learn

learn

Research any topic by gathering online resources and creating a comprehensive learning guide with RAG-optimized indexes.

Parse Arguments

const args = '$ARGUMENTS'.split(' ').filter(Boolean);
const depth = args.find(a => a.startsWith('--depth='))?.split('=')[1] || 'medium';
const topic = args.filter(a => !a.startsWith('--')).join(' ');

Input

Arguments: <topic> [--depth=brief|medium|deep]

• topic: Subject to research (required)
• --depth: Source gathering depth
  • brief: 10 sources (quick overview)
  • medium: 20 sources (default, balanced)
  • deep: 40 sources (comprehensive)

Research Methodology

Based on best practices from:

• Anthropic's Context Engineering
• DeepLearning.AI Tool Use Patterns
• Anara's AI Literature Reviews

1. Progressive Query Architecture

Use funnel approach to avoid noise from long query lists:

Broad Phase (landscape mapping):

"{topic} overview introduction"
"{topic} documentation official"

Focused Phase (core content):

"{topic} best practices"
"{topic} examples tutorial"
"{topic} site:stackoverflow.com"

Deep Phase (advanced, if depth=deep):

"{topic} advanced techniques"
"{topic} pitfalls mistakes avoid"
"{topic} 2025 2026 latest"

2. Source Quality Scoring

Multi-dimensional evaluation (max score: 100):

Factor	Weight	Max	Criteria
Authority	3x	30	Official docs (10), recognized expert (8), established site (6), blog (4), random (2)
Recency	2x	20	<6mo (10), <1yr (8), <2yr (6), <3yr (4), older (2)
Depth	2x	20	Comprehensive (10), detailed (8), overview (6), superficial (4), fragment (2)
Examples	2x	20	Multiple code examples (10), one example (6), no examples (2)
Uniqueness	1x	10	Unique perspective (10), some overlap (6), duplicate content (2)

Selection threshold: Top N sources by score (N = depth target)

3. Just-In-Time Retrieval

Don't pre-load all content (causes context rot):

1. Collect URLs first via WebSearch
2. Score based on metadata (title, description, URL)
3. Fetch only selected sources via WebFetch
4. Extract summaries (not full content)

4. Content Extraction Guidelines

For each source, extract:

{
  "url": "https://...",
  "title": "Article Title",
  "qualityScore": 85,
  "scores": {
    "authority": 9,
    "recency": 8,
    "depth": 7,
    "examples": 9,
    "uniqueness": 6
  },
  "keyInsights": [
    "Concise insight 1",
    "Concise insight 2"
  ],
  "codeExamples": [
    {
      "language": "javascript",
      "description": "Basic usage pattern"
    }
  ],
  "extractedAt": "2026-02-05T12:00:00Z"
}

Copyright compliance: Summaries and insights only, never verbatim paragraphs.

Output Structure

Topic Guide Template

Create agent-knowledge/{slug}.md:

# Learning Guide: {Topic}

**Generated**: {date}
**Sources**: {count} resources analyzed
**Depth**: {brief|medium|deep}

## Prerequisites

What you should know before diving in:
- Prerequisite 1
- Prerequisite 2

## TL;DR

Essential points in 3-5 bullets:
- Key point 1
- Key point 2
- Key point 3

## Core Concepts

### {Concept 1}

{Synthesized explanation from multiple sources}

**Key insight**: {Most important takeaway}

### {Concept 2}

{Synthesized explanation}

## Code Examples

### Basic Example

```{language}
// Description of what this demonstrates
{code}

Advanced Pattern

{code}

Common Pitfalls

Pitfall	Why It Happens	How to Avoid
Issue 1	Root cause	Prevention strategy

Best Practices

Synthesized from {n} sources:

1. Practice 1: Explanation
2. Practice 2: Explanation

Further Reading

Resource	Type	Why Recommended
Title	Official Docs	Authoritative reference
Title	Tutorial	Step-by-step guide

---

Generated by /learn from {count} sources. See resources/{slug}-sources.json for full source metadata.

### Master Index Template

Create/update `agent-knowledge/CLAUDE.md`:

```markdown
# Agent Knowledge Base

> Learning guides created by /learn. Reference these when answering questions about listed topics.

