
Learn
- 84 installs
- 931 repo stars
- Updated July 26, 2026
- avifenesh/agentsys
learn is a Claude Code skill that researches a topic online and creates a structured, RAG-optimized learning guide and index for an agent to reference.
About
learn researches a topic online and turns the gathered sources into a structured learning guide plus RAG-optimized indexes an agent can later reference. It uses a progressive broad-to-deep query strategy, scores each source on authority, recency, depth, examples and uniqueness, then fetches only the top sources to extract insights. A developer uses it to build a project knowledge base under agent-knowledge/ that Claude reads when answering questions about those topics. Depth is configurable to 10, 20 or 40 sources.
- Researches a topic online and writes a RAG-optimized learning guide the agent can reference
- Scores sources on authority, recency, depth, examples and uniqueness before fetching
- Builds a master index (CLAUDE.md/AGENTS.md) with trigger phrases and quick lookup
Learn by the numbers
- 84 all-time installs (skills.sh)
- Ranked #5,072 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
learn capabilities & compatibility
- Capabilities
- deep research · web search
- Use cases
- research · web search · documentation
What learn says it does
Research any topic by gathering online resources and creating a comprehensive learning guide with RAG-optimized indexes.
brief`: 10 sources (quick overview)
Don't pre-load all content (causes context rot):
npx skills add https://github.com/avifenesh/agentsys --skill learnAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 84 |
|---|---|
| repo stars | ★ 931 |
| Last updated | July 26, 2026 |
| Repository | avifenesh/agentsys ↗ |
What it does
Research a topic online and generate a RAG-optimized learning guide plus index for an agent to reference.
Who is it for?
Building a persistent, source-scored knowledge base an agent can reference later.
Skip if: Copying verbatim source text, since it stores summaries and insights only.
When should I use this skill?
The user asks to learn about, research a topic, or build a knowledge base.
What you get
- Learning guide markdown, master index, and sources metadata JSON under agent-knowledge/
By the numbers
- Three depth tiers gather 10, 20 or 40 sources
- Scores sources on 5 weighted factors up to 100 points
Files
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
// 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]({url}) | Official Docs | Authoritative reference |
| [Title]({url}) | 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:
# 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 sourcesCopy 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-docs', args: `agent-knowledge/${slug}.md --ai` });
// Enhance the master index
Skill({ name: '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-agentfor/learncommand- Potentially other research-oriented agents
Related skills
Forks & variants (1)
Learn has 1 known copy in the catalog totaling 2 installs. They canonicalize to this original listing.
- avifenesh - 2 installs