-03: Clean Imports (2/2) — Only altair and pandas imported; both used
  • CQ-04: Code Elegance (2/2) — Clean, Pythonic; position math is appropriate complexity for a manual Marimekko; no over-engineering
  • CQ-05: Output & API (0/1) — CRITICAL mismatch: committed code saves plot.png / plot.html (no ANYPLOT_THEME, no plot-{THEME}.png / plot-{THEME}.html). The repair generated correct themed images but did not commit the fixed code to the PR branch — the working-tree Python file still contains the original unthemed output logic.

Library Mastery (8/10)

  • LM-01: Idiomatic Usage (5/5) — x/x2/y/y2 rect encoding for variable-width bars is the canonical Altair approach to Marimekko charts; layer composition for adding region labels; configure_axis/configure_view — all idiomatic Altair patterns
  • LM-02: Distinctive Features (3/5) — Altair's explicit x2/y2 dual-bound rect encoding is a genuinely distinctive feature not easily replicated in matplotlib or seaborn. Tooltip encoding with multiple fields is also Altair-native. Does not leverage selection/brush interactivity.

Score Caps Applied

  • None

Strengths

  • Correct Marimekko geometry: x/x2/y/y2 rect approach with cumsum() position calculation is mathematically sound and idiomatically Altair
  • Revenue labels inside segments provide at-a-glance quantification — good data storytelling addition
  • Excellent spec compliance: proportional widths + heights, color coding, legend all present
  • Neutral, realistic retail market scenario with meaningful regional variation
  • Both theme renders pass legibility checks with identical data colors

Weaknesses

  • CRITICAL — code not committed: The repair generated correct themed images (Okabe-Ito palette, anyplot.ai title, ANYPLOT_THEME branching, plot-{THEME}.png output, revenue mark_text layer) but the committed altair.py still has the original unthemed code: Python Blue #306998 palette, pyplots.ai title, final_chart.save("plot.png"). The fix must be committed to the PR branch.
  • configure_axis labelFontSize=16 → should be 18px; titleFontSize=20 → should be 22px (altair.md style guide for pixel-based libs)
  • gridOpacity=0.3 is too high — style guide specifies 0.10
  • Revenue labels (mark_text layer) not present in committed code — must be part of the committed fix

Issues Found

  1. CQ-05 FAIL: Code saves plot.png / plot.html with no ANYPLOT_THEME; must use os.getenv("ANYPLOT_THEME", "light") and save f'plot-{THEME}.png' / f'plot-{THEME}.html' as shown in prompts/library/altair.md
    • Fix: Add full theme-adaptive block from altair.md, including PAGE_BG/INK/INK_SOFT tokens in configure_axis/configure_title/configure_legend
  2. VQ-01 minor: configure_axis labelFontSize=16 → 18, titleFontSize=20 → 22 per altair.md
  3. VQ-07 / gridOpacity: gridOpacity=0.3 → 0.10

AI Feedback for Next Attempt

The visual output (from repair) is correct and scores 87. The blocker is that the committed Python file still has the old unthemed code. Ensure the fix includes: (1) ANYPLOT_THEME = os.getenv("ANYPLOT_THEME", "light") with PAGE_BG/INK/INK_SOFT token block, (2) palette ['#009E73', '#D55E00', '#0072B2', '#CC79A7'], (3) chart title anyplot.ai, (4) save as f'plot-{THEME}.png' and f'plot-{THEME}.html', (5) revenue mark_text layer, (6) configure_axis labelFontSize=18, titleFontSize=22, gridOpacity=0.10.

