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Found 54 Skills
Extracts structured data from LLM responses using JSON schemas, Zod validation, and function calling for reliable parsing. Use when users request "structured output", "JSON extraction", "parse LLM response", "function calling", or "typed responses".
Extract structured JSON data from text using Zod schemas
Design prompts, schemas, validation, and recovery logic for reliable machine-readable model outputs. Use when generating JSON, typed objects, extraction results, tool arguments, or any output another system must parse safely.
Ann — Master Orchestrator for MEL/SRHR work. Use when Ane brings any analytical, evaluation, SRHR, or structured-output task. Ann classifies task complexity, queries the MEL Wiki, retrieves knowledge, creates an implementation plan (verifies with user for complex tasks), delegates to Vi for execution, runs a 5-point quality gate, and delivers. General-purpose — not tied to any specific project.
Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
Get AI-synthesized research on any topic with citations, directly in your terminal. Supports structured JSON output for pipelines. Use when you need comprehensive research grounded in web data without writing code.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
Use this skill when building applications with Gemini models, Gemini API, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript), model selection, and API capabilities.
Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.
Build with Claude Messages API using structured outputs for guaranteed JSON schema validation. Covers prompt caching (90% savings), streaming SSE, tool use, and model deprecations. Prevents 16 documented errors. Use when: building chatbots/agents, troubleshooting rate_limit_error, prompt caching issues, streaming SSE parsing errors, MCP timeout issues, or structured output hallucinations.
Use when designing prompts for LLMs, optimizing model performance, building evaluation frameworks, or implementing advanced prompting techniques like chain-of-thought, few-shot learning, or structured outputs.
INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.