Run a tutorial-specific quality gate and write a PASS/FAIL report for the final tutorial deliverable.
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See how to installReview the instructions the agent will receive before you install.
source-tutorial 的 C3，已经有 output/TUTORIAL.md，想在交付前确认它满足教程合同而不是普通长文。Goal: converge the tutorial deliverable to a stable teaching-quality bar.
The gate should compare output/TUTORIAL.md against the intended module shape from outline/module_plan.yml, rather than acting like a generic prose checker.
output/TUTORIAL.mdoutline/module_plan.ymloutput/TUTORIAL_SELFLOOP_TODO.mduv run python .codex/skills/tutorial-selfloop/scripts/run.py --workspace <workspace>--workspace <dir> (required)--unit-id <U###>--inputs <semicolon-separated>--outputs <semicolon-separated>--checkpoint <C#>uv run python .codex/skills/tutorial-selfloop/scripts/run.py --workspace <workspace>No reviews yet. Tell others what you thought.
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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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