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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Edit existing images with text prompts using fal.ai Gemini 3 Pro. Use when the user wants to modify, edit, transform, or change an existing image based on a text description. Supports multiple input images for context.
Maintain AGENTS.md files across a repo: find and review every AGENTS.md, apply hygiene rules (fix outdated or incorrect guidance, reduce verbosity, split large sections into docs and link them), add newly learned context from the current conversation, and provide a concise enumerated update summary. Use when asked to update AGENTS files, run/sweep the agents update skill, or when AGENTS.md maintenance is required across a repository or subfolder.
MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows
Creates new AI agent skills following the Agent Skills spec. Trigger: When user asks to create a new skill, add agent instructions, or document patterns for AI.
Multi-source parallel research using Perplexity, Gemini, and Claude. Three modes - Quick (1 query), Standard (3 queries), Extensive (8 queries). USE WHEN user says 'research', 'investigate', 'find out about', 'do research on', or requests comprehensive information gathering.
Expert guidance for creating Claude Code slash commands. Use when working with slash commands, creating custom commands, understanding command structure, or learning YAML configuration.
Amazon Bedrock AgentCore Evaluations for testing and monitoring AI agent quality. 13 built-in evaluators plus custom LLM-as-Judge patterns. Use when testing agents, monitoring production quality, setting up alerts, or validating agent behavior.
Stream Deck integration assistant for VSCode. Create, manage, and organize Stream Deck profiles, buttons, snippets, and scripts. Helps build productivity workflows for developers using Stream Deck with Claude Code and TAC (Tactical Agentic Coding) patterns.
Provides real-time weather forecasts using OpenWeatherMap API. Use when users ask about current or future weather conditions, temperature, precipitation, or weather-dependent planning for any location (e.g., 'What's the weather in Paris tomorrow?', 'Will it rain in Seattle this weekend?', 'Should I bring a jacket to Denver?').
Copilot agent that assists with machine learning model development, training, evaluation, deployment, and MLOps Trigger terms: machine learning, ML, AI, model training, MLOps, model deployment, feature engineering, model evaluation, neural network, deep learning Use when: User requests involve ai ml engineer tasks.
Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
Generate AI-friendly Python CLIs using Click, Pydantic, and uv. Use when user wants to create a new CLI tool that follows best practices for agentic coding environments.
Use this whenever a user wants to add a new feature or explitly states to research a feature/API or building a plan for a new feature. It iterviews the user for feature details (if not provided), research the best API/service for their needs, confirm choice, then gather all implementation notes for their request and save them as a .claude/plans file.
Create and maintain reusable skills for multiple agent platforms (OpenCode, Claude, Antigravity, Copilot). Use when defining or updating SKILL.md files that extend agent capabilities with workflows, tool integrations, or domain expertise.
This skill should be used when the user asks to "train a model", "fine-tune", "build NLP model", "create training task", "optimize model performance", "improve accuracy", "what model should I use", or expresses vague training needs like "I want to do sentiment analysis" or "help me with NER". Provides coaching-style guidance to clarify goals, diagnose pain points, and recommend optimal training approaches.
Phase 2 of Ontology Builder Pipeline. AI acts as domain SME to analyze raw inputs, extract entities/workflows/rules, fill knowledge gaps using market expertise. Use after Phase 1 ingestion is complete.
Google File Search API templates, configuration patterns, and usage examples for managed RAG with Gemini. Use when building File Search integrations, implementing RAG with Google AI, chunking documents, configuring grounding citations, or when user mentions Google File Search, Gemini RAG, document indexing, or semantic search.
