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Found 2,714 Skills
Use when `spec.md`, `plan.md`, and `tasks.md` exist and you need a read-only Spec Kit audit for consistency, requirement-to-task coverage, ambiguity, duplication, or constitution conflicts before implementation.
Power systems engineering covering grid modeling, power flow analysis, energy storage dispatch, demand response, and electricity market economics. Spans transmission/distribution planning to real-time operations. Use when "power flow|load flow|grid model, energy storage|battery dispatch|ESS, demand response|load management|peak shaving, electricity market|LMP|locational marginal price, grid stability|frequency|voltage, capacity planning|resource adequacy, unit commitment|economic dispatch, transmission|distribution|power system, " mentioned.
Generates comprehensive, workable unit tests for any programming language using a multi-agent pipeline. Use when asked to generate tests, write unit tests, improve test coverage, add test coverage, create test files, or test a codebase. Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more. Orchestrates research, planning, and implementation phases to produce tests that compile, pass, and follow project conventions.
Vitest test runner for JavaScript and TypeScript. Fast, modern alternative to Jest. Vite-native, ESM support, watch mode, UI mode, coverage, mocking, snapshot testing. Use when setting up tests for Vite projects, migrating from Jest, or needing fast test execution.
Analyze gaps between requirements/features that should be tested and actual test coverage, identifying testing deficiencies and prioritizing test improvements
Arquitecto de soluciones digitales basadas en IA. Dos modos: (1) ANALIZAR repositorios o código existente y explicar su arquitectura para cualquier audiencia, incluyendo personas sin conocimiento técnico. (2) DISEÑAR la arquitectura completa de sistemas nuevos que usan LLMs, RAG, agentes o fine-tuning. Usa este skill cuando el usuario mencione: arquitectura de IA, diseño de sistema con LLM, capas arquitectónicas, RAG architecture, tech stack para IA, vector database, diagrama de arquitectura, componentes del sistema, embedding, retrieval, pipeline de datos, MLOps, LLMOps, evaluar enfoques, RAG vs fine-tuning, diseñar solución de inteligencia artificial, explicar repositorio, explicar código, analizar proyecto, qué hace este repo, cómo funciona este sistema, explícame este proyecto, o cualquier variación de "qué componentes necesito" o "explícame cómo funciona esto". Actívalo cuando el usuario pegue código, README, estructura de archivos, o mencione un repositorio de GitHub para analizar. También cuando quiera diseñar arquitectura nueva.
Graph-informed mutation testing triage. Parses codebases with Trailmark, runs mutation testing and necessist, then uses survived mutants, unnecessary test statements, and call graph data to identify false positives, missing test coverage, and fuzzing targets. Use when triaging survived mutants, analyzing mutation testing results, identifying test gaps, finding fuzzing targets from weak tests, running mutation frameworks (including circomvent and cairo-mutants), or using necessist.
Dense vector embeddings, semantic search, RAG pipelines, and reranking via Together AI. Generate embeddings with open-source models and rerank results behind dedicated endpoints. Reach for it whenever the user needs vector representations or retrieval quality improvements rather than direct text generation.
Build semantic search with Cloudflare Vectorize V2. Covers async mutations, 5M vectors/index, 31ms latency, returnMetadata enum changes, and V1 deprecation. Prevents 14 errors including dimension mismatches, TypeScript types, testing setup. Use when: building RAG or semantic search, troubleshooting returnMetadata, V2 timing, metadata index, dimension errors, vitest setup, or wrangler --json output.
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
Motion (Framer Motion) React animation library. Use for drag-and-drop, scroll animations, gestures, SVG morphing, or encountering bundle size, complex transitions, spring physics errors.
Use when reviewing or scoring AI-generated unit tests/UT code, especially when coverage, assertion effectiveness, or test quality is in question and a numeric score, risk level, or must-fix checklist is needed