Methodology and templates for effective AI consultation workflows with external AI tools like Codex and Gemini.
Skills（SKILL.md）は、AIエージェント（Claude Code、Cursor、Codexなど）に特定の能力を追加するための設定ファイルです。
詳しく見る →Methodology and templates for effective AI consultation workflows with external AI tools like Codex and Gemini.
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.
You are a specialized cross-validation assistant that uses Google's Gemini 2.5 Pro API to provide independent, multi-perspective code validation alongside Claude's analysis.
Cross-verify Claude-generated plans and code using OpenAI Codex and Google Gemini CLI. Provides code review, plan validation, and comparative analysis. Use when needing second opinions on Claude's code or plans, validating technical decisions, or seeking consensus from multiple AI models.
Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create visualizations, or build predictive models with reproducible, code-based workflows.
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
Protecting personal and sensitive data throughout the machine learning lifecycle, from training to inference.
Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.
Synchronize and update Claude Code and GitHub Copilot development tool configurations to work similarly. Use when asked to update Claude Code setup, update Copilot setup, sync AI dev tools, add new skills/prompts/agents across both platforms, or ensure Claude and Copilot configurations are aligned. Covers skills, prompts, agents, instructions, workflows, and chat modes.
Use when deciding between HITL, OHOTL, and AHOTL modes in AI-DLC workflows. Covers decision frameworks for human involvement levels and mode transitions.
This skill should be used when writing, reviewing, or refactoring documentation that will be consumed as AI context. Optimizes documentation for LLM comprehension using principles of completeness, efficiency, and zero fluff—replacing prose with structured data, enforcing heading hierarchy, detecting meta-commentary, and validating that examples serve a purpose.
shadcn/ui AI chat components for conversational interfaces. Use for streaming chat, tool/function displays, reasoning visualization, or encountering Next.js App Router setup, Tailwind v4 integration, AI SDK v5 migration errors.
AI Elements component library for AI-native applications. Use when building chatbots, AI workflows, or integrating with Vercel AI SDK's useChat hook.
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use when building LLM features, chatbots, AI-powered applications, or need guidance on AI/ML engineering patterns.
Expert-level AI implementation, deployment, LLM integration, and production AI systems
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
Build production-ready LLM applications, advanced RAG systems, and
Practical guide for building production ML systems based on Chip Huyen's AI Engineering book. Use when users ask about model evaluation, deployment strategies, monitoring, data pipelines, feature engineering, cost optimization, or MLOps. Covers metrics, A/B testing, serving patterns, drift detection, and production best practices.
Navigating the regulatory landscape and ethical frameworks for responsible AI development and deployment.
Responsible AI development and ethical considerations. Use when evaluating
In traditional software, inputs and outputs are defined. In AI, inputs and outputs are fuzzy. Evals (evaluations) are the "unit tests" for AI products. They allow you to move from "vibes-based" develo
Help users create and run AI evaluations. Use when someone is building evals for LLM products, measuring model quality, creating test cases, designing rubrics, or trying to systematically measure AI output quality.
Create an AI Evals Pack (eval PRD, test set, rubric, judge plan, results + iteration loop). Use for LLM evaluation, benchmarks, rubrics, error analysis/open coding, and ship/no-ship quality gates for AI features.
This project uses xAI's Grok model for AI-powered features with X (Twitter) search capabilities.
Analyze Adobe Illustrator (.ai) files to extract design information including text content, fonts, color palettes, vector paths, and generate high-resolution preview images. Use when analyzing logo files, design assets, or any Adobe Illustrator documents that need programmatic inspection.
Build AI gateway services for routing and managing LLM requests. Use when implementing API proxies, rate limiting, or multi-provider AI services.
External AI API integration with retry logic, rate limiting, content safety detection, and multi-turn conversation support for image generation.
AI governance and compliance guidance covering EU AI Act risk classification, NIST AI RMF, responsible AI principles, AI ethics review, and regulatory compliance for AI systems.
Use AI to recreate PDF page as semantic HTML. Consumes three inputs (PNG image, parsed text, ASCII preview) for complete contextual understanding and accurate generation.
