Deterministically compact final H3 bodies to the active delivery profile's paragraph budget without deleting prose or changing citation-block order.
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Compact paragraph boundaries without performing a new semantic rewrite.
This is a deterministic convergence step. It prevents a long drafting loop from leaving sections outside the delivery profile, while keeping the original prose and evidence recoverable. It does not rank paragraphs, generate alternatives, replace evidence, or decide which claims are important.
sections/S<subsection-id>.mdqueries.md for draft_profileOnly H3 files are modified. Front matter (S1.md, S2.md), H2 lead files, and
global sections are not touched.
| Profile | Paragraphs per H3 |
|---|---|
course_paper | 5-7 |
survey | 10-12 |
deep | 11-13 |
The script first joins short adjacent body paragraphs while respecting the profile floor. If a section remains over budget, it repeatedly joins the shortest eligible adjacent pair. It never truncates a paragraph or drops a middle block.
sections/output/PARAGRAPH_CURATION_REPORT.mdsections/paragraphs_curated.refined.ok on PASSThe report records the active profile and before/after paragraph counts for each H3. PASS requires every H3 to be within budget and the sequence of citation blocks to be unchanged. On FAIL, the marker is removed.
subsection-writer or the relevant
evidence unit. Compaction must not invent padding.writer-selfloop or
section-logic-polisher. This Skill changes paragraph boundaries, not ideas.argument-selfloop after this Skill so the argument ledger and section
manifest describe the final section content.uv run python .codex/skills/paragraph-curator/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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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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