ReddRadar
Agent skill
agent-survey-corpus
Download a small corpus of open-access arXiv survey/review PDFs about LLM agents 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.
Install this agent skill to your Project
npx add-skill https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/agent-survey-corpus
SKILL.md
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 --helppython .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/).
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WILLOSCAR/research-units-pipeline-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。
WILLOSCAR/research-units-pipeline-skills
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`.
WILLOSCAR/research-units-pipeline-skills
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。
WILLOSCAR/research-units-pipeline-skills
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）。
WILLOSCAR/research-units-pipeline-skills
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。
WILLOSCAR/research-units-pipeline-skills
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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