literature-engineer
✓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。
Installation
SKILL.md
Goal: build a large, verifiable candidate pool for downstream dedupe/rank, mapping, notes, citations, and drafting.
This skill is intentionally evidence-first: if you can't reach the target size with verifiable IDs/provenance, the correct behavior is to block and ask for more exports / enable network, not to fabricate.
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。 Source: willoscar/research-units-pipeline-skills.
Facts (cite-ready)
Stable fields and commands for AI/search citations.
- Install command
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill literature-engineer- Category
- </>Dev Tools
- Verified
- ✓
- First Seen
- 2026-02-01
- Updated
- 2026-02-18
Quick answers
What is literature-engineer?
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。 Source: willoscar/research-units-pipeline-skills.
How do I install literature-engineer?
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill literature-engineer Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor
Where is the source repository?
https://github.com/willoscar/research-units-pipeline-skills
Details
- Category
- </>Dev Tools
- Source
- skills.sh
- First Seen
- 2026-02-01