---

name: ingesting-git description: "Transforms repositories into structured plain-text digests optimized for LLM consumption. Use when analyzing GitHub repositories, digesting codebases, or ingesting git repos for AI analysis." allowed-tools: [Read, Write, Edit, Bash, Grep]

GitIngest Protocol

Quick Start

Execute via Script:

Copyuv run --with gitingest scripts/ingest.py <url_or_path> [options]

Examples:

Copy# Ingest remote repo
uv run --with gitingest scripts/ingest.py https://github.com/user/repo

# Ingest with filtering
uv run --with gitingest scripts/ingest.py . -i "*.py" -e "tests/*"

Output Format

GitIngest returns structured plain-text optimized for LLM consumption with three distinct sections:

Section 1: Repository Summary

CopyRepository: owner/repo-name
Files analyzed: 42
Estimated tokens: 15.2k

Section 2: Directory Structure

CopyDirectory structure:
└── project-name/
    ├── src/
    │   ├── main.py
    │   └── utils.py
    ├── tests/
    │   └── test_main.py
    └── README.md

Section 3: File Contents

Copy================================================
FILE: src/main.py
================================================
def hello_world():
    print("Hello, World!")

Configuration Options

Common Exclude Patterns

Copynode_modules/*          # Dependencies
*.log                   # Log files
dist/*                  # Build outputs
build/*                 # Build directories
*.min.js                # Minified files
*.lock                  # Lock files

Implementation Protocol

When executing the gitingest skill:

1. Assess Requirements

   • Determine if CLI or Python integration is needed
   • Identify repository size and scope
   • Plan filtering strategy (include/exclude patterns)

2. Setup Environment

   • Verify gitingest installation
   • Check authentication for private repositories
   • Configure output destination

3. Execute Ingestion

   • Run gitingest with appropriate parameters
   • Monitor for errors and timeouts
   • Apply filtering and size limits

4. Process Results

   • Parse the three-section output format
   • Analyze summary, tree, and content
   • Generate insights and reports

Extended Documentation

For detailed integration examples, error handling patterns, and best practices:

• Integration Examples: references/integration-examples.md

Integration with CatToolkit

Usage Examples:

Copy# "Ingest this repository for AI analysis"
# → Uses gitingest to create structured digest

# "Analyze the codebase without dependencies"
# → Uses gitingest with exclude-patterns for node_modules, dist, etc.

# "Generate documentation from this repo"
# → Uses gitingest + filtering to extract docs and code structure

The gitingest skill integrates seamlessly with other CatToolkit skills:

• deep-analysis: Process gitingest output for comprehensive insights
• software-engineering: Analyze ingested code for quality and security
• prompt-engineering: Use repository context to generate better prompts