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 vinta · Codex Skill · ★ 138
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is hal-9000 safe to install? View the security audit →
HAL 9000 Opinionated macOS development environment automation that dominates your dev setup like cats rule the Internet. This project is named after Arthur C. Clarke's 2001: A Space Odyssey, a heuristic algorithmic computer designed for sentient processing and total mission control. Bootstrap All-in-one command to set up: Agent Skills Claude Code / Plugins / Rules / Statusline Codex Gemini Python Node.js Bun Solidity Docker (OrbStack) Kubernetes Amazon Web Services Google Cloud Components If you prefer only using some of them: Claude Code Plugin: magi: Multi-model brainstorming with Claude, Codex, and Gemini second-opinions: Gets independent code review from Codex
| Stars | 138 |
| Forks | 25 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.2224217059404/100 |
| Last Updated | 2026-10-03 |
| Created | 2015-11-21 |
| Platforms | claude-code, cli, codex, python |
| Est. Tokens | ~14k |
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hal-9000 is Opinionated AI coding agent and dev environment automation for macOS. It is categorized as a Codex Skill with 138 GitHub stars.
hal-9000 is primarily written in Python. It covers topics such as agent-skills, ai-agents, ansible-playbooks.
You can find installation instructions and usage details in the hal-9000 GitHub repository at github.com/vinta/hal-9000. The project has 138 stars and 25 forks, indicating an active community.
hal-9000 is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to hal-9000 on Agent Skills Hub include skillfile, forgecat-agent-profiles, maestro-orchestrate. 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: