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 he-yufeng · Agent Tool · ★ 264
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is NanoCoder safe to install? View the security audit →
NanoCoder 中文 Claude Code Architecture Deep Dive (7 articles) 512,000 lines of TypeScript → 950 lines of Python. I spent two days reverse-engineering the leaked Claude Code source — all half a million lines. Then I stripped it down to the load-bearing walls and rebuilt them in Python. The result: every key architectural pattern from Claude Code, in a codebase you can read in one sitting. NanoCoder is not another AI coding tool. It's a blueprint — the nanoGPT of coding agents. Read it, fork it, build your own. What You Get Claude Code's 512K lines distilled to 7 patterns that actually matter:
| Stars | 264 |
| Forks | 72 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71.8921452355638/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-06 |
| Created | 2026-04-01 |
| Platforms | claude-code, cli, python |
| Est. Tokens | ~42k |
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NanoCoder is Minimal AI coding agent (~950 Lines Python) inspired by Claude Code. Works with any LLM. Think nanoGPT for coding agents.. It is categorized as a Agent Tool with 264 GitHub stars.
NanoCoder is primarily written in Python. It covers topics such as ai-agent, claude-code, cli.
You can find installation instructions and usage details in the NanoCoder GitHub repository at github.com/he-yufeng/NanoCoder. The project has 264 stars and 72 forks, indicating an active community.
NanoCoder is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to NanoCoder on Agent Skills Hub include Overture, golembot, palot. 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: