Rewrite `outline/claim_evidence_matrix.md` as a projection/index of evidence packs (NO PROSE), so claims/axes are driven by `outline/evidence_drafts.jsonl` rather than outline placeholders.
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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。
Purpose: make outline/claim_evidence_matrix.md a navigation/index layer that is driven by evidence packs (not by outline bullets).
Key property: keep the legacy, gate-checked format so downstream QA can rely on stable markers:
- Claim: (one per subsection) - Evidence: (>=2 per subsection)outline/subsection_briefs.jsonloutline/evidence_drafts.jsonlcitations/ref.biboutline/claim_evidence_matrix.md..., unicode ellipsis (…), TODO, (placeholder), or <!-- SCAFFOLD -->.uv run python .codex/skills/claim-matrix-rewriter/scripts/run.py --helpuv run python .codex/skills/claim-matrix-rewriter/scripts/run.py --workspace <workspace>uv run python .codex/skills/claim-matrix-rewriter/scripts/run.py --helpuv run python .codex/skills/claim-matrix-rewriter/scripts/run.py --workspace <workspace>--help.outline/subsection_briefs.jsonl, outline/evidence_drafts.jsonl, citations/ref.bib.outline/subsection_briefs.jsonl and outline/evidence_drafts.jsonl exist and blocking_missing is empty.citations/ref.bib exists and contains the cited keys.uv run python .codex/skills/claim-matrix-rewriter/scripts/run.py --workspace <workspace>citations/ref.bibFix:
citation-verifier (or fix the upstream notes) so every referenced key exists before rewriting.blocking_missingFix:
paper-notes / evidence-draft) first; the matrix should not “paper over” missing evidence.还没有评价。欢迎分享你的使用感受。
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还没有评论。来写第一条吧。
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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