Verdict: REJECTED

@github-actions github-actions Bot added quality:87 Quality score 87/100 ai-approved Quality OK, ready for merge labels Apr 29, 2026
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claude Bot commented Apr 29, 2026

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): The chart renders on a warm off-white background (~#FAF8F1 — clearly not pure white, a cream/warm tone is visible). It shows a correct Marimekko chart with four proportional-width columns: Asia Pacific and North America are the widest (~31% each, $300M totals), Europe is slightly narrower (~25%, $240M), and Latin America is the narrowest (~14%, $130M). Each column is divided into four stacked segments — Electronics (teal-green #009E73, bottom), Clothing (burnt orange #D55E00), Food (steel blue #0072B2), Home (pinkish mauve #CC79A7, top) — separated by thin white borders. White revenue labels appear inside each segment ($100M, $120M, $90M, $40M, etc.), all readable. Region names and totals (e.g. "Asia Pacific / $300M") are shown in bold dark text below each column. Legend titled "Product Line" sits at top-right with light background and dark text. Y-axis "Product Mix (%)" runs 0–100 in dark text. Title "marimekko-basic · altair · anyplot.ai" is bold and centered at top. All text is dark against the light background — no light-on-light failures.
Legibility verdict: PASS

Dark render (plot-dark.png): The same chart on a near-black background (~#1A1A17). All data segment colors are visually identical to the light render — teal-green Electronics, burnt orange Clothing, steel blue Food, pinkish mauve Home — confirming Okabe-Ito positions 1–4 are theme-invariant. Title, Y-axis label, tick labels, region labels, revenue labels, and legend text all render in white/light text against the dark background. The legend box uses an elevated dark fill with light text. No dark-on-dark failures observed; theme chrome has flipped correctly for all elements.
Legibility verdict: PASS

⚠️ Code–Image Discrepancy (Critical, repeat from Attempt 1): The images do NOT reflect the committed code. The altair.py file still contains: colors = ["#306998", "#FFD43B", "#4ECDC4", "#E76F51"] (Python Blue, explicitly forbidden), title "marimekko-basic · altair · pyplots.ai" (wrong branding), final_chart.save("plot.png") (wrong filename — no ANYPLOT_THEME), and no revenue mark_text layer. The repair generated correct images but did not commit the fixed Python file to the PR branch.

Score: 87/100

Category Score Max
Visual Quality 29 30
Design Excellence 11 20
Spec Compliance 15 15
Data Quality 15 15
Code Quality 9 10
Library Mastery 8 10
Total 87 100

Visual Quality (29/30)

  • VQ-01: Text Legibility (7/8) — Title 28px ✓, legend 16px ✓; configure_axis labelFontSize=16 (style guide: 18px) and titleFontSize=20 (style guide: 22px) are slightly below spec for pixel-based libs
  • VQ-02: No Overlap (6/6) — No text collisions in either render; revenue labels and region totals all clear
  • VQ-03: Element Visibility (6/6) — All segments, revenue labels, and legend symbols clearly visible at full resolution
  • VQ-04: Color Accessibility (2/2) — Okabe-Ito is CVD-safe; white revenue labels on colored segments provide strong contrast
  • VQ-05: Layout & Canvas (4/4) — Chart fills canvas well; proportional columns use full width; legend compactly positioned top-right; balanced margins
  • VQ-06: Axis Labels & Title (2/2) — Y-axis "Product Mix (%)" with units; title matches required format in rendered images
  • VQ-07: Palette Compliance (2/2) — Rendered images show correct Okabe-Ito order starting with #009E73; light background #FAF8F1, dark background #1A1A17; chrome flips correctly. (Score reflects image output; code-level palette violation noted in weaknesses.)

Design Excellence (11/20)

  • DE-01: Aesthetic Sophistication (4/8) — Clean, well-composed output with Okabe-Ito colors, white segment borders, and revenue labels. Reads as a polished library-configured output but not publication-ready; no exceptional typographic or compositional choices beyond what the repair guided.
  • DE-02: Visual Refinement (3/6) — configure_view(strokeWidth=0) removes the view frame; grid absent or very subtle; theme-correct backgrounds in both renders. Refinement is present but standard.
  • DE-03: Data Storytelling (4/6) — Variable column widths immediately communicate that Asia Pacific and North America are the equal-largest markets while Latin America is smallest. Revenue labels enable direct value reading. Clear visual hierarchy guides the reader.

