- Use English for structural headers (Role, Workflow, Constraints)
- Use Chinese for business logic and detailed explanations
- Consistent formatting across all 6 agents:
- paper-director.md
- paper-analyzer.md
- paper-image-extractor.md
- code-writer.md
- test-runner.md
- result-verifier.md
Root cause: test-runner was giving overly optimistic results due to:
1. Context bias - knew the implementation, tended to defend it
2. No actual visual comparison - just wrote 'ACCEPTABLE' without looking
3. No structural validation - accepted 35x scale differences as 'acceptable'
Solution:
- New result-verifier agent that performs blind visual comparison
- Strict pass/fail criteria for structural validation
- Updated test-runner to use result-verifier for each figure
- Clear guidelines: structural mismatches = FAIL, not ACCEPTABLE
Test result: verifier correctly identified Fig3 as FAIL with 7 specific issues:
- Wrong X-axis variable (channels vs power)
- Wrong Y-axis scale (5x difference)
- Wrong curve count (5 vs 4)
- etc.
The subagent was only reading text descriptions about images instead of
actually using the read tool on image files. This caused poor quality
reproductions based on guessed data rather than visual analysis.
Changes:
- Add CRITICAL instruction to use read tool on each image file
- Add Step 4: Verification step to compare generated vs original
- Add 'Extracting Data from Images' section with specific guidance
- Update guidelines to emphasize visual over textual extraction
- Allow scipy dependency for interpolation
Major changes:
- paper-image-extractor: Generate reference_plots.py for visual verification
- paper-director: Add image understanding checkpoint with side-by-side comparison
- paper-analyzer: Add data source labeling with reliability levels
- code-writer: Change from TDD to VDD (Verification-Driven Development)
- test-runner: Generate comparison reports with images and explanations
- verification skill: Add difference classification system
- code-generation skill: Emphasize result independence
Key principles:
- Code results are authoritative, paper values are references
- Differences are expected and documented, not bugs to fix
- Visual comparison prioritized over exact numerical match
- Tests verify sanity (shape, gradient, range), not exact values
