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chapter-skeleton

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UpdatedMay 27, 2026 at 16:37

Build a retrieval-informed chapter skeleton (`outline/chapter_skeleton.yml`) from taxonomy/core scope before stable H3 decomposition. **Trigger**: chapter skeleton, chapter-level outline, H2 skeleton, section-first survey, 章节骨架, 章级骨架. **Use when**: survey structure should stabilize chapter-level intent before subsection mapping and writing cards. **Skip if**: `outline/chapter_skeleton.yml` already exists and is refined. **Network**: none. **Guardrail**: NO PROSE; do not invent papers; keep output chapter-level only.

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WILLOSCAR

WILLOSCAR/research-units-pipeline-skills

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4 files

SKILL.md

readonly

name

chapter-skeleton

description

Chapter Skeleton

Load Order

Always read:

references/overview.md

Use scripts/run.py only for deterministic materialization:

read outline/taxonomy.yml for retrieval-informed topic structure
read GOAL.md when present for scope hints
emit outline/chapter_skeleton.yml
preserve existing non-placeholder user work

Inputs

outline/taxonomy.yml
Optional: GOAL.md

Outputs

outline/chapter_skeleton.yml

Asset contract

assets/output_contract.json

Script

Quick Start

python .codex/skills/chapter-skeleton/scripts/run.py --workspace <workspace_dir>

All Options

--workspace <dir>: workspace containing outline/taxonomy.yml
--unit-id <id>: optional harness metadata
--inputs <semicolon-separated>: optional override from UNITS.csv
--outputs <semicolon-separated>: optional output override; default is outline/chapter_skeleton.yml
--checkpoint <C*>: optional harness metadata

Examples

Generate the chapter skeleton after taxonomy:
- python .codex/skills/chapter-skeleton/scripts/run.py --workspace workspaces/<ws> --inputs 'outline/taxonomy.yml;GOAL.md' --outputs 'outline/chapter_skeleton.yml'

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agent-survey-corpus

WILLOSCAR/research-units-pipeline-skills

Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning. **Trigger**: agent survey corpus, ref corpus, download surveys, 学习综述写法, 下载 survey. **Use when**: you want to study how real agent surveys structure sections (6–8 H2), size subsections, and write evidence-backed comparisons. **Skip if**: you cannot download PDFs (no network) or you don't want local PDF files. **Network**: required. **Guardrail**: only download arXiv PDFs; store under `ref/` and keep large files out of git.

2026-05-30472

global-reviewer

WILLOSCAR/research-units-pipeline-skills

Global consistency review for survey drafts: terminology, cross-section coherence, and scope/citation hygiene. Writes `output/GLOBAL_REVIEW.md` and (optionally) applies safe edits to `output/DRAFT.md`. **Trigger**: global review, consistency check, coherence audit, 术语一致性, 全局回看, 章节呼应, 拷打 writer. **Use when**: Draft exists and you want a final evidence-first coherence pass before LaTeX/PDF. **Skip if**: You are still changing the outline/mapping/notes (do those first), or prose writing is not approved. **Network**: none. **Guardrail**: Do not invent facts or citations; do not add new citation keys; treat missing evidence as a failure signal.

2026-05-30472

literature-engineer

WILLOSCAR/research-units-pipeline-skills

Multi-route literature expansion + metadata normalization for evidence-first surveys. Produces a large candidate pool (`papers/papers_raw.jsonl`, target ≥1200) with stable IDs and provenance, ready for dedupe/rank + citation generation. **Trigger**: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance. **Use when**: 需要把候选文献扩充到 ≥1200 篇并补齐可追溯 meta（survey pipeline 的 Stage C1，写作前置 evidence）。 **Skip if**: 已经有高质量 `papers/papers_raw.jsonl`（≥1200 且每条都有稳定标识+来源记录）。 **Network**: 可离线（靠 imports）；雪崩/在线检索需要网络。 **Guardrail**: 不允许编造论文；每条记录必须带稳定标识（arXiv id / DOI / 可信 URL）和 provenance；不写 output/ prose。

