Do I need special tools like jq to use this?

No, the core functionality works using standard bash scripts, though jq can be used optionally for advanced JSON filtering.

Can I use this for sprint planning?

Yes, it is ideal for quickly identifying documents with an 'approved' status that are ready for implementation during a sprint.

How does Doc Indexer save tokens?

It reads only the small YAML metadata section at the top of markdown files instead of loading entire documents into Claude's context window.

What happens if my files don't have frontmatter?

The skill will flag those files as non-compliant in the output, helping you maintain better documentation standards across your project.

Documentation Indexer

Name: Documentation Indexer
Author: bodangren

bybodangren

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Content Management

Indexes project documentation by scanning YAML frontmatter to provide instant context without exhausting the token window.

About

The Documentation Indexer skill is designed to optimize how Claude interacts with project-specific documentation within a development workflow. By scanning markdown files in the docs directory and extracting their YAML frontmatter, it creates a structured map of available specifications, plans, and retrospectives. This just-in-time approach allows developers to discover relevant files and identify their status—such as draft, approved, or in-review—before loading full content. This significantly reduces token consumption and improves session efficiency, especially in complex brownfield projects or spec-driven development environments.

Key Features

Automatic compliance checking for documentation metadata standards
9 GitHub stars
Support for both human-readable summaries and machine-readable JSON output
Just-in-time context mapping through YAML frontmatter extraction
Token-efficient documentation discovery without loading full file contents
Recursive scanning of the docs directory to identify specs, plans, and retrospectives

Use Cases

Discovering approved specifications at the start of a new coding session
Identifying relevant design documents and retrospectives during sprint planning
Mapping out project context when migrating or refactoring legacy codebases

About

Key Features

Automatic compliance checking for documentation metadata standards
9 GitHub stars
Support for both human-readable summaries and machine-readable JSON output
Just-in-time context mapping through YAML frontmatter extraction
Token-efficient documentation discovery without loading full file contents
Recursive scanning of the docs directory to identify specs, plans, and retrospectives

Use Cases

Discovering approved specifications at the start of a new coding session
Identifying relevant design documents and retrospectives during sprint planning
Mapping out project context when migrating or refactoring legacy codebases