| name | subsection-briefs |
| description | Build per-subsection writing briefs (NO PROSE) so later drafting is driven by evidence and checkable comparison axes (not outline placeholders).
**Trigger**: subsection briefs, writing cards, intent cards, H3 briefs, scope_rule, axes, clusters, 写作意图卡, 小节卡片, 段落计划.
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Subsection Briefs
outline/subsection_briefs.refined.ok freezes reviewed briefs only while the marker is newer than the briefs, declared inputs, domain packs, and generator. Stale markers are removed before backed-up regeneration.
Build deterministic H3 brief cards from outline + mapping + paper notes.
Compatibility mode is active: this skill keeps the current outline/subsection_briefs.jsonl field contract and paragraph-plan shape while moving phrase/domain logic into references/ and assets/.
Quick Use
- Run
scripts/run.py as the deterministic materializer.
- Keep the output NO PROSE: subsection-scoped plans, axes, clusters, and bridge handles only.
- Preserve current downstream compatibility for
transition-weaver, writer-context-pack, and subsection-writer.
Load Order
Always read:
Read by task:
- If
thesis feels repetitive or copyable, read references/thesis_patterns.md.
- If
tension_statement is too generic, read references/tension_patterns.md.
- If axes are weak or domain-biased, read
references/axis_catalog_generic.md and references/axis_catalog_llm_agents.md.
- If transition handles feel bland, read
references/bridge_terms.md.
- For calibration, read
references/examples_good.md.
Machine-readable assets:
assets/phrase_packs/thesis_patterns.json
assets/phrase_packs/bridge_contrast.json
assets/domain_packs/generic.json
assets/domain_packs/llm_agents.json
assets/domain_packs/embodied_ai.json
assets/domain_packs/rag_evaluation.json
assets/domain_packs/text_to_image.json
The script loads these packs first; patch them before changing Python when the issue is phrasing, domain routing, axis inventory, cluster purity, or lexical bridge coverage.
Inputs
outline/outline.yml
outline/mapping.tsv
papers/paper_notes.jsonl
- Optional:
GOAL.md
- Optional:
outline/claim_evidence_matrix.md
Output
outline/subsection_briefs.jsonl
Required record shape remains compatibility-preserving:
- identity:
sub_id, title, section_id, section_title
- planning core:
rq, thesis, scope_rule, axes, bridge_terms, contrast_hook, tension_statement
- evidence hooks:
evaluation_anchor_minimal, required_evidence_fields, clusters
- execution plan:
paragraph_plan, evidence_level_summary, generated_at
What run.py Should Do
- Read outline, mapping, and notes.
- Normalize subsection seeds from outline bullets.
- Load thesis/tension/domain-axis packs from
assets/.
- Produce stable JSONL records with the existing contract.
What run.py Should Not Do
- Do not invent papers, citations, or claims.
- Do not emit reader-facing narrative prose.
- Do not hardcode domain-specific sentence templates when an asset pack can hold them.
Block / Reroute
- If outline, mapping, or notes are missing, stop.
- If evidence is thin, keep
thesis/tension_statement conservative and let downstream evidence skills strengthen the subsection.
- If contrast clusters collapse into overlapping paper pools, reroute before writing: after removing bridge papers, each side should still retain at least 2 unique papers.
- Use
bridge_terms to surface concrete lexical handles that later evidence/ranking stages can still match (OOD, sim-to-real, world model, failure detector, specific benchmark families), not only generic axis names.
- Prefer domain-pack
cluster_rules over ad-hoc bootstrap overlaps when the mapped set is already large enough to support disjoint clusters.
- Do not “fix” thin evidence by inventing more specific axes or stronger claims.
Execution notes
When running in compatibility mode, scripts/run.py currently reads:
outline/outline.yml for section/subsection structure
outline/mapping.tsv for paper-to-subsection coverage
papers/paper_notes.jsonl for structured evidence
GOAL.md for topic/domain cues
outline/claim_evidence_matrix.md as optional supporting context when present
Script
Quick Start
uv run python .codex/skills/subsection-briefs/scripts/run.py --workspace <workspace>
All Options
--workspace <dir>
--unit-id <id>
--inputs <a;b;...>
--outputs <a;b;...>
--checkpoint <C*>
Examples
uv run python .codex/skills/subsection-briefs/scripts/run.py --workspace <workspace>
Troubleshooting
- If the wrong domain pack is selected, inspect
GOAL.md and the asset packs before changing the script.
- If briefs sound too generic, adjust the phrase/domain packs instead of adding more Python prose.
- If
papers/paper_notes.jsonl is thin, reroute to note extraction rather than inventing axes.