Open-Sourced Claude Code Setup — 117k+ Stars
LinkedIn post by Sarthak Rastogi (AI engineer; posts on agents + advanced RAG) — view post
An Anthropic hackathon winner open-sourced his entire Claude Code setup (117k+ GitHub stars as claimed in the post). Repo link: https://lnkd.in/e6jrrA9p
The five layers
- Agents — planner, architect, code reviewer, security auditor; build-error resolvers across Python/Java/Go/Rust/C++; E2E runners, refactor cleaners, doc updaters; even a “chief-of-staff” agent for communication.
- Skills (100+ workflows) — TDD, eval harnesses, verification loops; token optimization + cost-aware LLM pipelines; continuous learning that turns sessions into reusable skills; backend, frontend, DB, DevOps, even investor workflows.
- Commands (60+) —
/plan(break down features),/tdd(enforce test-first),/verify(evaluation loops),/multi-*(multi-agent orchestration),/pm2(service management). - Rules (always-on constraints) — coding standards, testing requirements, security checks; language-specific best practices (Python, TS, Go…); enforced on every generation for consistency.
- Hooks + automation layer — save/load memory across sessions, auto-evaluate outputs, suggest compaction before context breaks, trigger logic per tool call, context-injection modes (dev/review/research), MCP integrations (GitHub, Supabase…), cross-platform scripts/installers.
Also advertised: full test suite validating the setup; SaaS-app scaffolding (Next.js + Stripe).
Comment takeaways
- Shannon Atkinson: real unlock = collaborative workflow network, plus enforcing standards/security every generation, not when convenient.
- Ilya Strelov counterpoint: “more skills ≠ better results — less context is always better.”
- Mahima Mehta: treat AI coding as a system (agents + skills + guardrails + hooks), not prompts alone; question is which patterns become standard.
(P.S. section is self-promo for Liten AI — noted, not summarized.)