## Available Topics

| Topic | File | Sources | Depth | Created |
|-------|------|---------|-------|---------|
| {Topic 1} | {slug1}.md | {n} | medium | 2026-02-05 |
| {Topic 2} | {slug2}.md | {n} | deep | 2026-02-04 |

## Trigger Phrases

Use this knowledge when user asks about:
- "How does {topic1} work?" → {slug1}.md
- "Explain {topic1}" → {slug1}.md
- "{Topic2} best practices" → {slug2}.md

## Quick Lookup

| Keyword | Guide |
|---------|-------|
| recursion | recursion.md |
| hooks, react | react-hooks.md |

## How to Use

1. Check if user question matches a topic
2. Read the relevant guide file
3. Answer based on synthesized knowledge
4. Cite the guide if user asks for sources

Copy to agent-knowledge/AGENTS.md for OpenCode/Codex.

Sources Metadata

Create agent-knowledge/resources/{slug}-sources.json:

{
  "topic": "{original topic}",
  "slug": "{slug}",
  "generated": "2026-02-05T12:00:00Z",
  "depth": "medium",
  "totalSources": 20,
  "sources": [
    {
      "url": "https://...",
      "title": "...",
      "qualityScore": 85,
      "scores": {
        "authority": 9,
        "recency": 8,
        "depth": 7,
        "examples": 9,
        "uniqueness": 6
      },
      "keyInsights": ["..."]
    }
  ]
}

Self-Evaluation Checklist

Before finalizing, rate output (1-10):

Metric	Question	Target
Coverage	Does guide cover main aspects?	≥7
Diversity	Are sources from diverse types?	≥6
Examples	Are code examples practical?	≥7
Accuracy	Confidence in content accuracy?	≥8

Flag gaps: Note any important subtopics not covered.

Enhancement Integration

If enhance=true, invoke after guide creation:

// Enhance the topic guide for RAG
Skill({ name: 'enhance:enhance-docs', args: `agent-knowledge/${slug}.md --ai` });

// Enhance the master index
Skill({ name: 'enhance:enhance-prompts', args: 'agent-knowledge/CLAUDE.md' });

Output Format

Return structured JSON between markers:

=== LEARN_RESULT ===
{
  "topic": "recursion",
  "slug": "recursion",
  "depth": "medium",
  "guideFile": "agent-knowledge/recursion.md",
  "sourcesFile": "agent-knowledge/resources/recursion-sources.json",
  "sourceCount": 20,
  "sourceBreakdown": {
    "officialDocs": 4,
    "tutorials": 5,
    "stackOverflow": 3,
    "blogPosts": 5,
    "github": 3
  },
  "selfEvaluation": {
    "coverage": 8,
    "diversity": 7,
    "examples": 9,
    "accuracy": 8,
    "gaps": ["tail recursion optimization not covered"]
  },
  "enhanced": true,
  "indexUpdated": true
}
=== END_RESULT ===

Error Handling

Error	Action
WebSearch fails	Retry with simpler query
WebFetch timeout	Skip source, note in metadata
<minSources found	Warn user, proceed with available
Enhancement fails	Skip, note in output
Index doesn't exist	Create new index

Token Budget

Estimated token usage by phase:

Phase	Tokens	Notes
WebSearch queries	~2,000	5-8 queries
Source scoring	~1,000	Metadata only
WebFetch extraction	~40,000	20 sources × 2,000 avg
Synthesis	~10,000	Guide generation
Enhancement	~5,000	Two skill calls
Total	~60,000	Within opus budget

Integration

This skill is invoked by:

• learn-agent for /learn command
• Potentially other research-oriented agents