learn

learn

Research any topic online and create comprehensive learning guides with RAG-optimized indexes for your AI agents.

Why

AI agents work better when they have curated, pre-researched knowledge to draw from instead of searching the web on every question. /learn builds that knowledge base systematically - gathering sources, scoring them for quality, and synthesizing structured guides that agents can reference instantly.

Use cases:

• Learning a new technology before starting implementation
• Building a shared knowledge base across a team's AI tools
• Creating authoritative reference material from scattered online sources
• Producing guides that work as RAG context for Claude Code, OpenCode, and Codex

Installation

agentsys install learn

Requires agentsys to be set up in your project.

Quick Start

/learn react hooks

This searches the web for ~20 sources on React hooks, scores each source for authority and depth, fetches the top results, and writes a synthesized guide to agent-knowledge/react-hooks.md with a companion resources/react-hooks-sources.json containing full source metadata.

How It Works

The learn skill follows a six-stage methodology:

1. Progressive discovery - Funnel approach: broad queries for landscape mapping, focused queries for core content, deep queries for advanced material. Avoids noise from dumping all queries at once.

2. Quality scoring - Each source is scored on a 100-point scale across five dimensions: authority (3x weight), recency (2x), depth (2x), examples (2x), and uniqueness (1x). Official docs score highest; undated blog posts score lowest.

3. Just-in-time extraction - Only high-scoring sources get fetched. Summaries and key insights are extracted - never full content. This keeps token usage predictable and respects copyright.

4. Synthesis - A structured learning guide is generated with prerequisites, core concepts, code examples, common pitfalls, best practices, and further reading. Content is cross-referenced across sources, not copied from any single one.

5. RAG index - The master index (agent-knowledge/CLAUDE.md and AGENTS.md) is updated with the new topic, trigger phrases, and keyword mappings so agents can find relevant guides automatically.

6. Enhancement - Runs enhance:enhance-docs and enhance:enhance-prompts on the output to improve RAG retrieval quality. Skip with --no-enhance.

Usage

# Default depth (20 sources)
/learn recursion

# Deep research (40 sources)
/learn kubernetes networking --depth=deep

# Quick overview (10 sources)
/learn python decorators --depth=brief

# Skip enhancement pass
/learn typescript generics --no-enhance

Depth Levels

Level	Sources	When to Use
brief	10	Quick overview, time-sensitive topics
medium	20	Balanced coverage (default)
deep	40	Comprehensive research, complex topics

Output Files

Each run creates or updates:

agent-knowledge/
  CLAUDE.md                       # Master index (updated)
  AGENTS.md                       # Master index for OpenCode/Codex (updated)
  <topic-slug>.md                 # Synthesized learning guide
  resources/
    <topic-slug>-sources.json     # Source metadata with quality scores

Existing Topics

If a guide already exists for the topic, you are prompted to either update the existing guide with new sources or start fresh.

Architecture

Component	Type	Model	Role
learn	command	-	Entry point, argument parsing
learn-agent	agent	sonnet	Research coordination, web search, synthesis
learn	skill	-	Research methodology, scoring rubric, templates

Requirements

• agentsys runtime
• Web access (WebSearch and WebFetch tools)
• An agent-knowledge/ directory in the workspace (created automatically)

Related Plugins

• agent-knowledge - Where guides are stored; contains existing research
• enhance - Post-processing for RAG optimization
• consult - For getting a second opinion on specific questions instead of building a full guide

License

MIT