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codebase-context

Generate codebase-context.md and JSON/Mermaid artifacts from project structure.

9|1|Updated Jan 22, 2026
Search Tags:#AI agents#project onboarding#dependency graph#codebase context#staleness detection#semantic lookup
One-click install
npx skills add https://github.com/airowe/codebase-context-skill --skill codebase-context-airowe
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: codebase-context
Source: https://github.com/airowe/codebase-context-skill/tree/main
Command: npx skills add https://github.com/airowe/codebase-context-skill --skill codebase-context-airowe

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires madge, depcruise, jq, and includes scripts (resource) components.

What problem does it solve?

It prevents AI agents from wasting tokens blindly exploring a codebase by generating compact, reusable context documents that reflect the project’s current structure and conventions.

Core Features & Use Cases

  • Pre-built context generation: Produces a human-readable codebase-context.md plus machine-optimized artifacts for quicker understanding.
  • Staleness detection: Adds a snapshot and a freshness check so you can regenerate when the codebase meaningfully changes.
  • Good onboarding + less exploration: Ideal for new-project setup, post-architecture-change updates, and scenarios where agents repeatedly re-discover the same fundamentals.

Quick Start

Run the skill to generate the project’s codebase-context.md and supporting index/graph files from your repository so your agent can consult them when needed.

Frequently Asked Questions about codebase-context

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate codebase context for AI agents to reduce token waste during project onboarding?▼

Generate lean codebase context by running the skill to analyze project structure and output a codebase-context.md file with JSON and Mermaid dependency graph artifacts. This prevents AI agents from wasting tokens on blind exploration during project onboarding.

What is staleness detection for codebase context and how does it work?▼

Staleness detection uses a snapshot and freshness check script to monitor codebase context validity. When structural or configuration changes occur, the check identifies stale context and triggers regeneration to maintain accurate AI agent operational reuse.

Do I need specific dependencies to generate dependency graphs and codebase context?▼

Yes, generating dependency graphs and codebase context requires madge, depcruise, and jq installed. These dependencies analyze project patterns to produce machine-optimized artifacts and Mermaid dependency graphs for AI agent consumption.

Can I use this skill for monorepo onboarding and post-architecture-change updates?▼

Yes, the skill supports monorepo onboarding, new project setup, and context regeneration after significant structural or configuration changes. It requires a valid YAML frontmatter entry to analyze the project and output reusable context documents.

What's the best way to keep AI agent context documents fresh in a changing codebase?▼

Keep AI agent context documents fresh by using the built-in staleness detection snapshot and freshness check script. This mechanism identifies meaningful codebase changes and signals when to regenerate the codebase-context.md and supporting index files.

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