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 Miosa-osa · Codex Skill · ★ 76
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OSA safe to install? View the security audit →
OSA: the Optimal System Agent OSA finds the signal in your work. Built on Signal Theory, it classifies what you ask, filters the noise, and routes the work to the right model, proactively, and on your machine. Across your code, your ops, and the everyday busywork, OSA separates what matters from the noise and does the work that counts. One command to install. Runs locally. Works with any model. Install in one command macOS / Linux, paste this into a terminal: Windows, paste this into PowerShell: That's it. No Elixir, Erlang, or Rust required. The installer detects your OS an
| Stars | 76 |
| Forks | 20 |
| Language | Elixir |
| Category | Codex Skill |
| Quality Score | 62.3966322124588/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-30 |
| Created | 2026-02-26 |
| Platforms | claude-code, cli |
| Est. Tokens | ~28k |
These tools work well together with OSA for enhanced workflows:
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OSA is An AI agent for your terminal, built on signal theory: every output is signal, not noise. Codes, runs multi-step work with subagents, and works with any model, including your Claude or ChatGPT subscri. It is categorized as a Codex Skill with 76 GitHub stars.
OSA is primarily written in Elixir. It covers topics such as ai-agent, anthropic, autonomous-agent.
You can find installation instructions and usage details in the OSA GitHub repository at github.com/Miosa-osa/OSA. The project has 76 stars and 20 forks, indicating an active community.
The top alternatives to OSA on Agent Skills Hub include captain-claw, edge, cognithor. 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: