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subsection-briefs

Build per-subsection writing briefs (NO PROSE) so later drafting is driven by evidence and checkable comparison axes (not outline placeholders).

インストール方法を見る

含まれるファイル(16)

  • SKILL.md4.9 KB
  • assets/domain_packs/embodied_ai.json22.3 KB
  • assets/domain_packs/generic.json4.7 KB
  • assets/domain_packs/llm_agents.json6.5 KB
  • assets/domain_packs/rag_evaluation.json10.9 KB
  • assets/domain_packs/text_to_image.json3.9 KB
  • assets/phrase_packs/bridge_contrast.json168 B
  • assets/phrase_packs/thesis_patterns.json1.5 KB
  • references/axis_catalog_generic.md817 B
  • references/axis_catalog_llm_agents.md1.1 KB
  • references/bridge_terms.md877 B
  • references/examples_good.md1.3 KB
  • references/overview.md1.4 KB
  • references/tension_patterns.md961 B
  • references/thesis_patterns.md1.0 KB
  • scripts/run.py56.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Subsection Briefs

Triggers & routing

  • 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.

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:

  • references/overview.md

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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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