AI-assisted, end-to-end meta-analysis with reproducible tooling.
From research question to manuscript-ready output -- powered by Claude Code.
# 1. Setup (one-time)
cp .env.example .env # Add your API keys
cd tooling/python && uv init
# 2. Create a new project
uv run tooling/python/init_project.py --name my-meta-analysis
# 3. Edit your research question
# Open projects/my-meta-analysis/TOPIC.txt and paste your topic
# 4. Launch Claude Code and say:"Start project my-meta-analysis" or "See projects/my-meta-analysis/TOPIC.txt and start"
That's it. Claude will handle the rest.
Don't have a topic yet? Say:
"Help me brainstorm a topic"
Claude will guide you through an interactive session to develop your research question and create the project for you.
- Creates your project in
projects/<your-project-name>/ - Reads your topic from
projects/<your-project-name>/TOPIC.txt - Asks clarifying questions (databases, outcomes, dates)
- Runs the 9-stage pipeline automatically
- Generates manuscript-ready outputs
All stages are created inside projects/<your-project-name>/:
| Stage | Output |
|---|---|
| 01_protocol | pico.yaml, eligibility.md |
| 02_search | dedupe.bib |
| 03_screening | decisions.csv |
| 04_fulltext | manifest.csv |
| 05_extraction | extraction.csv |
| 06_analysis | figures/, tables/ |
| 07_manuscript | manuscript.pdf |
| 08_reviews | grade_summary.md |
| 09_qa | final_qa_report.md |
See a real meta-analysis → projects/ici-breast-cancer/
This is a 99% complete meta-analysis on immune checkpoint inhibitors in triple-negative breast cancer:
- 5 RCTs, N=2,402 patients
- Primary outcome: RR 1.26 (95% CI 1.16-1.37), p=0.0015, ⊕⊕⊕⊕ HIGH quality
- Manuscript: 4,921 words (Lancet Oncology compliant)
- Time invested: ~14 hours (vs 100+ hours manual)
Quick tour:
projects/ici-breast-cancer/README.md- Project overviewprojects/ici-breast-cancer/00_overview/FINAL_PROJECT_SUMMARY.md- Key findingsprojects/ici-breast-cancer/07_manuscript/- Complete manuscript (5 sections)
Use as template for your own meta-analysis workflow.
meta-pipe/
├── ma-*/ # Framework code modules
├── docs/archive/ # Archived documentation
├── tooling/ # Shared tools and scripts
└── projects/ # All your meta-analysis projects
├── ici-breast-cancer/ # Example: complete meta-analysis
├── legacy/ # Historical data (pre-2026-02-08)
└── your-project/ # Your new projects
├── 01_protocol/
├── 02_search/
├── ...
├── 09_qa/
└── TOPIC.txt
Note: Projects are isolated and NOT tracked by Git (see .gitignore). Only the example project ici-breast-cancer/ is tracked for reference.
| Doc | Purpose |
|---|---|
| GETTING_STARTED.md | Manual step-by-step guide |
| API Setup | Database API keys |
| CLAUDE.md | Agent behavior (auto-loaded) |
| tooling/scripts/ | Utility scripts |
If you use meta-pipe in your research, please cite it:
AMA Format:
Lin HT, Yeh JT. meta-pipe: AI-assisted, end-to-end meta-analysis pipeline with reproducible tooling. GitHub; 2025. Accessed 2026. https://github.com/htlin222/meta-pipe
BibTeX:
@software{lin_metapipe_2025,
author = {Lin, Hsieh-Ting and Yeh, Jiunn-Tyng},
title = {meta-pipe: AI-assisted, end-to-end meta-analysis pipeline with reproducible tooling},
year = {2025},
url = {https://github.com/htlin222/meta-pipe},
note = {Accessed: 2026}
}uv(Python)- R ≥ 4.2 +
renv cmake(required for building R packages likefson macOS ARM)- Quarto
- API keys in
.env
If you use meta-pipe in your research, please cite it.
AMA format:
Lin HT, Yeh JT. meta-pipe: AI-assisted, end-to-end meta-analysis pipeline with reproducible tooling. GitHub; 2026. Accessed March 22, 2026. https://github.com/htlin222/meta-pipe
BibTeX:
@software{lin2026metapipe,
author = {Lin, Hsieh-Ting and Yeh, Jiunn-Tyng},
title = {meta-pipe: AI-Assisted, End-to-End Meta-Analysis Pipeline with Reproducible Tooling},
year = {2026},
url = {https://github.com/htlin222/meta-pipe},
note = {Software}
}