2026-05-30472

pdf-text-extractor

WILLOSCAR/research-units-pipeline-skills

Download PDFs (when available) and extract plain text to support full-text evidence, writing `papers/fulltext_index.jsonl` and `papers/fulltext/*.txt`. **Trigger**: PDF download, fulltext, extract text, papers/pdfs, 全文抽取, 下载PDF. **Use when**: `queries.md` 设置 `evidence_mode: fulltext`（或你明确需要全文证据）并希望为 paper notes/claims 提供更强 evidence。 **Skip if**: `evidence_mode: abstract`（默认）；或你不希望进行下载/抽取（成本/权限/时间）。 **Network**: fulltext 下载通常需要网络（除非你手工提供 PDF 缓存在 `papers/pdfs/`）。 **Guardrail**: 缓存下载到 `papers/pdfs/`；默认不覆盖已有抽取文本（除非显式要求重抽）。

2026-05-30472

prose-writer

WILLOSCAR/research-units-pipeline-skills

Write `output/DRAFT.md` (or `output/SNAPSHOT.md`) from an approved outline and evidence packs, using only verified citation keys from `citations/ref.bib`. **Trigger**: write draft, prose writer, snapshot, survey writing, 写综述, 生成草稿, section-by-section drafting. **Use when**: structure is approved (`DECISIONS.md` has `Approve C2`) and evidence packs exist (`outline/subsection_briefs.jsonl`, `outline/evidence_drafts.jsonl`). **Skip if**: approvals are missing, or evidence packs are incomplete / scaffolded (missing-fields, TODO markers). **Network**: none. **Guardrail**: do not invent facts or citations; only cite keys present in `citations/ref.bib`; avoid pipeline-jargon leakage in final prose.

2026-05-30472

schema-normalizer

WILLOSCAR/research-units-pipeline-skills

Normalize cross-skill JSONL interfaces (ids + titles + citation key formats) so downstream skills do not rely on best-effort joins. **Trigger**: schema normalize, jsonl contract, interface drift, join drift, 字段不一致, schema 规范化. **Use when**: you have generated C2-C4 JSONL artifacts (outline/briefs/bindings/packs/anchors) and want deterministic, stable fields before self-loops/writing. **Skip if**: you are not using the survey pipelines, or the workspace already has a fresh PASS `output/SCHEMA_NORMALIZATION_REPORT.md` for the current artifacts. **Network**: none. **Guardrail**: NO PROSE; deterministic transforms only; do not invent evidence/claims; only fill missing ids/titles from `outline/outline.yml`.

2026-05-30472

name

chapter-skeleton

description

Chapter Skeleton

Load Order

Always read:

references/overview.md

Use scripts/run.py only for deterministic materialization:

read outline/taxonomy.yml for retrieval-informed topic structure
read GOAL.md when present for scope hints
emit outline/chapter_skeleton.yml
preserve existing non-placeholder user work

Inputs

outline/taxonomy.yml
Optional: GOAL.md

Outputs

outline/chapter_skeleton.yml

Asset contract

assets/output_contract.json

Script

Quick Start

python .codex/skills/chapter-skeleton/scripts/run.py --workspace <workspace_dir>

All Options

--workspace <dir>: workspace containing outline/taxonomy.yml
--unit-id <id>: optional harness metadata
--inputs <semicolon-separated>: optional override from UNITS.csv
--outputs <semicolon-separated>: optional output override; default is outline/chapter_skeleton.yml
--checkpoint <C*>: optional harness metadata

Examples

Generate the chapter skeleton after taxonomy:
- python .codex/skills/chapter-skeleton/scripts/run.py --workspace workspaces/<ws> --inputs 'outline/taxonomy.yml;GOAL.md' --outputs 'outline/chapter_skeleton.yml'