self-learning

philschmid/self-learning-skill · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/philschmid/self-learning-skill --skill self-learning
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summary

Autonomously research new technologies from the web and generate reusable agent skills.

  • Discovers authoritative documentation via web search, prioritizing official docs, GitHub repositories, and blogs
  • Extracts installation, core concepts, API references, and code examples from 3–5 high-quality sources
  • Generates a self-contained skill with YAML frontmatter, markdown instructions, and optional bundled resources (scripts, references, assets)
  • Saves skills to workspace-specific or glob
skill.md

Self-Learning Skill Generator

Autonomously research and learn new technologies from the web, then generate a reusable skill.

Usage

/learn <topic>

If <topic> is missing, show usage. If topic is ambiguous, ask to clarify:

  • "react" → "React for web, React Native, or a specific library like react-query?"
  • "apollo" → "Apollo GraphQL client, Apollo Server, or Apollo Federation?"
  • "aws" → "Which AWS service? (S3, Lambda, DynamoDB, etc.)"

Normalize to kebab-case for filenames.

2. Discover Sources (Web Search)

Use web search tool to find authoritative documentation:

Search queries to try:

  1. <topic> official documentation
  2. <topic> getting started guide
  3. <topic> API reference
  4. <topic> GitHub repository

Source prioritization:

  1. Official docs sites (e.g., docs.*, *.dev)
  2. Official GitHub repositories (README, /docs)
  3. Official blogs/announcements

Select 3–5 high-quality URLs maximum.

If no credible sources found, ask user to provide a URL.


3. Extract Content (URL Reading)

For each selected URL, read the content:

Extract only relevant sections:

  • Installation / setup
  • Core concepts
  • API reference / key functions
  • Common patterns / examples
  • Version information

Skip irrelevant content:

  • Navigation, ads, login prompts
  • Unrelated sidebar content
  • Comments, forums

If reading the content fails (JavaScript-heavy sites), fall back to browser agent:

Task: Navigate to <URL> and extract the main content including:
- Installation instructions
- Core concepts and API reference
- Code examples
Return the extracted content as markdown.

Record scrape timestamp for each source (use current date: YYYY-MM-DD format).


4. Generate Skill

Skills are modular, self-contained packages. Every skill consists of a required SKILL.md file and optional bundled resources:

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter metadata (required)
│   │   ├── name: (required)
│   │   └── description: (required)
│   └── Markdown instructions (required)
└── Bundled Resources (optional)
    ├── scripts/          - Executable code (Python/Bash/etc.)
    ├── references/       - Documentation intended to be loaded into context as needed
    └── assets/           - Files used in output (templates, icons, fonts, etc.)
  1. Read references/skill_creation_guide.md to understand the format and principles.
  2. Synthesize the learned and extracted information into a new skill.
    • Trigger: Write a description that clearly defines when to use it.
    • Workflow: Create step-by-step instructions.
    • Format: Ensure valid YAML frontmatter and proper file structure.

5. Save the Skill

Antigravity supports two types of skills, save a global-workspace if asked.

  • .agent/skills/<skill-folder>/ Workspace-specific
  • ~/.gemini/antigravity/skills/<skill-folder>/ Global (all workspaces)

Create directory if it doesn't exist, warn user before overwriting existing skill.


6. Confirm to User

Report:

✓ Created skill: <topic>
  Sources scraped: <N>
  Saved to: .agent/skills/<topic>/SKILL.md
  This skill will auto-trigger when working with <topic>.

Tool Reference

  • search_web: Discover documentation URLs
  • read_url_content: Extract content from static pages
  • browser_subagent: Extract content from JavaScript-heavy sites
  • write_to_file: Save the generated skill

Critical Rules

  1. Never hallucinate documentation: Only include information from scraped sources.
  2. Never invent APIs: If documentation is unclear, ask the user what to do.
  3. Ask for URLs: If automated discovery fails, ask user for specific URLs.
  4. Verify sources: Prefer official sources over third-party tutorials.
how to use self-learning

How to use self-learning on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add self-learning
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/philschmid/self-learning-skill --skill self-learning

The skills CLI fetches self-learning from GitHub repository philschmid/self-learning-skill and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/self-learning

Reload or restart Cursor to activate self-learning. Access the skill through slash commands (e.g., /self-learning) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.638 reviews
  • Arya Sanchez· Dec 24, 2024

    Registry listing for self-learning matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Dev Mehta· Dec 8, 2024

    self-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Lucas Okafor· Nov 27, 2024

    self-learning reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Tariq Rao· Nov 15, 2024

    Keeps context tight: self-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Jin Mensah· Oct 18, 2024

    Registry listing for self-learning matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Tariq Srinivasan· Oct 6, 2024

    self-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Zara Sharma· Sep 25, 2024

    self-learning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Oshnikdeep· Sep 17, 2024

    self-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Alexander Bansal· Sep 13, 2024

    self-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Alexander Agarwal· Aug 16, 2024

    We added self-learning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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