Can I limit the size of the data being ingested?

Yes, the skill includes a --max-size option to prevent large binary files or logs from overwhelming the context window.

What makes this better than standard file reading?

It provides a consolidated, three-section output (Summary, Tree, Content) that gives the AI a holistic view of the project architecture rather than fragmented file segments.

How do I exclude unnecessary files like node_modules?

You can use the -e or --exclude-pattern flag within the skill to filter out directories like node_modules, build, or dist to save on token costs.

What is the ingesting-git skill?

It is a specialized Claude Code skill that converts GitHub or local repositories into a structured text format that AI models can easily read, parse, and analyze.

Does this skill work with private GitHub repositories?

Yes, by providing a GitHub access token via the -t or --token configuration option, you can securely ingest and analyze private repositories.

GitIngest Repo Processor

Name: GitIngest Repo Processor
Author: Git-Fg

byGit-Fg

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Herramientas para Desarrolladores

Transforms entire codebases into structured, LLM-optimized text digests for deep repository analysis and contextual understanding.

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The ingesting-git skill bridges the gap between complex source code repositories and Large Language Model context windows by transforming folder structures and file contents into a single, structured text document. By utilizing the GitIngest protocol, it provides an organized summary, a visual directory tree, and consolidated file contents while allowing users to filter out noise like dependency folders or build artifacts. This is an essential utility for developers needing to perform comprehensive codebase audits, automated documentation generation, or rapid context loading for AI-driven development tasks.

Características Principales

Generates a clear directory structure visualization for immediate architecture context
1 GitHub stars
Converts remote and local repositories into structured plain-text digests
Advanced filtering with include and exclude patterns to minimize token usage
Supports branch-specific ingestion and private repository access via tokens
Optimized output formatting designed specifically for Claude and LLM consumption

Casos de Uso

Analyzing a new open-source codebase to quickly understand its architecture and logic
Ingesting specific modules of a project for targeted bug fixing or large-scale refactoring
Generating comprehensive technical documentation based on the current repository state

Acerca de

Características Principales

Generates a clear directory structure visualization for immediate architecture context
1 GitHub stars
Converts remote and local repositories into structured plain-text digests
Advanced filtering with include and exclude patterns to minimize token usage
Supports branch-specific ingestion and private repository access via tokens
Optimized output formatting designed specifically for Claude and LLM consumption

Casos de Uso

Analyzing a new open-source codebase to quickly understand its architecture and logic
Ingesting specific modules of a project for targeted bug fixing or large-scale refactoring
Generating comprehensive technical documentation based on the current repository state