Spec Compliance (15/15)

  • SC-01: Plot Type (5/5) — Correct Marimekko chart with variable-width proportional bars and stacked proportional heights
  • SC-02: Required Features (4/4) — Proportional widths ✓, proportional heights ✓, color-coded y-categories ✓, legend ✓, value labels on segments ✓ (in rendered images)
  • SC-03: Data Mapping (3/3) — Regions determine bar widths; products stacked as % within region; area encodes actual revenue
  • SC-04: Title & Legend (3/3) — Rendered image title "marimekko-basic · altair · anyplot.ai" matches required format; legend "Product Line" with correct category labels

Data Quality (15/15)

  • DQ-01: Feature Coverage (6/6) — 4×4 matrix with variable widths and heights demonstrates full Marimekko feature set; cross-tabulation clearly visible
  • DQ-02: Realistic Context (5/5) — Retail revenue by region and product line is a canonical, neutral Marimekko use case
  • DQ-03: Appropriate Scale (4/4) — $25M–$120M per segment; $130M–$300M per region — realistic for a mid-sized retail business

Code Quality (9/10)

  • CQ-01: KISS Structure (3/3) — Linear flow: imports → data dict → DataFrame → position math → chart layers → save
  • CQ-02: Reproducibility (2/2) — Fully deterministic hard-coded data; no random elements
  • CQ-03: Clean Imports (2/2) — Only altair and pandas imported; both used
  • CQ-04: Code Elegance (2/2) — Clean, Pythonic; position math using cumsum() is appropriate complexity; no over-engineering
  • CQ-05: Output & API (0/1) — CRITICAL (second occurrence): committed code saves plot.png / plot.html with no ANYPLOT_THEME handling. Required: os.getenv("ANYPLOT_THEME", "light") + save f'plot-{THEME}.png' / f'plot-{THEME}.html' per prompts/library/altair.md

Library Mastery (8/10)

  • LM-01: Idiomatic Usage (5/5) — x/x2/y/y2 rect encoding for variable-width bars is the canonical Altair Marimekko approach; alt.layer() composition; configure_axis/configure_view — all idiomatic Altair patterns
  • LM-02: Distinctive Features (3/5) — Altair's dual-bound x2/y2 rect encoding is genuinely distinctive and not easily replicated in matplotlib/seaborn; multi-field tooltip encoding is Altair-native; HTML export with interactive tooltips is a meaningful differentiator

Score Caps Applied

  • None — DE-01=4 and DE-02=3, so the "correct but boring" cap (DE-01 ≤ 2 AND DE-02 ≤ 2) does not trigger

Strengths

  • Correct Marimekko geometry: x/x2/y/y2 rect approach with cumsum() position calculation is mathematically sound and idiomatically Altair
  • Revenue labels inside segments provide at-a-glance quantification — good data storytelling addition
  • Excellent spec compliance: proportional widths + heights, color coding, legend, and value labels all present in rendered output
  • Neutral, realistic retail market scenario with meaningful regional variation across all four markets
  • Both theme renders pass legibility checks; data colors are identical across light and dark

Weaknesses

  • CRITICAL (repeat) — fixed code not committed to PR branch: The repair generated correct themed images (Okabe-Ito palette starting #009E73, anyplot.ai title, ANYPLOT_THEME branching, plot-{THEME}.png output, revenue mark_text layer) but committed altair.py still contains the original unthemed code: Python Blue #306998 palette, pyplots.ai title, final_chart.save("plot.png") — the fix must be committed to the PR branch
  • configure_axis labelFontSize=16 should be 18px; titleFontSize=20 should be 22px per prompts/library/altair.md spec for pixel-based libraries
  • gridOpacity=0.3 is too prominent — style guide specifies 0.10
  • Revenue mark_text layer is missing from committed code and must be included in the fix

Issues Found

  1. CQ-05 FAIL (second occurrence): Code saves plot.png / plot.html with no ANYPLOT_THEME; must use os.getenv("ANYPLOT_THEME", "light") and save f'plot-{THEME}.png' / f'plot-{THEME}.html' as shown in prompts/library/altair.md
    • Fix: Add full theme-adaptive block — PAGE_BG/INK/INK_SOFT tokens applied via .properties(background=PAGE_BG), .configure_axis(labelColor=INK_SOFT, titleColor=INK, gridOpacity=0.10), .configure_title(color=INK), .configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
  2. VQ-07 / Code palette violation: colors = ["#306998", ...] — replace with Okabe-Ito ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
  3. SC-04 / Title branding: Change "pyplots.ai""anyplot.ai" in the title string
  4. SC-02 / Missing revenue labels in code: Add a mark_text layer rendering Revenue as $XXM inside each segment (matching the repaired images)
  5. VQ-01 minor: configure_axis labelFontSize=16 → 18, titleFontSize=20 → 22
  6. VQ-07 / gridOpacity: gridOpacity=0.3 → 0.10

