Build a retrieval-informed chapter skeleton (`outline/chapter_skeleton.yml`) from taxonomy/core scope before stable H3 decomposition.
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Create outline/chapter_skeleton.refined.ok only after reviewing a manually refined skeleton. A changed taxonomy, goal, or generator invalidates the marker; reruns then back up and rebuild the skeleton.
Always read:
references/overview.mdUse scripts/run.py only for deterministic materialization:
outline/taxonomy.yml for retrieval-informed topic structureGOAL.md when present for scope hintsoutline/chapter_skeleton.ymloutline/taxonomy.ymlGOAL.mdoutline/chapter_skeleton.ymlassets/output_contract.jsonuv run python .codex/skills/chapter-skeleton/scripts/run.py --workspace <workspace>--workspace <dir>: workspace containing outline/taxonomy.yml--unit-id <id>: optional harness metadata--inputs <semicolon-separated>: optional override from UNITS.csv--outputs <semicolon-separated>: optional output override; default is outline/chapter_skeleton.yml--checkpoint <C*>: optional harness metadatauv run python .codex/skills/chapter-skeleton/scripts/run.py --workspace <workspace> --inputs 'outline/taxonomy.yml;GOAL.md' --outputs 'outline/chapter_skeleton.yml'还没有评价。欢迎分享你的使用感受。
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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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