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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).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(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(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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.

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같은 저장소의 스킬

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`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

WILLOSCAR/research-units-pipeline-skills5152026년 10월 5일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

WILLOSCAR/research-units-pipeline-skills5152026년 10월 5일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

WILLOSCAR/research-units-pipeline-skills5152026년 10월 5일 업데이트

Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

WILLOSCAR/research-units-pipeline-skills5152026년 10월 5일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

WILLOSCAR/research-units-pipeline-skills5152026년 10월 5일 업데이트

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