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 shivasurya · MCP Server · ★ 140
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is code-pathfinder safe to install? View the security audit →
Open-source SAST engine that traces vulnerabilities across files and functions Website · Docs · Rule Registry · MCP Server · Blog Quick Start Install: Scan a Python project (rules download automatically): Scan Dockerfiles: bash pathfinder scan --ruleset dock
| Stars | 140 |
| Forks | 17 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 71.8439998248714/100 |
| Open Issues | 37 |
| Last Updated | 2026-07-10 |
| Created | 2023-11-19 |
| Platforms | go, mcp |
| Est. Tokens | ~2847k |
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code-pathfinder is Static Code Analysis for security teams with Inter file taint analysis. Built for finding vulnerabilities, advanced structural search, derive insights and supports MCP. It is categorized as a MCP Server with 140 GitHub stars.
code-pathfinder is primarily written in Go. It covers topics such as ai-agents, ai-sast, application-security.
You can find installation instructions and usage details in the code-pathfinder GitHub repository at github.com/shivasurya/code-pathfinder. The project has 140 stars and 17 forks, indicating an active community.
code-pathfinder is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to code-pathfinder on Agent Skills Hub include 0, 0sec, fossil-mcp. 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: