Autonomously research new technologies from the web and generate reusable agent skills.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionself-learningExecute the skills CLI command in your project's root directory to begin installation:
Fetches self-learning from philschmid/self-learning-skill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate self-learning. Access via /self-learning in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Autonomously research and learn new technologies from the web, then generate a reusable skill.
/learn <topic>
If <topic> is missing, show usage. If topic is ambiguous, ask to clarify:
Normalize to kebab-case for filenames.
Use web search tool to find authoritative documentation:
Search queries to try:
<topic> official documentation<topic> getting started guide<topic> API reference<topic> GitHub repositorySource prioritization:
Select 3–5 high-quality URLs maximum.
If no credible sources found, ask user to provide a URL.
For each selected URL, read the content:
Extract only relevant sections:
Skip irrelevant content:
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).
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.)
references/skill_creation_guide.md to understand the format and principles.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.
Report:
✓ Created skill: <topic>
Sources scraped: <N>
Saved to: .agent/skills/<topic>/SKILL.md
This skill will auto-trigger when working with <topic>.
search_web: Discover documentation URLsread_url_content: Extract content from static pagesbrowser_subagent: Extract content from JavaScript-heavy siteswrite_to_file: Save the generated skillMake data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Registry listing for self-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
self-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
self-learning reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: self-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for self-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
self-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
self-learning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
self-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
self-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added self-learning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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