librarian

Organize Markdown corpora into a frontmatter-driven catalog with INDEX.md.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/jeff-hamm/ai-tools --skill librarian-jeff-hamm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: librarian
Source: https://github.com/jeff-hamm/ai-tools/tree/main/src/skills/librarian
Command: npx skills add https://github.com/jeff-hamm/ai-tools --skill librarian-jeff-hamm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

You organize Markdown corpora so that agents can traverse them by query, not by reading everything. The rules here are corpus-agnostic: research folders, workspace docs, skill references, design notes, RFC archives, decision logs. Anything that will be re-read by an LLM.

You are invoked by other skills and by the user directly. When invoked by another skill (e.g. /internet-researcher, /claude-expert), stay in scope: organizational guidance only. Don't rewrite narrative content; leave editorial decisions to the caller.

Core Features & Use Cases

  • Catalog-driven organization: frontmatter-based records define relationships (topic ↔ sources) and generate human-readable INDEX.md while keeping a machine-readable catalog for queries.
  • Domain-aware scaling: partition topics/sources under domains when the corpus grows beyond ~20 entries, while preserving archive flow and generated views.
  • On-demand references: optional references/, scripts/, and assets/ are used as needed to support auditing, migrations, and tooling without bloating the active context.

Quick Start

Generate the INDEX.md by running the librarian on your topics and sources to bootstrap a navigable, LLM-friendly corpus.

Frequently Asked Questions about librarian

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

FAQPage Schema
How do I organize a large Markdown corpus for LLM-friendly retrieval?

You organize Markdown corpora by applying structured frontmatter fields like title, status, last_updated, and tags to generate a machine-readable catalog and a navigable INDEX.md, enabling fast queryable access for LLM agents.

What is domain partitioning and when should I use it for Markdown knowledge management?

Domain partitioning separates topics and sources into distinct domains within your catalog, and you should apply it when your Markdown corpus grows beyond approximately twenty entries to preserve archive flow and queryable access.

How do I create an index of Markdown documents with frontmatter for agent-assisted browsing?

Running the librarian on your topics and sources bootstraps a navigable corpus by generating an INDEX.md from frontmatter-driven records, establishing bidirectional links between topics and sources for both human and agent traversal.

Can I use this Markdown indexing approach for research folders and design docs?

Yes, this corpus-agnostic approach applies to research folders, design docs, RFC archives, decision logs, and workspace docs—any Markdown collection that will be re-read by an LLM benefits from frontmatter-driven organization.

Does organizing Markdown files with frontmatter require any dependencies or scripts?

No dependencies are required. The organizational rules apply directly to Markdown frontmatter, while optional references, scripts, and assets directories can support auditing, migrations, and tooling without bloating the active context.