by revo2wheels · MCP Server · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 51 |
| Forks | 8 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 58/100 |
| Open Issues | 4 |
| Last Updated | 2026-11-04 |
| Created | 2025-11-02 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~15k |
Montis.icu Data access is not coaching intelligence. Montis.icu is a deterministic endurance coaching engine built around Intervals.icu data. It validates athlete evidence, resolves physiological and training context through explicit coaching logic, and produces a governed coaching state before conversational AI enters the dialogue. A direct API or MCP connection can expose useful athlete data.
These tools work well together with intervalsicugptcoach-public for enhanced workflows:
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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: