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Found 34 Skills
AI content generation with OpenAI and Claude, callAIWithPrompt usage, prompt storage in app_settings, structured outputs, response format validation, multi-criteria scoring, rate limiting, JSON schema, and AI API best practices. Use when generating content, creating prompts, scoring articles, or working with OpenAI/Claude APIs.
Use when implementing on-device AI with Apple's Foundation Models framework (iOS 26+), building summarization/extraction/classification features, or using @Generable for type-safe structured output.
Monitor API integration of Parallel. Use when building applications with Parallel Monitor API.
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
Browser automation and content capture patterns for Playwright, Puppeteer, web scraping, and structured data extraction. Use when automating browser workflows, capturing web content, or extracting structured data from web pages.
Invokes Google Gemini models for structured outputs, multi-modal tasks, and Google-specific features. Use when users request Gemini, structured JSON output, Google API integration, or cost-effective parallel processing.
Integrate Perplexity API for web-grounded AI responses and search. Covers Sonar models, Search API, SDK usage (Python/TypeScript), streaming, structured outputs, filters, media attachments, Pro Search, and prompting. Keywords: Perplexity, Sonar, sonar-pro, sonar-reasoning-pro, sonar-deep-research, web search API, grounded LLM, chat completions, perplexityai SDK, image attachments, PDF analysis.
Advanced Gemini 3 Pro features including function calling, built-in tools (Google Search, Code Execution, File Search, URL Context), structured outputs, thought signatures, context caching, batch processing, and framework integration. Use when implementing tools, function calling, structured JSON output, context caching, batch API, LangChain, Vercel AI, or production features.
Implement or modify Ruby code that uses the claude-agent-sdk gem, including query() one-shot calls, Client-based interactive sessions, streaming input, option configuration, tools/permissions, hooks, SDK MCP servers, structured output, budgets, sandboxing, session resumption, Rails integration, and error handling.
Expert guidance for building production-grade AI agents and workflows using Pydantic AI (the `pydantic_ai` Python library). Use this skill whenever the user is: writing, debugging, or reviewing any Pydantic AI code; asking how to build AI agents in Python with Pydantic; asking about Agent, RunContext, tools, dependencies, structured outputs, streaming, multi-agent patterns, MCP integration, or testing with Pydantic AI; or migrating from LangChain/LlamaIndex to Pydantic AI. Trigger even for vague requests like "help me build an AI agent in Python" or "how do I add tools to my LLM app" — Pydantic AI is very likely what they need.