No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by htlin222 · Claude Skill · ★ 130
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is meta-pipe safe to install? View the security audit →
Meta-Analysis Pipeline AI-assisted, end-to-end meta-analysis with reproducible tooling. From research question to manuscript-ready output -- powered by Claude Code. Quick Start "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. What Claude Does Creates your project in projects
| Stars | 130 |
| Forks | 50 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 66.5138963890409/100 |
| Open Issues | 11 |
| Last Updated | 2026-09-23 |
| Created | 2026-02-05 |
| Platforms | claude-code, cli, python |
| Est. Tokens | ~14k |
These tools work well together with meta-pipe for enhanced workflows:
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meta-pipe is Claude Code-powered end-to-end meta-analysis automation: AI-assisted literature review, screening, extraction, analysis, and manuscript generation for systematic reviews and clinical evidence synthesi. It is categorized as a Claude Skill with 130 GitHub stars.
meta-pipe is primarily written in Python. It covers topics such as biostatistics, claude-code, clinical-research.
You can find installation instructions and usage details in the meta-pipe GitHub repository at github.com/htlin222/meta-pipe. The project has 130 stars and 50 forks, indicating an active community.
The top alternatives to meta-pipe on Agent Skills Hub include medsci-skills, Auto-Research-Skills, af-deep-research. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: