leads-literature-mining
SkillDev toolsA specialized LLM agent for automating systematic reviews and meta-analyses, capable of high-accuracy study selection and data extraction.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the leads-literature-mining skill
About this skill
The largest open-source medical AI skills library for OpenClaw🦞.
What this skill tells your AI
The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/leads-literature-mining/SKILL.md and read by ahel’s review.
name: leads-literature-mining description: Review Automator keywords:
- literature-mining
- systematic-review
- meta-analysis
- pubmed
- evidence-synthesis measurable_outcome: Complete a systematic review screen of 100+ papers with >90% inclusion/exclusion accuracy compared to human baseline. license: CC-BY-4.0 metadata: author: Nature Communications 2025 version: "1.0.0" compatibility:
- system: Python 3.9+ allowed-tools:
- run_shell_command
- web_fetch
LEADS (Literature Mining Agent)
A specialized LLM agent for automating systematic reviews and meta-analyses, capable of high-accuracy study selection and data extraction.
When to Use
- Systematic Reviews: Screening thousands of abstracts for inclusion criteria.
- Data Extraction: Pulling specific metrics (e.g., hazard ratios, sample sizes) from full-text PDFs.
- Evidence Synthesis: Aggregating findings across multiple studies.
Core Capabilities
- Study Selection: Automated screening based on PICO criteria.
- Data Extraction: Structured extraction of study characteristics and results.
- Quality Assessment: Risk of bias evaluation.
Workflow
- Search: Query PubMed/Embase.
- Screen: Apply inclusion/exclusion criteria to abstracts.
- Extract: Parse full text for data points.
- Report: Generate PRISMA flow diagram and evidence table.
Example Usage
User: "Perform a systematic review on the efficacy of CAR-T in solid tumors."
Agent Action:
python -m leads.review --topic "CAR-T solid tumors" --criteria ./criteria.json
Advanced
- Item type
- skill
- Key
leads-literature-mining- Source
- github.com/freedomintelligence/openclaw-medical-skills
github.com/freedomintelligence/openclaw-medical-skills
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