AI Feedback for Next Attempt

The chart structure, data, and visual output (from repair) are correct and score well. The sole blocker is that the repaired code was not committed back to the PR branch. The committed altair.py still contains the original unthemed code. The fix for attempt 3 must commit ALL of the following to the file: (1) import os + THEME = os.getenv("ANYPLOT_THEME", "light") with PAGE_BG/ELEVATED_BG/INK/INK_SOFT token block; (2) palette ['#009E73', '#D55E00', '#0072B2', '#CC79A7']; (3) title string "marimekko-basic · altair · anyplot.ai"; (4) save as f'plot-{THEME}.png' and f'plot-{THEME}.html'; (5) revenue mark_text layer inside segments; (6) configure_axis labelFontSize=18, titleFontSize=22, gridOpacity=0.10; (7) full configure_axis/configure_title/configure_legend theme-token wiring. The code that generated the correct images must be the code that exists in the committed file.

Verdict: REJECTED

MarkusNeusinger added a commit that referenced this pull request Apr 29, 2026
)

## Summary

The 3 AI-approved implementation PRs from today (#5476, #5480, #5481)
all hit `gh pr merge` failures with `the base branch policy prohibits
the merge`. Root cause: the branch ruleset on `main` requires three
status checks (`Run Linting`, `Run Tests`, `Run Frontend Tests`) — and
impl-PRs created by `impl-generate.yml` never get those checks.

## Why CI doesn't run on impl-PRs

`impl-generate.yml` (and `impl-repair.yml`, `impl-review.yml`) push
commits to PR branches using `GITHUB_TOKEN`. By GitHub's anti-recursion
design, pushes / PRs created with `GITHUB_TOKEN` do **not** trigger
downstream `pull_request` or `workflow_run` events. Verified across all
5 stuck PRs:

| PR | Branch | `Run Linting` ever ran? |
|----|--------|--------------------------|
| #5476 seaborn/marimekko-basic | yes (once, on a 04-27 impl-repair
commit; newer score commits invalidated it) |
| #5480 altair/marimekko-basic | no |
| #5481 letsplot/marimekko-basic | no |
| #5483 plotnine/marimekko-basic | no |
| #5486 plotly/line-basic | no |

So the merge is gated on a check that structurally cannot complete.

## The fix

Add `--admin` to the `gh pr merge` call inside `impl-merge.yml`. This
lets the pipeline complete autonomously without weakening main's
protection for human PRs.

```diff
+            # --admin bypasses the branch ruleset's required-status-check
+            # gate. Required because impl-generate.yml pushes via GITHUB_TOKEN,
+            # which by GitHub's anti-recursion design does not trigger
+            # downstream CI workflows (Run Linting / Run Tests / Run Frontend
+            # Tests), so impl PRs never get those checks. The pipeline already
+            # gates merge behind the AI quality review threshold.
             if gh pr merge "$PR_NUM" \
               --repo "$REPOSITORY" \
               --squash \
+              --admin \
               --delete-branch; then
```

The merge is still gated by:
- AI quality threshold (cascading 90 / 80 / 70 / 60 / 50 across initial
review + 4 repair attempts)
- `impl-merge.yml`'s own pre-merge "Validate PR completeness" step
- The label-based trigger requiring `ai-approved`

So `--admin` only bypasses the structurally-missing CI artifact, not the
substantive review gates.

## Considered alternative

Push from `impl-generate` / `impl-repair` / `impl-review` via a PAT
instead of `GITHUB_TOKEN` so CI triggers naturally. Cleaner long-term
but needs a maintained secret and a broader review of which workflows
touch which branches; deferred.

