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 sharpdeveye · Codex Skill · ★ 411
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is maestro safe to install? View the security audit →
Maestro Workflow fluency for AI coding agents. 1 core skill · 21 commands · 7 domain references · curated anti-patterns Quick Start · Commands · Supported Tools · Contributing What is Maestro? AI agents are only as good as the workflows they operate in. Without guidance, you get the same predictable mistakes: unstructur
| Stars | 411 |
| Forks | 63 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 70.352176079773/100 |
| Last Updated | 2026-04-29 |
| Created | 2026-04-02 |
| Platforms | claude-code, cli, codex, gemini, node |
| Est. Tokens | ~69k |
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maestro is Workflow fluency for AI coding agents. 1 core skill · 25 commands · 7 domain references · memory layer · audit trail — works across Cursor, Claude Code, Gemini CLI, Copilot, and 6 more.. It is categorized as a Codex Skill with 411 GitHub stars.
maestro is primarily written in TypeScript. It covers topics such as agent-orchestration, ai, ai-agents.
You can find installation instructions and usage details in the maestro GitHub repository at github.com/sharpdeveye/maestro. The project has 411 stars and 63 forks, indicating an active community.
maestro is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to maestro on Agent Skills Hub include quint-code, Overture, agnix. 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: