learn
Research any topic online and create comprehensive learning guides with RAG-optimized indexes for your AI agents. `/learn react hooks` searc
Open source Open in the app JSON README (API)
About
Research any topic online and create comprehensive learning guides with RAG-optimized indexes for your AI agents. `/learn react hooks` searches the web for ~20 sources, scores each on a 100-point scale across authority (3x), recency (2x), depth (2x), examples (2x), and uniqueness (1x), fetches only high-scoring sources, and writes a synthesized guide to `agent-knowledge/<topic>.md` with companion `resources/<topic>-sources.json` containing full source metadata. Six-stage methodology: progressive discovery, quality scoring, just-in-time extraction, synthesis, source citation, RAG optimization. Output shape designed for ingestion by Claude Code, OpenCode, and Codex agent-knowledge libraries.
Details
- Kind
- Plugins
- Topic
- AI, RAG & memory
- Publisher
- agent-sh
- Origin
- marketplace
- Category
- ferramentas
- Stars
- 2
- Last push
- 2026-07-22T13:27:59Z
- Repository state
- ativo
- Language
- JavaScript
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
agent-sh/learn/learn
README
# 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
```bash
agentsys install learn
```
Requires [agentsys](https://github.com/agent-sh/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
```bash
# 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](https://github.com/agent-sh/agentsys) runtime
- Web access (WebSearch and WebFetch tools)
- An `agent-knowledge/` directory in the workspace (created automatically)
## Related Plugins
- [agent-knowledge](https://github.com/agent-sh/agent-knowledge) - Where guides are stored; contains existing research
- [enhance](https://github.com/agent-sh/enhance) - Post-processing for RAG optimization
- [consult](https://github.com/agent-sh/consult) - For getting a second opinion on specific questions instead of building a full guide
## License
MIT