## Test plan

- [ ] After merge, dispatch `impl-merge.yml` (or trust the `ai-approved`
label trigger) for the 3 stuck approved PRs (#5476, #5480, #5481)
- [ ] Verify merge succeeds without retries on attempt 1
- [ ] Verify post-merge: metadata file created, GCS staging→production
promotion done, `impl:{library}:done` label on parent issue

🤖 Generated with [Claude Code](https://claude.com/claude-code)
MarkusNeusinger added a commit that referenced this pull request Apr 29, 2026
…et (#5523)

## Summary

Follow-up to #5521 (which added `--admin` to `gh pr merge`). That change
alone wasn't enough — verified just now: 3 dispatched merges (#5476,
#5480, #5481) all failed identically with:

```
GraphQL: Repository rule violations found
3 of 3 required status checks are expected.
(mergePullRequest)
```

## Why --admin alone didn't work

The `main` ruleset's bypass list contains only `RepositoryRole admin`
(mode: `pull_request`). Default `GITHUB_TOKEN` runs as
`github-actions[bot]` with `write` role — not admin — so the API rejects
the bypass.

```bash
gh api repos/MarkusNeusinger/anyplot/rulesets/10578859 --jq '.bypass_actors'
# [{"actor_id":5,"actor_type":"RepositoryRole","bypass_mode":"pull_request"}]
```

## The fix

Route **only the merge step** through a repo-admin PAT (`ADMIN_TOKEN`).
All other steps in `impl-merge.yml` and the rest of the impl-* workflows
keep using `GITHUB_TOKEN`. Bypass scope is therefore exactly one step,
not the whole pipeline.

```diff
       - name: Merge PR to main (with retry)
         if: steps.check.outputs.should_run == 'true'
         env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GH_TOKEN: ${{ secrets.ADMIN_TOKEN || secrets.GITHUB_TOKEN }}
           PR_NUM: ${{ steps.check.outputs.pr_number }}
           REPOSITORY: ${{ github.repository }}
+          HAS_ADMIN_TOKEN: ${{ secrets.ADMIN_TOKEN != '' }}
         run: |
+          if [ "$HAS_ADMIN_TOKEN" != "true" ]; then
+            echo "::warning::ADMIN_TOKEN secret is not set..."
+          fi
```

The fallback `secrets.ADMIN_TOKEN || secrets.GITHUB_TOKEN` and the
warning preserve the previous behavior if `ADMIN_TOKEN` isn't set yet —
workflow still runs, fails with the same ruleset error as before, but
the log says clearly what's missing instead of an opaque auth error.

## Required after merge

1. **Create PAT**: Settings → Developer settings → Personal access
tokens → Fine-grained
   - Repository: `anyplot`
   - Permissions:
     - Contents: Read+Write
     - Pull requests: Read+Write
     - Administration: Read+Write
     - Metadata: Read
2. **Set secret**: Settings → Secrets and variables → Actions → New
repository secret
   - Name: `ADMIN_TOKEN`
   - Value: the PAT

## Considered alternatives

| Option | Verdict |
|--------|---------|
| Add `github-actions[bot]` as bypass actor on ruleset | broader blast
radius — *every* workflow run could bypass main |
| Remove the 3 required checks from ruleset | weakens protection for
human PRs too |
| Push from impl-generate via PAT so CI triggers naturally | cleanest
semantically but needs PAT in 3 workflows + same maintenance overhead |
| **Scope PAT to merge step only (this PR)** | smallest blast radius,
matches the actual permission gap |

## Test plan

- [ ] Merge this PR
- [ ] Create the fine-grained PAT and add as `ADMIN_TOKEN` repo secret
- [ ] Re-dispatch `impl-merge.yml` for the 3 stuck approved PRs (#5476
seaborn, #5480 altair, #5481 letsplot)
- [ ] Verify each merges successfully on attempt 1 (no ruleset error in
run log)
- [ ] Verify metadata file created, GCS staging→production promotion
done, parent issue gets `impl:{library}:done` label

🤖 Generated with [Claude Code](https://claude.com/claude-code)
@MarkusNeusinger MarkusNeusinger merged commit b63818f into main Apr 29, 2026
3 checks passed
@MarkusNeusinger MarkusNeusinger deleted the implementation/marimekko-basic/altair branch April 29, 2026 11:20
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