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Found 4,864 Skills
Guide AI agents to generate complete PageObject pattern web scraper projects using Playwright and TypeScript with Docker deployment. Supports agent-browser site analysis for automated selector discovery. Keywords: scraper, playwright, pageobject, web scraping, docker, typescript, data extraction, automation.
Comprehensive SEO analysis for any website or business type. Performs full site audits, single-page deep analysis, technical SEO checks (crawlability, indexability, Core Web Vitals with INP), schema markup detection/validation/generation, content quality assessment (E-E-A-T framework per Dec 2025 update extending to all competitive queries), image optimization, sitemap analysis, and Generative Engine Optimization (GEO) for AI Overviews, ChatGPT, and Perplexity citations. Analyzes AI crawler accessibility (GPTBot, ClaudeBot, PerplexityBot), llms.txt compliance, brand mention signals, and passage-level citability. Industry detection for SaaS, e-commerce, local business, publishers, agencies. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "page speed", "structured data".
This skill should be used ONLY when the user asks to update README.md, CLAUDE.md, AGENTS.md, or CONTRIBUTING.md. Trigger phrases include "update README", "update context files", "init context", "create CLAUDE.md", "update CLAUDE.md", "update AGENTS.md", "update CONTRIBUTING". Do NOT activate this skill for any other Markdown file updates.
Autonomous ML experimentation framework by Andrej Karpathy. AI agent autonomously modifies train.py, runs 5-minute GPU experiments, evaluates with val_bpb, and commits only improvements via git ratcheting — so you wake up to 100+ experiments and a better model. Use when setting up autoresearch, writing program.md directives, interpreting results, configuring hardware, or running overnight autonomous ML experiments. Triggers on: autoresearch, autonomous ml experiments, overnight gpu experiments, karpathy autoresearch, train.py experiments, val_bpb, program.md research directives, ai runs experiments.
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
A comprehensive starting point for AI agents to work with the Ionic Framework. Covers core concepts, components, CLI, theming, layout, lifecycle, navigation, and framework-specific patterns for Angular, React, and Vue. Pair with the other Ionic skills in this collection for deeper topic-specific guidance like app creation, framework integration, and upgrades.
Guides the agent through general Ionic Framework development including core concepts, component reference, CLI usage, layout, theming, animations, gestures, development workflow, and troubleshooting. Covers all Ionic UI components grouped by category with properties, events, methods, slots, and CSS custom properties. Do not use for creating a new Ionic app (use ionic-app-creation), framework-specific patterns (use ionic-angular, ionic-react, ionic-vue), or upgrading Ionic versions (use ionic-app-upgrades).
Guides the agent through Angular-specific patterns for Ionic app development. Covers project structure, standalone vs NgModule architecture detection, Angular Router integration with Ionic navigation (tabs, side menu, modals), lazy loading, Ionic page lifecycle hooks, reactive forms with Ionic input components, Angular services for state management, route guards, performance optimization, and testing. Do not use for creating a new Ionic app from scratch, upgrading Ionic versions, general Ionic component usage unrelated to Angular, Capacitor plugin integration, or non-Angular frameworks (React, Vue).
Convert websites into LLM-ready data with Firecrawl API. Features: scrape, crawl, map, search, extract, agent (autonomous), batch operations, and change tracking. Handles JavaScript, anti-bot bypass, PDF/DOCX parsing, and branding extraction. Prevents 10 documented errors. Use when: scraping websites, crawling sites, web search + scrape, autonomous data gathering, monitoring content changes, extracting brand/design systems, or troubleshooting content not loading, JavaScript rendering, bot detection, v2 migration, job status errors, DNS resolution, or stealth mode pricing.
Essential development workflow agents for code review, debugging, testing, documentation, and git operations. Includes 7 specialized agents with strong auto-discovery triggers. Use when: setting up development workflows, code reviews, debugging errors, writing tests, generating documentation, creating commits, or verifying builds.
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.