This skill should be used when generating AI image assets for websites, landing pages, or applications. It automatically analyzes page requirements, generates images using Gemini API, removes backgrounds, converts to SVG for interactivity, and places assets in frontend code. Ideal for creating hero images, icons, backgrounds, product mockups, and infographic elements. Use this skill when users need image assets for their web projects.
Generate and edit images using either OpenAI GPT Image 1.5 or Google's Nano Banana Pro (Gemini 3 Pro Image). Use when the user asks to generate/create/edit/modify images. Supports image-to-image editing for both providers and optional mask-based inpainting for OpenAI.
Generate AI images using OpenAI's gpt-image-1 model with customizable aspect ratios and artistic themes. Use when the user wants to create images, generate artwork, or mentions image generation with specific styles like Ghibli, futuristic, Pixar, oil painting, or Chinese painting.
Integrate AI tools and APIs into business workflows and applications
Chat endpoints, embeddings, RAG workflows, vector search
ai-llm-engineering
Operational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving at scale. Emphasizes production-grade performance, cost control, and observability.
Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.
Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference serving, and lifecycle operations with cost/safety controls.
Manage AI agents through the AI Maestro CLI. This skill provides commands for creating, updating, deleting, hibernating, and waking agents. It also handles plugin management and agent import/export.
AI-powered marketing engineering skill based on Alon Huri's framework. Transforms marketing from copywriting to engineering discipline through 10 agentic mechanisms: infinite creative generation, adaptive budget management, LTV signal hunting, contextual data layers, AEO optimization, dynamic quizzes, behavior-driven activation, personalized video at scale, competitor weakness targeting, and active churn prevention. Use when building marketing automation systems, designing growth engineering workflows, creating AI-powered marketing agents, optimizing ad creatives at scale, implementing AEO (Answer Engine Optimization), or architecting data-driven marketing infrastructure.
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
AI and ML expert including PyTorch, LangChain, LLM integration, and scientific computing
AI/ML APIs, LLM integration, and intelligent application patterns
Operational patterns, templates, and decision rules for time series forecasting (modern best practices): tree-based methods (LightGBM), deep learning (Transformers, RNNs), future-guided learning, temporal validation, feature engineering, generative TS (Chronos), and production deployment. Emphasizes explainability, long-term dependency handling, and adaptive forecasting.
Production MLOps and ML/LLM/agent security skill for deploying and operating ML systems in production (registry + CI/CD, serving, monitoring/drift, evaluation loops, incident response/runbooks, and governance), including GenAI security (prompt injection, jailbreaks, RAG security, privacy, and supply chain).
A production-ready pattern for integrating AI models (specifically Google Gemini) with automatic fallback, retry logic, structured output via Zod schemas, and comprehensive error handling. Use when integrating AI/LLM APIs, need automatic fallback when models are overloaded, want type-safe structured responses, or building features requiring reliable AI generation.
AI 모델 API 호출명 및 가격 참조 가이드. API 키로 AI 모델을 호출할 때 정확한 모델명(model string)과 최신 가격 정보를 제공합니다. 사용 시점: (1) OpenAI, Anthropic, Google, DeepSeek 등의 API 호출 시 모델명이 필요할 때, (2) 토큰 비용/가격 비교가 필요할 때, (3) 최신 추론 모델/FAST 모델/가성비 모델 선택이 필요할 때, (4) 프롬프트 캐싱/배치 처리 비용 최적화가 필요할 때
Gère les modèles IA de Motivia. Utilise ce skill quand l'utilisateur demande d'ajouter un nouveau modèle IA, modifier un provider, ou configurer les options de génération. Supporte OpenAI, Anthropic, Google, Mistral et xAI.
Process and generate multimedia content using Google Gemini API for better vision capabilities. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (better image analysis than Claude models, captioning, reasoning, object detection, design extraction, OCR, visual Q&A, segmentation, handle multiple images), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image with Imagen 4, editing, composition, refinement), generate videos (text-to-video with Veo 3, 8-second clips with native audio). Use when working with audio/video files, analyzing images or screenshots (instead of default vision capabilities of Claude, only fallback to Claude's vision capabilities if needed), processing PDF documents, extracting structured data from media, creating images/videos from text prompts, or implementing multimodal AI features. Supports Gemini 3/2.5, Imagen 4, and Veo 3 models with context windows up to 2M tokens.