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 sandydasari · MCP Server · ★ 85
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is openacme safe to install? View the security audit →
An AI workforce. You're in charge. Not a single assistant. Not a fixed team of four. A workforce — named agents with roles, models, tools, and memory — that scales the way you want it to and self-organizes through delegation. Hand the top of the org chart a goal; it breaks the work down and assigns it. You steer. <img a
| Stars | 85 |
| Forks | 5 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65.4503553340383/100 |
| Open Issues | 12 |
| Last Updated | 2026-07-28 |
| Created | 2026-06-03 |
| Platforms | browser, claude-code, cli, mcp, node |
| Est. Tokens | ~15k |
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openacme is An AI workforce platform: role-specialized agents with multi-provider LLM, MCP, tasks, and a web + CLI UI. You're in charge.. It is categorized as a MCP Server with 85 GitHub stars.
openacme is primarily written in TypeScript. It covers topics such as agent-framework, agentic-ai, ai-agents.
You can find installation instructions and usage details in the openacme GitHub repository at github.com/sandydasari/openacme. The project has 85 stars and 5 forks, indicating an active community.
openacme is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openacme on Agent Skills Hub include squads-cli, captain-claw, agent-builder. 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: