Agent skill

repomix-package-builder

Create targeted repomix XML packages for AI code analysis. Suggests templates (metadata-flow, code-review, architecture, context) and file patterns based on task objectives. See reference.md for detailed workflows and patterns.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/repomix-package-builder

SKILL.md

Repomix Package Builder

Quick-start tool for creating AI-ready code analysis packages saved to .claude/repomix_packages/.

When to Use

Activate when user needs to:

  • Prepare code for external AI review or analysis
  • Document cross-service integration flows (metadata, events, API calls)
  • Conduct architectural analysis or pattern identification
  • Create context packages for debugging or knowledge transfer
  • Mentions: "repomix", "code package", "AI review", "context export"

Available Templates

  1. metadata-flow - Trace data/metadata between services via events and APIs
  2. code-review - Comprehensive implementation + tests + docs + standards compliance
  3. architecture - System design, service boundaries, DDD layers, patterns
  4. context - Quick overview with essential docs and key implementation files

Quick Workflow

  1. Understand task objective (review? debug? document? analyze?)
  2. Select matching template
  3. Identify scope (which services/components?)
  4. Build file include list (see reference.md for patterns)
  5. Generate filename: repomix-{feature}-{purpose}.xml
  6. Execute repomix command targeting .claude/repomix_packages/

Reference Documentation

  • Detailed Templates & Patterns: See reference.md in this directory
  • Real-World Examples: See examples.md in this directory
  • Slash Command: User can trigger /repomix for interactive workflow

Output Conventions

  • Location: .claude/repomix_packages/
  • Naming: repomix-{feature}-{purpose}.xml
  • Examples: repomix-eng5-kafka-review.xml, repomix-metadata-population-task.xml

Token Budgets

  • Quick context: 20-30K tokens (~7-10 files)
  • Standard review: 60-80K tokens (~25-35 files)
  • Deep analysis: 80-100K tokens (~30-40 files)

For comprehensive guidance on file selection, templates, and best practices, reference reference.md.

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