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 paiml · MCP Server · ★ 165
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is paiml-mcp-agent-toolkit safe to install? View the security audit →
PMAT Zero-configuration AI context generation for any codebase Installation Documentation Table of Contents What is PMAT? Installation Usage Features Architecture What is PMAT? PMAT (Pragmatic Multi-language Agent Toolkit) provides everything needed to analyze code quality and generate AI-ready context: Context Generation - Deep analysis for Claude, GPT, and other LLMs Technical Debt Grading - A+
| Stars | 165 |
| Forks | 29 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 75.9053269533905/100 |
| Open Issues | 126 |
| Last Updated | 2026-09-18 |
| Created | 2025-05-25 |
| Platforms | mcp, rust |
| Est. Tokens | ~25k |
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paiml-mcp-agent-toolkit is Pragmatic AI Labs MCP Agent Toolkit - An MCP Server designed to make code with agents more deterministic. It is categorized as a MCP Server with 165 GitHub stars.
paiml-mcp-agent-toolkit is primarily written in Rust. It covers topics such as agentic, c, deno.
You can find installation instructions and usage details in the paiml-mcp-agent-toolkit GitHub repository at github.com/paiml/paiml-mcp-agent-toolkit. The project has 165 stars and 29 forks, indicating an active community.
paiml-mcp-agent-toolkit is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to paiml-mcp-agent-toolkit on Agent Skills Hub include drift, sysplant, reddit-mcp-buddy. 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: