Use when a `source-tutorial` workspace has ingested sources and needs a grounded tutorial contract before structure planning.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
source-tutorial 的 C2，需要根据 sources/index.jsonl / sources/provenance.jsonl 锁定 audience、prerequisites、learning objectives、source scope 和 running example policy。Builds the C2 tutorial contract from the ingested source corpus, not from a bare topic prompt.
sources/index.jsonlsources/provenance.jsonlGOAL.mdDECISIONS.mdoutput/TUTORIAL_SPEC.mdThe spec must include:
## Audience## Prerequisites## Learning objectives## Non-goals## Source scope## Running example policy## Delivery shapeIt should also embed machine-readable structured data for downstream deterministic planning.
scripts/run.py should:
Keep phrase extraction, concept selection, and source matching in shared tutorial tooling, not in the thin skill wrapper.
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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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