一键在 Manus 中运行任何 Skill

claims-extractor

星标472

分支36

更新时间2026年4月14日 11:50

Use when a review workspace has manuscript text and needs a traceable claim ledger. **Trigger**: claims extractor, extract claims, contributions, assumptions, peer review, 审稿, 主张提取. **Use when**: 审稿/评审或 evidence audit，需要把主张列表落盘并可追溯到原文位置（section/page/quote）。 **Skip if**: 没有可用的稿件/全文（例如缺少 `output/PAPER.md` 或等价文本）。 **Network**: none. **Guardrail**: 每条 claim 必须带可定位的 source pointer；区分 empirical vs conceptual claims。

安装

用 Codex 或 Claude 帮你安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。

在 Manus 中运行

来源

WILLOSCAR

WILLOSCAR/research-units-pipeline-skills

打开 GitHub 仓库查看创作者相关仓库

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在 Manus 中运行

Claims Extractor

Transforms manuscript text into a traceable claim ledger for paper-review.

Input

output/PAPER.md

Output

output/CLAIMS.md

Contract

Each claim block must include:

claim text
type: empirical or conceptual
scope
source pointer back into output/PAPER.md

Script boundary

scripts/run.py should:

detect claim-like sentences
normalize them into stable claim blocks
attach source pointers
separate empirical and conceptual claims

Keep parsing and ranking heuristics in shared review tooling, not in the skill script.

Acceptance

output/CLAIMS.md exists
every claim has a source pointer
empirical and conceptual claims are separated

Non-goals

judging whether a claim is good
generating evidence gaps
writing review prose

同仓库更多 Skills

同仓库

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

claims-extractor

description

Claims Extractor

Transforms manuscript text into a traceable claim ledger for paper-review.

Input

output/PAPER.md

Output

output/CLAIMS.md

Contract

Each claim block must include:

claim text
type: empirical or conceptual
scope
source pointer back into output/PAPER.md

Script boundary

scripts/run.py should:

detect claim-like sentences
normalize them into stable claim blocks
attach source pointers
separate empirical and conceptual claims

Keep parsing and ranking heuristics in shared review tooling, not in the skill script.

Acceptance

output/CLAIMS.md exists
every claim has a source pointer
empirical and conceptual claims are separated

Non-goals

judging whether a claim is good
generating evidence gaps
writing review prose