by rpamis · Agent Tool · ★ 3.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm install -g @rpamis/comet
npx skills add rpamis/cometTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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 | 3,272 |
| Forks | 313 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64/100 |
| Open Issues | 34 |
| Last Updated | 2026-11-05 |
| Created | 2026-05-14 |
| Platforms | node |
| Est. Tokens | ~18k |
<img alt="Comet Docs" src
These tools work well together with comet for enhanced workflows:
Looking for a comet alternative? If you're comparing comet with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
OpenLoomi is an open-source AI coworker. It connects your work tools, understands what you’re working on, and
Personal memory across agents
The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.
A full-stack AI Red Teaming platform securing AI ecosystems via OpenClaw Security Scan, Agent Scan, Skills Sca
Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budget
Nexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering princi
Explore other popular agent tool tools:
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: