Total 50,633 skills, AI & Machine Learning has 8484 skills
Showing 12 of 8484 skills
Set up the Claude Brain Logseq graph (first-time) or add a new project page. Triggers: "init brain", "setup brain", "init brain project <name>", "add project to brain". Don't fire for loads, saves, or status checks — those are handled by brain-load, brain-save, and brain-status.
A collection of Agent Skills for the Stitch MCP server: generate high-fidelity UI screens, create multi-page websites from a single prompt, produce DESIGN.md documentation, enhance vague UI prompts, convert designs to React/shadcn-ui components, and generate walkthrough videos via Remotion. Use when the user needs AI-assisted UI design generation, prompt refinement, or screen-to-code workflows. Triggers on: stitch, stitch-design, stitch-loop, enhance-prompt, react-components, remotion, shadcn-ui, screen generation, ui generation.
Build and operate multi-agent workflows with OpenAI Agents SDK (Python): define agents/tools/handoffs, add guardrails, run conversations, and debug orchestration behavior. Use when users ask for agent orchestration with OpenAI-native patterns, handoff routing, or production-ready agent loops.
Design enterprise-grade agent systems with Microsoft's agent framework patterns: role separation, workflow control, policy boundaries, and observability. Use when users need robust organizational agent workflows, governance, and maintainable multi-agent architecture.
Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.
Generate true pixel art sprites, tilesets, and animations with the Retro Diffusion API — the best dedicated pixel art model. Use when the user says "generate pixel art", "make a sprite with AI", "use Retro Diffusion", "rdpk", or wants high-quality pixel art faster than hand-coded sprite arrays. Requires a Retro Diffusion account and RETRODIFFUSION_API_KEY (paid credits). Great for quick prototyping of 2D Phaser games.
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
Guides engineering of multi-agent systems—agent roles and specialization, orchestration topologies (supervisor, peer-to-peer, hierarchical, blackboard), task decomposition and routing, inter-agent messaging (A2A-style patterns), shared vs partitioned state, fan-out/fan-in and DAG workflows, synchronization and consensus, conflict resolution, fault tolerance and retries across agents, cost/latency/token budgets, cross-agent observability, testing multi-agent flows, and deployment (queues, durable workflows). Framework-agnostic; high-level LangGraph, Deep Agents, and agenthub—not single-agent loops (agentic-ai-developer), ML training (ai-engineer), strategy-only whiteboard (enterprise-strategist), or PM planning (technical-program-manager). Use for multi-agent system, multi-agent engineer, agent orchestration, supervisor agent, agent topology, fan-out fan-in, agent handoff protocol, multi-agent workflow, agent coordination, blackboard pattern, hierarchical agents, A2A, agent DAG, multi-agent architecture.
Use when planning, running, comparing, or recording computational experiments, benchmarks, ablations, autonomous research loops, overnight runs, training runs, or exploratory variants.
A session continuity loop where the frog is disposable but the pad is not.
Multi-agent swarm coordination for complex tasks. Uses hierarchical topology with specialized agents to break down and execute complex work across multiple files and modules. Use when: 3+ files need changes, new feature implementation, cross-module refactoring, API changes with tests, security-related changes, performance optimization across codebase, database schema changes. Skip when: single file edits, simple bug fixes (1-2 lines), documentation updates, configuration changes, quick exploration.
Agent skill for coder - invoke with $agent-coder