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 amazinglvxw · Codex Skill · ★ 87
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is enso-os safe to install? View the security audit →
Quickstart • PAC 🪞 • What Enso Adds • Works With • How It Works • 中文 Enso is the first AI plugin that speaks first. Every LLM product today — ChatGPT, Claude, Gemini, Perplexity — is built on the same anti-pattern: AI responds, AI never initiates. It observes your mistakes and stays silent. It sees your self-limiting patterns and waits for you to ask. Enso breaks that silence. It is a discipline plugin that adds three things no mainstream AI product has by default: Code-enforced learning — errors become
| Stars | 87 |
| Forks | 5 |
| Language | Shell |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 66.897051065525/100 |
| Last Updated | 2026-04-24 |
| Created | 2026-03-29 |
| Platforms | claude-code, cli, gemini |
| Est. Tokens | ~89k |
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enso-os is The first AI plugin that speaks first. Code-enforced learning + active forgetting + PAC (Proactive Accountability Challenge). Works with Claude Code, Gemini CLI, Hermes, OpenClaw.. It is categorized as a Codex Skill with 87 GitHub stars.
enso-os is primarily written in Shell. It covers topics such as accountability, agent-os, agent-plugin.
You can find installation instructions and usage details in the enso-os GitHub repository at github.com/amazinglvxw/enso-os. The project has 87 stars and 5 forks, indicating an active community.
enso-os is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to enso-os on Agent Skills Hub include ComfyUI_Skills_OpenClaw, awesome-agent-evolution, shellward. 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: