Use when a tutorial concept DAG exists and the run needs an ordered teaching plan with objectives and outputs.
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See how to installReview the instructions the agent will receive before you install.
outline/concept_graph.yml，需要把 concept DAG 收敛成模块顺序。Turns the concept graph into the teachable module sequence used by later tutorial stages.
outline/concept_graph.ymloutline/module_plan.ymlThe module plan must define:
modulesid, titleobjectivesconceptsoutputsrunning_example_stepsscripts/run.py should:
Keep graph traversal and clustering heuristics in shared tutorial tooling, not in the 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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