Remove AI-generated code slop from branches. Use after AI-assisted coding

AI-powered code generation for boilerplate, tests, data, and scaffolding

ai-collaborate-teaching

Design co-learning experiences using the Three Roles Framework (AI as Teacher/Student/Co-Worker). Use when teaching AI-driven development workflows, spec-first collaboration, or balancing AI assistance with foundational learning. NOT for curriculum without AI integration.

Use when collaborating with other AI assistants (Codex, Gemini, Aider, Cursor, OpenCode), delegating tasks, or requesting code review.

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.

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

Navigating the regulatory landscape and ethical frameworks for responsible AI development and deployment.

Responsible AI development and ethical considerations. Use when evaluating

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.

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.

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.

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.

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

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).

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.

Multimodal AI processing via Google Gemini API (2M tokens context). Capabilities: audio (transcription, 9.5hr max, summarization, music analysis), images (captioning, OCR, object detection, segmentation, visual Q&A), video (scene detection, 6hr max, YouTube URLs, temporal analysis), documents (PDF extraction, tables, forms, charts), image generation (text-to-image, editing). Actions: transcribe, analyze, extract, caption, detect, segment, generate from media. Keywords: Gemini API, audio transcription, image captioning, OCR, object detection, video analysis, PDF extraction, text-to-image, multimodal, speech recognition, visual Q&A, scene detection, YouTube transcription, table extraction, form processing, image generation, Imagen. Use when: transcribing audio/video, analyzing images/screenshots, extracting data from PDFs, processing YouTube videos, generating images from text, implementing multimodal AI features.

A complete end-to-end framework for non-technical product managers to build and ship software using AI coding agents. Use this when starting a side project, building a prototype, or automating internal tools without an engineering team.

Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.

In the AI era, product market fit must be constantly "refounded." This framework moves teams away from "blunt instruments" (long roadmaps, rigid PRDs) toward a high-velocity, hands-on approach where t

Multi-AI engineering loop orchestrating Claude, Codex, and Gemini for comprehensive validation. USE WHEN (1) mission-critical features requiring multi-perspective validation, (2) complex architectural decisions needing diverse AI viewpoints, (3) security-sensitive code requiring deep analysis, (4) user explicitly requests multi-AI review or triple-AI loop. DO NOT USE for simple features or single-file changes. MODES - Triple-AI (full coverage), Dual-AI Codex-Claude (security/logic), Dual-AI Gemini-Claude (UX/creativity).

Multi-model AI collaboration via orchestrator MCP. Use when seeking second opinions, debugging complex issues, building consensus on architectural decisions, conducting code reviews, or needing external validation on analysis.

Build a unified, ADHD-friendly web UI that consolidates 70+ CLI tools into a beautiful liquid glass interface for the AI File Organizer. Use when creating the complete frontend application that replaces all terminal interactions with a macOS-inspired dashboard for file organization, search, VEO prompts, and system management.

name: ai-pair-programming