by supermodeltools · MCP Server · ★ 94
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm install -g @supermodeltools/cli
git clone https://github.com/supermodeltools/cliTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 94 |
| Forks | 9 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73/100 |
| Open Issues | 11 |
| Last Updated | 2026-07-20 |
| Created | 2026-03-31 |
| Platforms | claude-code, cli, go, mcp |
| Est. Tokens | ~72k |
Supermodel CLI Save 40%+ on agent token costs with code graphs. Supermodel maps every file, function, and call relationship in your repo and writes a file next to each source file. Your agent reads them automatically via grep and cat. No prompt changes. No extra context windows. No new tools to learn.
Looking for a cli alternative? If you're comparing cli with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
One command gives AI agents instant codebase context. ~250 tokens replaces 50,000+ tokens of exploration. Auto
The code quality toolkit for the agentic AI era. Find dead code, clones, and scaffolding across 15 languages.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: