菜单
Programmatic validation of figure data and generation code. Since LLMs cannot interpret rendered images, this skill ensures figure correctness by analysing the underlying data and plotting code — catching identical-value plots, missing labels, broken references, and other common defects.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Skill providing the exact structured YAML review format that all reviewers must use. Enables automated parsing, scoring, action-item extraction, and convergence detection.
Skill for enforcing review blinding — ensuring reviewers cannot see each other's reports, managing worktree isolation, handling optional author anonymization, and controlling unblinding at synthesis.
Skill for determining whether the review process has converged (accept), needs another round (iterate), or has reached diminishing returns (exhausted).
Skill for managing the full peer review lifecycle: intake validation, reviewer dispatch, review collection, editorial synthesis, revision cycles, re-review, and convergence determination.
Skill for venue-calibrated scoring, score weighting formulas, decision rules, anti-bias mechanisms, and score interpretation across different venue tiers.
Skill for managing review state persistence — the .review/ directory structure, state.yaml lifecycle tracking, artifact hashing, round management, and final output generation.
| name | figure-audit |
| description | Programmatic validation of figure data and generation code. Since LLMs cannot interpret rendered images, this skill ensures figure correctness by analysing the underlying data and plotting code — catching identical-value plots, missing labels, broken references, and other common defects. |
Use this skill whenever figures are generated or modified — typically after the Revision Agent produces new plots, and before the EIC finalises the paper.
LLMs cannot see rendered figures. A plot where every bar is the same height, an
axis that shows NaN, or a \includegraphics pointing at a missing file will all
pass unnoticed in text-only review. This audit catches those problems
programmatically so the agents never need to interpret pixels.
| Phase | Trigger |
|---|---|
| Post-revision | Revision agent finished generating/updating figures |
| Pre-finalize | Before EIC declares Accept — final quality gate |
| Re-audit | After any code change that touches figures/, experiments/, or plotting scripts |
python scripts/audit-figures.py <submission_dir> --out <submission_dir>/.review/artifacts/figure-audit.yaml
Exit codes:
0 — all clear1 — warnings only (non-blocking, note in review)2 — critical issues found (blocks acceptance)savefig with DPI < 150 → warning (below publication quality)\includegraphics{...} pointing to missing file → critical\ref{fig:X} without matching \label{fig:X} → warning\label{fig:X} never referenced (orphan) → warningfigure_audit:
submission: "my-paper"
status: "fail" | "warning" | "pass"
critical_count: 2
warning_count: 3
critical_issues:
- "results.csv:accuracy: all 5 values are identical (0.5)"
- "paper.tex: \includegraphics{figures/ablation.pdf} — file not found"
warnings:
- "plot_results.py: saved figure but no ylabel found"
- "plot_results.py: uses random data without setting a seed"
- "paper.tex: \label{fig:overview} is never referenced (orphan figure)"
After generating or updating any figure, run the audit and fix all critical issues before committing. Add to the completion checklist:
16. ✅ Figure audit passed (python scripts/audit-figures.py <submission>)
Before declaring Accept, run the audit as a final gate. If critical issues remain, dispatch the Revision Agent to fix them before finalising.
When reviewing figures, reference the audit report in
.review/artifacts/figure-audit.yaml for objective data quality signals
alongside your subjective assessment of presentation quality.