Scan a git repository to automatically detect directories like domain boundaries, integrations, and complex areas, then generate hierarchical CLAUDE.md files throughout following Anthropic best practices for enhanced AI context management.
Generate hierarchical CLAUDE.md files throughout a repository, following Anthropic's best practices.
/plugin marketplace add reese-allison/claude-tools
/plugin install deep-init
/deep-init
Runs 3 phases:
git ls-files with metadata/deep-init services/api
Generates/updates CLAUDE.md for just that directory.
CLAUDE.md files contain:
A directory qualifies if it is any of:
| Type | Description | Examples |
|---|---|---|
| Domain Boundary | Distinct conceptual area | auth/, billing/, search/ |
| Integration Point | Connects to external systems | integrations/stripe/, api/webhooks/ |
| Sub-App | Self-contained application | apps/admin/, services/worker/ |
| High Technical Complexity | Complex code needing explanation | compiler/, query-engine/ |
If parent and child both qualify, only the child gets a file.
See deep-init/commands/init.md for full details.
MIT
No model invocation
Executes directly as bash, bypassing the AI model
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimnpx claudepluginhub reese-allison/claude-tools --plugin deep-initComprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation
Create comprehensive documentation for code, APIs, and projects.
Complete AI coding workflow system. Self-correcting memory + persistent FTS5-indexed research wikis + auto-research loop + multi-LLM council on a single SQLite store. 33 skills, 8 agents, 22 commands, 37 hook scripts across 24 events. Cross-agent via SkillKit.
Make your AI agent code with your project's architecture, rules, and decisions.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Build and maintain an LLM-curated personal knowledge base in your project — Andrej Karpathy's LLM Wiki pattern, designed to scale to thousands of pages without becoming a context bottleneck. Now with an optional compiled graph layer for typed, provenance-backed relationships.