Agent skill
survey-seed-harvest
Identify survey/review papers in a retrieved set and extract taxonomy seeds into `outline/taxonomy.yml` (topics/subtopics/terminology). **Trigger**: survey seed harvest, taxonomy seeds, 从 survey 提 taxonomy, bootstrap taxonomy. **Use when**: retrieval/dedup 后想快速从已有 survey/review 论文中提取术语与主题结构,用于加速 `taxonomy-builder`。 **Skip if**: 已经有高质量 taxonomy(或你不想被 survey 既有框架限制)。 **Network**: none. **Guardrail**: 产物是 seed,必须经 `taxonomy-builder` 重写与对齐 scope;避免生成泛化占位节点。
Install this agent skill to your Project
npx add-skill https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/survey-seed-harvest
SKILL.md
Survey Seed Harvest
Bootstrap taxonomy seeds from existing survey/review papers inside your retrieved set.
This is an accelerator for the early structure stage: it should make taxonomy-builder easier, not replace it.
Inputs
papers/papers_dedup.jsonl(deduped paper metadata with titles/abstracts)
Outputs
outline/taxonomy.yml(seed taxonomy; expected to be refined)
Workflow (heuristic)
Uses: papers/papers_dedup.jsonl.
- Find likely survey/review papers:
- title/abstract contains “survey”, “review”, “systematic”, “meta-analysis”
- Extract candidate topic terms and group them into:
- ~4–10 top-level nodes (“chapters”)
- 2–6 children per node (mappable leaves)
- Write short, actionable descriptions:
- what belongs here / what does not
- (optional) list 2–5 representative titles as seeds
- Treat the result as a starting point:
- pass it to
taxonomy-builderfor domain-meaningful rewriting and scope alignment.
- pass it to
Quality checklist
-
outline/taxonomy.ymlexists and is valid YAML. - Taxonomy has at least 2 levels (
childrenused) and every node has a description. - Avoid generic placeholder nodes like “Overview/Benchmarks/Open Problems” unless they are truly content-based for your domain.
Script (optional helper)
Quick Start
python .codex/skills/survey-seed-harvest/scripts/run.py --helppython .codex/skills/survey-seed-harvest/scripts/run.py --workspace <workspace_dir>
All Options
--top-k <n>: number of candidate terms to consider--min-freq <n>: minimum frequency threshold
Examples
- More conservative term selection:
python .codex/skills/survey-seed-harvest/scripts/run.py --workspace <ws> --top-k 80 --min-freq 3
Notes
- This helper is keyword-based; treat the output as seeds and refine with
taxonomy-builder.
Troubleshooting
Issue: no survey/review papers are detected in the set
Fix:
- Broaden retrieval (add “survey”, “review”, “benchmark” variants) or manually seed a few known surveys, then rerun.
Issue: taxonomy seeds look like generic buckets
Fix:
- Keep seeds concrete (named methods/benchmarks/tasks) and rely on
taxonomy-builderto rewrite under the actual scope.
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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