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 shareAI-lab · Codex Skill · ★ 3.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is claw0 safe to install? View the security audit →
English 日本語 claw0 From Zero to One: Build an AI Agent Gateway 10 progressive sections -- every section is a single, runnable Python file. 3 languages (English, Chinese, Japanese) -- code + docs co-located. What is this? Most agent tutorials stop at "call an API once." This repository starts from that while loop and takes you all the way to a production-grade gateway. Build a minimal AI agent gateway from scratch, section by section. 10 sections, 10 core concepts, 7,000 lines of Python. Each section introduces exactly one new idea while keeping all prior code intact. After all 10, you can read OpenClaw's production codebase with confidence. Architecture + claw0 layers + |
| Stars | 3,383 |
| Forks | 386 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 64.0027628923062/100 |
| Open Issues | 5 |
| Last Updated | 2026-06-30 |
| Created | 2026-02-24 |
| Platforms | python |
| Est. Tokens | ~15k |
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claw0 is 0 - 1 learn OpenClaw: sections to build an claw-AI agent from scratch. It is categorized as a Codex Skill with 3.4k GitHub stars.
claw0 is primarily written in Python.
You can find installation instructions and usage details in the claw0 GitHub repository at github.com/shareAI-lab/claw0. The project has 3.4k stars and 386 forks, indicating an active community.
The top alternatives to claw0 on Agent Skills Hub include superset, refly, Claude-to-IM-skill. 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: