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 →
by kris-hansen · MCP Server · ★ 324
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is comanda safe to install? View the security audit →
comanda Make coding agents earn their exit. Comanda is the terminal-native runtime for durable, self-improving agent work in a repository. Describe a workflow in English, inspect the generated program, run the coding agents you already use, and stop only when your own quality gates say the work is done. From an idea to a governed workflow Comanda starts with English, but ends with a YAML program you can inspect, version, and improve. Describe it. Inspect it. Run it. Improve it. Commit it. A loop does not finish because an agent says “DONE” Long-running work needs an observable exit criterion. Comanda's agentic loops persist stat
| Stars | 324 |
| Forks | 26 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.5766375744793/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-16 |
| Created | 2024-10-23 |
| Platforms | claude-code, cli, codex, gemini, go, mcp |
| Est. Tokens | ~17k |
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comanda is The CLI-native orchestrator for AI agent workflows. Run Claude Code, Codex, Gemini CLI & Kimi Code from declarative YAML. Because the terminal is where real work happens.. It is categorized as a MCP Server with 324 GitHub stars.
comanda is primarily written in Go. It covers topics such as agents, ai, ai-agents.
You can find installation instructions and usage details in the comanda GitHub repository at github.com/kris-hansen/comanda. The project has 324 stars and 26 forks, indicating an active community.
comanda is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to comanda on Agent Skills Hub include agentsys, hcom, gptree. 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: