Reverse-engineering Claude Code's brain from the outside in
A research repo that reconstructs how agentic AI assistants assemble prompts, coordinate sub-agents, and police their own tool use—without ever seeing the source.

What it does This project documents 30 reconstructed prompt patterns and architectural behaviors from Claude Code, assembled purely through behavioral observation, output analysis, and community discussion. Think of it as a field guide to how a modern agentic coding assistant likely structures its inner monologue.
The interesting bit The authors treat the black box as a puzzle: they infer a modular prompt assembly pipeline with cacheable prefixes and dynamic per-session suffixes, plus a multi-stage security classifier for auto-approving tool calls. It’s forensic prompt engineering—useful precisely because it’s unofficial and unvarnished.
Key highlights
- Reconstructed patterns span core identity, orchestration, specialized agents, security classification, context compaction, and memory hierarchies
- Documents inferred sub-agents: verification (adversarial testing), explore (read-only), and even an “agent creation architect” that spawns new agents from requirements
- Proposes a memory loading order with transitive file inclusion and path-based conditional injection
- Explicitly disclaims being a leak; all content is approximate and invites correction
Caveats
- Everything is reconstructed approximation, not verbatim source; the README repeatedly warns that “actual implementation may differ significantly”
- No code to run—this is documentation and analysis, not a toolkit
Verdict Worth a skim if you’re building or hardening agentic tools and want a plausible reference architecture to argue with. Skip it if you need copy-paste prompts or verified internals.