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 jerryfane · Agent Tool · ★ 52
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gitmoot safe to install? View the security audit →
Gitmoot Local-first multi-agent coordination for GitHub pull request workflows. Gitmoot lets humans and AI agents collaborate through the place software teams already audit work: GitHub pull requests. It runs on the user's machine, keeps workflow state in local SQLite, routes PR comments to registered agent runtimes, and writes the agent's work back into the repo and PR discussion. V1 is intentionally local-only. There is no hosted dashboard, webhook receiver, cloud runner, or remote control plane. Why Gitmoot AI agents can already edit code, review diffs, and run local tools. The hard part is coordinating that work across sessions without losing the human audit trail. Gitmoot makes the repository and its pull requests the shared surface: PR comments become agent tasks, review requests, retries, and merge signals. Local SQLite records
| Stars | 52 |
| Forks | 43 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 67.5731744340694/100 |
| Open Issues | 22 |
| Last Updated | 2026-07-15 |
| Created | 2026-05-20 |
| Platforms | go |
| Est. Tokens | ~20k |
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gitmoot is A local-first control layer for AI agents. It is categorized as a Agent Tool with 52 GitHub stars.
gitmoot is primarily written in Go. It covers topics such as agentic-skill, agentic-workflows, agents.
You can find installation instructions and usage details in the gitmoot GitHub repository at github.com/jerryfane/gitmoot. The project has 52 stars and 43 forks, indicating an active community.
gitmoot is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to gitmoot on Agent Skills Hub include AgenticGoKit, agentor, skilllite. 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: