by ArihantDeva · MCP Server · ★ 137
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm i -g @arihantdeva/heimdallTaken 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 | 137 |
| Forks | 9 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65/100 |
| Open Issues | 3 |
| Last Updated | 2026-11-02 |
| Created | 2026-08-20 |
| Platforms | claude-code, cli, mcp, node |
| Est. Tokens | ~22k |
Heimdall Your agent keeps rebuilding work you already did. Heimdall makes it stop. Heimdall gives AI coding agents persistent memory across every repository and project you work on so the question "did I already solve this in another project?" gets answered by one verified search instead of twenty minutes of grep, , and loops. The problem it solves 1. Memory that doesn't live in one repository.
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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: