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 repowise-dev · MCP Server · ★ 7.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is repowise safe to install? View the security audit →
The intelligence layer that gives your AI agent context, ownership, decisions — and a code-health score proven to predict real bugs. Five intelligence layers · Nine MCP tools · 15 languages · Multi-repo workspaces · One pip install <img src="https://img.shields.io/badge/MCP-compatible-1E293B?style=for-the-badge&labelColor=0A0A0A
| Stars | 7,136 |
| Forks | 775 |
| Language | Python |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 60.4240154600271/100 |
| Open Issues | 243 |
| Last Updated | 2026-10-03 |
| Created | 2026-03-23 |
| Platforms | mcp, python |
| Est. Tokens | ~20k |
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repowise is Codebase intelligence for AI and humans: code health scores, auto-generated docs, git analytics, dead code detection, and architectural decisions via MCP.. It is categorized as a MCP Server with 7.1k GitHub stars.
repowise is primarily written in Python. It covers topics such as ai, code-complexity, code-health.
You can find installation instructions and usage details in the repowise GitHub repository at github.com/repowise-dev/repowise. The project has 7.1k stars and 775 forks, indicating an active community.
repowise is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to repowise on Agent Skills Hub include brooks-lint, composio, klavis. 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: