nature-literature-pipeline
Complete automated literature discovery pipeline: multi-source search → six-dimension scoring → fine reading → formatted delivery → archival. Combines a configurable engine with daily cron-driven application layer. Works with Feishu, Telegram, or any messaging platform.
Install
npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-literature-pipelineSKILL.md
Nature Literature Pipeline
A complete, production-tested automated literature pipeline. Not just "search for papers" — it's a structured engine that scores, classifies, reads, delivers, and archives research papers daily.
What It Does
Cron (daily trigger, e.g. 08:30)
│
├─ ① SEARCH (30 candidates)
│ arXiv / OpenAlex / Crossref / Semantic Scholar (auto-degradation)
│
├─ ② COARSE FILTER (30 → 5)
│ Six-dimension scoring: topic match × 35 + methodology × 20
│ + journal quality × 15 + network relevance × 10
│ + applied value × 10 + archival value × 10
│
├─ ③ FINE READ (top 5)
│ Abstract-level or full-text. Source level tagged:
│ Full-text / Abstract only / Metadata only
│
├─ ④ DELIVER
│ Formatted digest to Feishu/Telegram/etc.
│ 🏅 rank | title | journal | ⭐ score | 💡 one-liner
│ 🔬 methods | 📊 key results | 🧭 commentary
│
└─ ⑤ ARCHIVE
DOI/arXiv de-dup → classify → write notes → update index
Quick Start
After installing, tell your agent:
My research area is [X], keywords: [Y], deliver to [feishu group name], archive to [path]
The agent will configure keywords, delivery target, and archive path automatically.
Then set up a daily cron job:
Set up a daily literature push at 08:30 Beijing time, 30 candidates, top 5 delivered
Architecture
The skill is organized in two layers:
| Layer | Purpose | Files |
|---|---|---|
| Engine | Scoring, classification, note templates, gap analysis | references/scoring-system.md, references/gap-analysis.md, references/note-template.md |
| Application | Daily cron pipeline, delivery formatting, archival workflow | references/push-format.md, references/cron-setup.md, references/review-compilation-workflow.md |
Configuration
All domain-specific content is configurable:
- Keywords — your research keywords (English + Chinese)
- Scoring weights — adjust the six dimensions for your field
- Classification rules — define your own tier system (A-E or custom)
- Delivery target — Feishu group, Telegram channel, email, etc.
- Archive path — local vault/wiki directory
A config template is provided in templates/literature-push-template.md.
Built-in Safeguards
- Score validation: Each dimension capped, total recalculated — no 11/10 allowed
- Triple de-duplication: DOI / arXiv ID / OpenAlex ID
- Graceful degradation: Semantic Scholar down → auto-switch to OpenAlex + Crossref + arXiv
- Read-only archive: Daily pipeline writes to
raw/literature directory only; never modifies wiki/knowledge base without user approval
Related Skills
nature-academic-search— ad-hoc literature search (complementary; this skill adds structured daily automation)nature-citation— CNS citation export (for importing pipeline discoveries into manuscripts)zotero— library management (for long-term organization of pipeline outputs)arxiv— arXiv API (used as a search source)
References
| Reference | Purpose |
|---|---|
references/scoring-system.md |
Six-dimension scoring rubric with weights, caps, and evaluation logic |
references/gap-analysis.md |
Methodology for identifying research gaps through systematic literature survey |
references/note-template.md |
Standardized literature note format with YAML frontmatter |
references/push-format.md |
Daily digest message template with field guidelines and example |
references/cron-setup.md |
Cron job creation, verification, and manual fallback procedures |
references/review-compilation-workflow.md |
End-to-end workflow for concentrated literature review writing |
Pitfalls
- Keyword drift: Review keywords monthly — research directions evolve
- Score inflation: Subagents may inflate scores; always validate arithmetic
- Duplicate creep: Classic papers will reappear; maintain a dedup index
- Wiki safety: Pipeline writes to
raw/only; wiki integration is manual - Cron locality: Hermes cron is local, not cloud — machine must be running
Related skills
researchmattpocock575KInvestigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.paper-context-resolverlllllllama451KRigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing Renv-and-assets-bootstraplllllllama450KRigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.ai-research-explorelllllllama311KRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow c