by harshkedia177 · MCP Server · ★ 798
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is axon safe to install? View the security audit →
Axon The knowledge graph for your codebase — explore it visually, or let your AI agent query it. Indexes any codebase into a structural knowledge graph — every dependency, call chain, cluster, and execution flow. Explore it through an interactive web dashboard with force-directed graph visualization, or expose it through MCP tools so AI agents get full structural understanding in every tool call. $ axon analyze . Walking files... 142 files found Parsing code... 142/142 Tracing calls... 847 calls resolved Analyzing types... 234 type relationships Detecting communities... 8 clusters found Detecting execution flows... 34 processes found Finding dead code... 12 unreachable symbols Analyzing git history... 18 coupled file pairs Genera
pip install axoniq # 1. Install
pip install axoniqTaken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 798 |
| Forks | 124 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 66.5446571980264/100 |
| Open Issues | 33 |
| Last Updated | 2026-08-03 |
| Created | 2026-02-21 |
| Platforms | claude-code, cli, mcp, python |
| Est. Tokens | ~19k |
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axon is Graph-powered code intelligence engine — indexes codebases into a knowledge graph, exposed via MCP tools for AI agents and a CLI for developers.. It is categorized as a MCP Server with 798 GitHub stars.
axon is primarily written in Python. It covers topics such as claude-code, code-analysis, code-intelligence.
The README on GitHub (github.com/harshkedia177/axon) gives: pip install axoniq # 1. Install , then pip install axoniq. Usage details are in the same README.
The top alternatives to axon on Agent Skills Hub include Auditor, roam-code, antigravity-workspace-template. 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: