Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.
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See how to installReview the instructions the agent will receive before you install.
Create the final C5 snapshot consumed by merge and later audit.
Despite the historical Skill name, the current implementation is not an autonomous rewrite loop. It reads the final H3 files, assigns bounded structural move labels, writes a compact consistency contract, refreshes section hashes, and fails when required prose is absent or too thin.
section writing
-> style and numeric hygiene
-> logic polish
-> paragraph compaction
-> argument snapshot
-> optional transitions
-> merge
Running the snapshot after every section mutator prevents the ledger and manifest from describing an earlier draft.
sections/outline/outline.ymlqueries.mdoutput/PARAGRAPH_CURATION_REPORT.mdoutput/ARGUMENT_SELFLOOP_TODO.mdoutput/SECTION_ARGUMENT_SUMMARIES.jsonloutput/ARGUMENT_SKELETON.mdsections/sections_manifest.jsonlSECTION_ARGUMENT_SUMMARIES.jsonl contains one record per expected H3 and one
record per paragraph. Current move labels are deterministic signals drawn from:
setup, thesis, contrast, evidence, evaluation, limitation, synthesis, takeaway
They make section shape inspectable; they do not prove that an argument is
scientifically valid. ARGUMENT_SKELETON.md contains a
## Consistency Contract and a chapter-level map. It is a writer-facing audit
artifact, never reader-facing paper content.
ARGUMENT_SKELETON.md contains ## Consistency Contract;sections_manifest.jsonl carries current bytes and sha256 values.On FAIL, inspect ARGUMENT_SELFLOOP_TODO.md and route the named section back to
its writer or evidence owner. This Skill does not apply the repair itself.
uv run python .codex/skills/argument-selfloop/scripts/run.py \
--workspace workspaces/<name>
Optional runner fields are --unit-id, --inputs, --outputs, and
--checkpoint.
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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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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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Retrieve arXiv paper metadata with keyword queries or import an offline arXiv export, and save results as JSONL (`papers/papers_raw.jsonl`).
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