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 SafeRL-Lab · Agent Tool · ★ 396
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is nano-claude-code safe to install? View the security audit →
English Português CheetahClaws: A Fast and Easy-to-Use Agent Harness Infrastructure for Long-Horizon, Multi-Model, and Tool-Using AI Systems Website · Scaling the Harness · Issue Quick Install After installation: Other install methods: pip install full details 🔥🔥🔥 News (Pacific Time) Jun
| Stars | 396 |
| Forks | 166 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 66.951921451708/100 |
| Open Issues | 3 |
| Last Updated | 2026-04-06 |
| Created | 2026-04-01 |
| Platforms | claude-code, python |
| Est. Tokens | ~103k |
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nano-claude-code is Nano Claude Code: A Fast, Easy-to-Use Python Reimplementation of Claude Code Supporting Any Model.. It is categorized as a Agent Tool with 396 GitHub stars.
nano-claude-code is primarily written in Python. It covers topics such as agentic-ai, claude, claude-code.
You can find installation instructions and usage details in the nano-claude-code GitHub repository at github.com/SafeRL-Lab/nano-claude-code. The project has 396 stars and 166 forks, indicating an active community.
nano-claude-code is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to nano-claude-code on Agent Skills Hub include ios-simulator-skill, mcp-server-code-execution-mode, mcp-memory-service. 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: