Agent skill
claims-extractor
Extract key claims, contributions, and assumptions from a paper/manuscript into `output/CLAIMS.md` with traceability to source locations. **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。
Install this agent skill to your Project
npx add-skill https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/claims-extractor
SKILL.md
Claims Extractor (peer review)
Goal: turn a manuscript into an auditable list of claims that downstream skills can check.
Inputs
Required:
output/PAPER.md(or equivalent plain-text manuscript)
Optional:
DECISIONS.md(review scope or constraints)
Outputs
output/CLAIMS.md
Output format (recommended)
For each claim, include at minimum:
Claim: one sentenceType:empirical|conceptualScope: what the claim applies to / what it does not apply toSource: a locatable pointer intooutput/PAPER.md(section + page/figure/table + a short quote)
Workflow
- If
DECISIONS.mdexists, apply any review scope/format constraints. - Read the manuscript (
output/PAPER.md) end-to-end (at least abstract + intro + method + experiments + limitations). - Extract:
- primary contributions (what is new)
- key claims (what is asserted)
- assumptions (what must be true for claims to hold)
- Normalize each item into one sentence.
- Attach a source pointer for every item.
- Split into two sections:
- Empirical claims (must be backed by experiments/data)
- Conceptual claims (must be backed by argument/definition)
Definition of Done
-
output/CLAIMS.mdexists. - Every claim has a source pointer that can be located in
output/PAPER.md. - Empirical vs conceptual claims are clearly separated.
Troubleshooting
Issue: the paper is only a PDF or HTML
Fix:
- Convert/extract it into a plain-text
output/PAPER.mdfirst (even rough extraction is OK), then run claim extraction.
Issue: claims are vague (“significant”, “better”, “state-of-the-art”)
Fix:
- Rewrite each claim to include the measurable dimension (metric/dataset/baseline) or mark it as “underspecified” with a note.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
thesis-compile-review
对中文毕业论文进行编译、warning 分级、模板模式检查、数据与引用复查,并把问题回写成可继续迭代的 review checklist。 **Trigger**: 毕业论文编译检查, thesis compile review, warning 分级, 终稿复查, main.pdf 检查. **Use when**: 论文已经回写到 TeX 交付层,需要确认是否真正达到“可提交”的质量,而不是只做到能编译。 **Skip if**: 还处于中间层重构阶段,`chapters/*.tex` 尚未形成稳定交付稿。 **Network**: none. **Guardrail**: 不在这里重构章节主线;如果发现结构问题,明确回退到上游修复。
front-matter-writer
Write the survey's front matter files (Abstract, Introduction, Related Work, Discussion, Conclusion) in paper voice, with high citation density and a single evidence-policy paragraph. **Trigger**: front matter writer, introduction writer, related work writer, abstract writer, discussion writer, conclusion writer, 引言, 相关工作, 摘要, 讨论, 结论. **Use when**: you are in C5 (prose allowed) and need the paper-like shell to stop the draft reading like stitched subsections. **Skip if**: `Approve C2` is missing in `DECISIONS.md`, or `citations/ref.bib` is missing. **Network**: none. **Guardrail**: no invented facts/citations; no pipeline jargon in final prose; no repeated evidence disclaimers; only use keys present in `citations/ref.bib`.
thesis-question-list
维护中文毕业论文的 `codex_md/question_list.md`:把本轮问题、边界、优先级、协作方案和验收口径结构化,作为整条 thesis pipeline 的控制面。 **Trigger**: 毕业论文问题清单, thesis question list, 论文修改清单, 本轮目标, 结构问题梳理, review问题整理. **Use when**: 你已经有一批材料或上一轮 review 结果,需要明确这一轮到底修什么、不修什么,并给后续重构与编译复查提供统一入口。 **Skip if**: 当前只是在做一次性局部措辞修改,且没有形成新一轮结构/证据/编译问题。 **Network**: none. **Guardrail**: 不在这里写正文;不把问题单写成长篇散文;每条问题必须可执行、可验收。
novelty-matrix
Create a novelty/prior-work matrix comparing the submission’s contributions against related work (overlaps vs deltas). **Trigger**: novelty matrix, prior-work matrix, overlap/delta, 相关工作对比, 新颖性矩阵. **Use when**: peer review 中评估 novelty/positioning,需要把贡献与相关工作逐项对齐并写出差异点证据。 **Skip if**: 缺少 claims(先跑 `claims-extractor`)或你不打算做新颖性定位分析。 **Network**: none (retrieval of additional related work is out-of-scope unless provided). **Guardrail**: 明确 overlap 与 delta;尽量给出可追溯证据来源(来自稿件/引用/作者陈述)。
protocol-writer
Write a systematic review protocol into `output/PROTOCOL.md` (databases, queries, inclusion/exclusion, time window, extraction fields). **Trigger**: protocol, PRISMA, systematic review, inclusion/exclusion, 检索式, 纳入排除. **Use when**: systematic review pipeline 的起点(C1),需要先锁定 protocol 再开始 screening/extraction。 **Skip if**: 不是做 systematic review(或 protocol 已经锁定且不允许修改)。 **Network**: none. **Guardrail**: protocol 必须包含可执行的检索与筛选规则;需要 HUMAN 签字后才能进入 screening。
rubric-writer
Write a rubric-based peer review report (`output/REVIEW.md`) using extracted claims and evidence gaps (novelty/soundness/clarity/impact). **Trigger**: rubric review, referee report, peer review write-up, 审稿报告, REVIEW.md. **Use when**: peer-review pipeline 的最后阶段(C3),已有 `output/CLAIMS.md` + `output/MISSING_EVIDENCE.md`(以及可选 novelty matrix)。 **Skip if**: 上游产物未就绪(claims/evidence gaps 缺失)或你不打算输出完整审稿报告。 **Network**: none. **Guardrail**: 给可执行建议(actionable feedback),并覆盖 novelty/soundness/clarity/impact;避免泛泛而谈。
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