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agent-survey-corpus

Name: Agent Survey Corpus
Author: WILLOSCAR

// 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.

Manusで実行

$ git log --oneline --stat

stars:449

forks:31

updated:2026年5月30日 12:16

ファイルエクスプローラー

2 ファイル

SKILL.md

readonly

name

agent-survey-corpus

description

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.

Agent Survey Corpus (arXiv PDFs → text extracts)

Goal: create a small, local reference library so you can learn from real agent surveys when refining:

C2 outline structure (paper-like sectioning)
C4 tables/claims organization
C5 writing style and density

This is intentionally not part of the pipeline; it is an optional, repo-level toolkit.

Inputs

ref/agent-surveys/arxiv_ids.txt

Outputs

ref/agent-surveys/pdfs/
ref/agent-surveys/text/
ref/agent-surveys/STYLE_REPORT.md (tracked; auto-generated summary)

Workflow

Edit ref/agent-surveys/arxiv_ids.txt (one arXiv id per line).
Run the downloader to fetch PDFs and extract the first N pages to text.
Skim the extracted text under ref/agent-surveys/text/:
- look at section counts (H2), subsection granularity (H3), and how they transition between chapters.
- identify repeated rhetorical patterns you want the pipeline writer to imitate.

Script

Quick Start

python .codex/skills/agent-survey-corpus/scripts/run.py --help
python .codex/skills/agent-survey-corpus/scripts/run.py --workspace . --max-pages 20

All Options

--workspace <dir> (use . to write into repo root)
--inputs <semicolon-separated> (default: ref/agent-surveys/arxiv_ids.txt)
--max-pages <N> (default: 20)
--sleep <seconds> (default: 1.0)
--overwrite (re-download + re-extract)

Examples

Download/extract into repo root ref/:
- python .codex/skills/agent-survey-corpus/scripts/run.py --workspace . --max-pages 20
Download/extract into a specific folder (treated as workspace root):
- python .codex/skills/agent-survey-corpus/scripts/run.py --workspace /tmp/surveys --max-pages 30

Troubleshooting

Download fails / timeout: rerun with a larger --sleep, or try fewer ids.
Text extract is empty: the PDF may be scanned; try another survey or increase --max-pages.
Files showing up in git status: PDFs/text are ignored via .gitignore (ref/**/pdfs/, ref/**/text/).

related-skills.json

同じリポジトリ

global-reviewer.md

from "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.

2026-05-30449

literature-engineer.md

from "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。

2026-05-30449

pdf-text-extractor.md

from "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）。

2026-05-30449

prose-writer.md

from "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.

2026-05-30449

schema-normalizer.md

from "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`.

2026-05-30449

writer-selfloop.md

from "WILLOSCAR/research-units-pipeline-skills"

Writing self-loop for surveys: run the strict section-quality gate, then rewrite only the failing `sections/*.md` files until the report is PASS. **Trigger**: writer self-loop, writing loop, quality gate loop, rewrite failing sections, 自循环, 反复改到 PASS. **Use when**: per-section files exist but C5 is FAIL/BLOCKED (thin sections, missing leads/front matter, citation-scope violations, generator voice). **Skip if**: you are still pre-C2 (NO PROSE), or evidence packs are incomplete (fix C3/C4 first). **Network**: none. **Guardrail**: do not invent facts; only use citation keys present in `citations/ref.bib`; keep citations in-scope per `outline/evidence_bindings.jsonl`; do not add/remove citation keys during rewrites.

2026-05-30449

package.json

"author": "WILLOSCAR"

"repository": "WILLOSCAR/research-units-pipeline-skills"

GitHub リポジトリを開く Creator のリポジトリを見る

$ install --global

$ download --local

Manusで実行

$ useful --forSOC

テクニカルライター芸術・デザイン・エンタメ・スポーツ・メディア職27-3042L4

name

agent-survey-corpus

description

Agent Survey Corpus (arXiv PDFs → text extracts)

Goal: create a small, local reference library so you can learn from real agent surveys when refining:

C2 outline structure (paper-like sectioning)
C4 tables/claims organization
C5 writing style and density

This is intentionally not part of the pipeline; it is an optional, repo-level toolkit.

Inputs

ref/agent-surveys/arxiv_ids.txt

Outputs

ref/agent-surveys/pdfs/
ref/agent-surveys/text/
ref/agent-surveys/STYLE_REPORT.md (tracked; auto-generated summary)

Workflow

Edit ref/agent-surveys/arxiv_ids.txt (one arXiv id per line).
Run the downloader to fetch PDFs and extract the first N pages to text.
Skim the extracted text under ref/agent-surveys/text/:
- look at section counts (H2), subsection granularity (H3), and how they transition between chapters.
- identify repeated rhetorical patterns you want the pipeline writer to imitate.

Script

Quick Start

python .codex/skills/agent-survey-corpus/scripts/run.py --help
python .codex/skills/agent-survey-corpus/scripts/run.py --workspace . --max-pages 20

All Options

--workspace <dir> (use . to write into repo root)
--inputs <semicolon-separated> (default: ref/agent-surveys/arxiv_ids.txt)
--max-pages <N> (default: 20)
--sleep <seconds> (default: 1.0)
--overwrite (re-download + re-extract)

Examples

Download/extract into repo root ref/:
- python .codex/skills/agent-survey-corpus/scripts/run.py --workspace . --max-pages 20
Download/extract into a specific folder (treated as workspace root):
- python .codex/skills/agent-survey-corpus/scripts/run.py --workspace /tmp/surveys --max-pages 30

Troubleshooting

Download fails / timeout: rerun with a larger --sleep, or try fewer ids.
Text extract is empty: the PDF may be scanned; try another survey or increase --max-pages.
Files showing up in git status: PDFs/text are ignored via .gitignore (ref/**/pdfs/, ref/**/text/).

agent-survey-corpus

Agent Survey Corpus (arXiv PDFs → text extracts)

Inputs

Outputs

Workflow

Script

Quick Start

All Options

Examples

Troubleshooting

このリポジトリの他の Skills

このリポジトリの他の Skills

Agent Survey Corpus (arXiv PDFs → text extracts)

Inputs

Outputs

Workflow

Script

Quick Start

All Options

Examples

Troubleshooting