Creator · Yuan1z0825
Last updated · Sep 2, 2026
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.
Review then install
Install targets
Codex install prompt
Install the "nature-literature-pipeline" agent skill from https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-literature-pipeline. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"yuan1z0825-nature-literature-pipeline","task":"Install nature-literature-pipeline","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add Yuan1z0825/nature-skills --skill nature-literature-pipeline
Maintenance
fresh
7d since push
Risk
Safe to try
Quality score needs review
GitHub quality
39K
92/100 Quality · 87/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
39K GitHub stars
Repo activity
39K stars, 2.1K forks
Maintenance
7d since push
License
MIT
Install
npx skills add Yuan1z0825/nature-skills --skill nature-literature-pipeline
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add Yuan1z0825/nature-skills --skill nature-literature-pipelineDo not use when
Alternative
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Alternative
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Alternative
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256.3K Stars
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Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20nature-literature-pipeline%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20nature-literature-pipeline%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/yuan1z0825-nature-literature-pipeline/install
Agent should check
Copy prompt
Task: Use nature-literature-pipeline in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20nature-literature-pipeline%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/yuan1z0825-nature-literature-pipeline/install
Install command: npx skills add Yuan1z0825/nature-skills --skill nature-literature-pipeline
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/yuan1z0825-nature-literature-pipeline/install
LLM text format
/api/skills/yuan1z0825-nature-literature-pipeline/install?format=text
Find alternatives
/api/skills/search?q=nature-literature-pipeline&limit=3
Agent prompt
Use nature-literature-pipeline for this task. Review https://www.openagentskill.com/api/skills/yuan1z0825-nature-literature-pipeline/install, then install with: npx skills add Yuan1z0825/nature-skills --skill nature-literature-pipelineRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/yuan1z0825-nature-literature-pipeline
LLM text
/api/registry/manifest/yuan1z0825-nature-literature-pipeline?format=text
Install alias
/api/registry/install/yuan1z0825-nature-literature-pipeline
Recommend
/api/registry/recommend?task=Use%20nature-literature-pipeline%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS39K GitHub stars
Stars/forks activity
PASS39K stars, 2.1K forks; issue activity unavailable in current metadata
Recent maintenance
PASS7d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
A relentless interview to sharpen a plan or design.
--- name: nature-literature-pipeline description: | 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. license: MIT metadata: author: Jiahao8595 hermes: tags: [research, literature, pipeline, cron, automation, discovery] related_skills: [nature-academic-search, nature-citation, arxiv, zotero] ---
# 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
1. **Keyword drift**: Review keywords monthly — research directions evolve 2. **Score inflation**: Subagents may inflate scores; always validate arithmetic 3. **Duplicate creep**: Classic papers will reappear; maintain a dedup index 4. **Wiki safety**: Pipeline writes to `raw/` only; wiki integration is manual 5. **Cron locality**: Hermes cron is local, not cloud — machine must be running
Source provenance
Decision snapshot
38,703 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for nature-literature-pipeline, ready for a manual X post.
nature-literature-pipeline: Complete automated literature discovery pipeline: multi-source search → six-dimension scoring... 38.7K stars https://www.openagentskill.com/skills/yuan1z0825-nature-literature-pipeline?ref=x
Listing + install path for nature-literature-pipeline: https://www.openagentskill.com/skills/yuan1z0825-nature-literature-pipeline?ref=x Install: npx skills add Yuan1z0825/nature-skills --skill nature-literature-pipeline
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Review then install
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256.3K StarsPermission surface
network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness