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Claude Code

Angzarr uses Claude Code (Anthropic’s CLI tool) for AI-assisted development. Project-specific instructions are maintained in CLAUDE.md files.


Claude Code reads instructions from CLAUDE.md files at multiple levels:

~/CLAUDE.md # User-level (global preferences)
~/workspace/angzarr/CLAUDE.md # Project-level (angzarr-specific)

Project-level instructions override user-level for that codebase.


The angzarr CLAUDE.md includes:

  • @handles / @applies — Decorator-driven OO aggregate structure
  • Error constants — Centralized error messages
  • IoC patterns — Dependency injection via generics
  • TDD mandatory — Red/green/refactor cycle
  • Three levels — Unit, integration, acceptance
  • Gherkin specs — Living documentation
  • No AI attribution — Commits don’t mention Claude/Anthropic
  • lefthook — Pre-commit hooks for lint/format/test

CLAUDE.md includes assembled context from SCM:

{/* SCM:BEGIN */}
@.scm/context.md
{/* SCM:END */}

This keeps AI context in sync with project practices. See SCM for details.


Claude Code supports custom skills (slash commands). Angzarr defines:

Tear down, rebuild from scratch, and redeploy to the Kind cluster.

Comprehensive code review following project standards.

Review recent changes (uncommitted or last commit).


Claude Code hooks execute shell commands in response to events:

{
"hooks": {
"pre-commit": ["just check", "just fmt --check"],
"post-tool-use": {
"Write": ["just fmt {{file}}"]
}
}
}

Hooks ensure code quality without manual intervention.


Claude Code can connect to MCP servers for extended capabilities:

{
"mcpServers": {
"scm": {
"command": "scm",
"args": ["mcp"]
},
"mcp-tasks": {
"command": "mcp-tasks"
}
}
}
  • scm — Context fragment management
  • mcp-tasks — Task tracking in markdown files

Update CLAUDE.md when project practices change. Stale instructions cause AI confusion.

Claude follows instructions literally. Vague guidance produces inconsistent results.

Large CLAUDE.md files become unwieldy. Use SCM fragments for modular context.

After updating instructions, test common tasks to verify AI behavior matches expectations.