Andrej Karpathy recently wrote about a new pattern he noticed in NanoClaw —configurability through skills instead of config files. “The implied new meta is to write the most maximally forkable repo and then have skills that fork it into any desired more exotic configuration.” I’ve been building SlimClaw, a Python fork inspired by NanoClaw, building on this same idea. Skills over features. Want to add Telegram? You don’t edit config files or toggle feature flags. You create /add-telegram skill and the AI agent modifies the actual code — writing a new channel file, wiring up auth, adding the dependency. The codebase stays clean because the skill is the configuration layer. Maximally forkable. The entire app system is modular — each messaging app is one file in channels/ that gets auto-discovered at startup. The core engine is ~4,800 lines of Python. Small enough to fit in your head (and in an AI agent’s context window), auditable, and easy to fork. Containers by default. Every group conversation runs in an isolated Docker container with its own filesystem, memory, and Claude session. The agent can browse the web, schedule tasks, and manage groups — all sandboxed. Some numbers:
- 30 MB idle memory
- 4,860 lines, 6 dependencies
- One command setup: slimclaw-setup I wrote more about the architecture and design decisions here: https://lnkd.in/g_mKSzBh Give it a try: GitHub: https://lnkd.in/gESdkdSz Join the discord: https://lnkd.in/gwjzn3pv submitted by /u/ganeshan0070
Originally posted by u/ganeshan0070 on r/ArtificialInteligence