Use when module planning and source coverage are done and the run needs writer-ready per-module packs.
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See how to installReview the instructions the agent will receive before you install.
source-tutorial 的 C2，已有 module plan + source coverage，需要组织成稳定写作输入。Combines module structure, source coverage, and source snippets into one deterministic JSONL pack per module.
outline/module_plan.ymloutline/source_coverage.jsonlsources/provenance.jsonloutline/tutorial_context_packs.jsonlEach pack must include:
module_idtitleobjectivecore_conceptsexercise_seedsource_snippetsscripts/run.py should:
Keep snippet ranking and pack synthesis in shared tutorial tooling rather than in the thin wrapper script.
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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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