Use when `paper-review` has claims plus evidence gaps and needs the final referee-style report.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
paper-review pipeline 的最后阶段（C3），已有 output/CLAIMS.md + output/MISSING_EVIDENCE.md（以及可选 novelty matrix）。Transforms review evidence artifacts into the final paper-review deliverable.
Required:
output/CLAIMS.mdoutput/MISSING_EVIDENCE.mdOptional:
output/NOVELTY_MATRIX.mdDECISIONS.mdoutput/REVIEW.mdThe review must expose stable sections:
### Summary### Novelty### Soundness### Clarity### Impact### Major Concerns### Minor Comments### RecommendationEvery major concern must cite its claim_id or gap_id. This is the
traceability interface consumed by the paper-review scorecard.
scripts/run.py should:
It should not re-parse the manuscript from scratch or perform retrieval.
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Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`.
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Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning.
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Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of generic summaries.
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Curate reader-facing survey tables for the Appendix (clean layout + high information density), using only in-scope evidence and existing citation keys.
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Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.
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Audit one research Workspace for declared Unit outputs and Pipeline target Artifacts, writing `output/CONTRACT_REPORT.md`; use for mid-Run coverage snapshots or final delivery completeness, not deep provenance integrity.
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