ingesting-git_skill

Overview

This skill transforms Git repositories into structured plain-text digests optimized for large language model consumption. It produces a three-part output: a concise repository summary, a readable directory tree, and full file contents blocks. Use it to quickly convert codebases into LLM-friendly context for analysis, documentation, or downstream tools.

How this skill works

The skill clones or reads a repository path, applies include/exclude patterns and size limits, and then emits a three-section plain-text digest (summary, directory structure, file contents). Configuration flags let you target branches, authenticate to private repos, and control output destination. The output is intentionally simple so other tools or LLMs can parse and consume it reliably.

When to use it

• Preparing a codebase as context for LLM-based code review or analysis
• Generating an ingestible snapshot of a repo for automated documentation or summarization
• Extracting repository structure and file contents while excluding dependencies or large assets
• Feeding downstream analysis pipelines that expect plain-text inputs
• Quickly auditing repository surface area (file counts, estimated tokens, tree)

Best practices

• Use include patterns to limit files to relevant source and docs (e.g., *.py, *.md)
• Exclude node_modules, build/dist, and large binary files to reduce token usage
• Set a sensible max-size per file to avoid very large blobs in the digest
• Provide a GitHub token for private repositories and to avoid rate limits
• Run ingestion on the specific branch you want analyzed to get accurate context

Example use cases

• Ingest a public GitHub repo to generate a digest for an LLM-driven architecture summary
• Filter and ingest only documentation and source files to produce docs-ready content
• Create a reproducible plain-text snapshot for automated security or quality scanning
• Combine with deeper analysis skills to produce prioritized code issues and remediation steps
• Feed the digest into prompt-engineering workflows to craft precise, repo-aware prompts