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Found 270 Skills
Build production-ready MCP clients in TypeScript or Python. Handles connection lifecycle, transport abstraction, tool orchestration, security, and error handling. Use for integrating LLM applications with MCP servers.
The orchestration layer for AI-native creative production. This skill coordinates multiple AI tools—video, image, audio, digital humans, effects—into cohesive campaigns, productions, and creative systems. As AI tools proliferate, the challenge shifts from "can we create this?" to "how do we orchestrate these capabilities into something coherent?" The AI Creative Director thinks in systems, not tools. In pipelines, not one-offs. In brand consistency across AI-generated assets. This is where creative vision meets technical orchestration. The AI Creative Director doesn't just use AI tools—they compose them into creative instruments that produce at scales and speeds previously impossible. Use when "AI creative director, orchestrate AI, AI campaign, multi-tool, AI workflow, AI pipeline, coordinate AI, AI production, AI creative system, full AI production, AI at scale, orchestration, creative-direction, ai-production, workflow, pipeline, multi-tool, scale, quality-control" mentioned.
Repo Updater - Multi-repo synchronization with AI-assisted review orchestration. Parallel sync, agent-sweep for dirty repos, ntm integration, git plumbing. 17K LOC Bash CLI.
AI content generation suite with 35+ models. Image generation, video creation, audio processing via FAL AI, Google Vertex AI, ElevenLabs. Pipeline orchestration and cost management.
Expert MCP (Model Context Protocol) orchestration with n8n workflow automation. Master bidirectional MCP integration, expose n8n workflows as AI agent tools, consume MCP servers in workflows, build agentic systems, orchestrate multi-agent workflows, and create production-ready AI-powered automation pipelines with Claude Code integration.
Dynamic orchestration engine that plans multi-step agent work as DAGs with Mermaid visualization.
When the user wants to build GTM automation with code, design workflow architectures, use AI agents for GTM tasks, or implement the 'architecture over tools' principle. Also use when the user mentions 'GTM engineering,' 'GTM automation,' 'n8n,' 'Make,' 'Zapier,' 'workflow automation,' 'Clay API,' 'instruction stacks,' 'AI agents for GTM,' or 'revenue automation.' This skill covers technical GTM infrastructure from workflow design through agent orchestration.
Use when user says "create workflow", "create a workflow", "design workflow", "orchestrate", "automate multiple steps", "coordinate agents", "multi-agent workflow". Creates orchestration workflows from natural language using Socratic questioning to plan multi-agent workflows with visualization.
Generate Chi HTTP handlers following GO modular architechture conventions (request/response DTOs, use case orchestration, error handling, swagger annotations, Fx DI). Use when creating HTTP endpoint handlers in internal/modules/<module>/http/chi/handler/ for REST operations (List, Create, Update, Delete, Get) that need to decode requests, call use cases, map responses, and handle errors with proper logging and tracing.
Implement real-time Hotwire behavior: Turbo Streams over WebSocket/SSE, custom stream actions, inline stream tags, live list updates, and cross-tab state synchronization. Prefer this skill when the core problem is push-based updates or stream action orchestration. Use hwc-navigation-content for pull-based pagination/tab/lazy-navigation flows, hwc-forms-validation for form lifecycle and validation, hwc-media-content for media upload/playback behavior, hwc-ux-feedback for generic loading/progress/transitions, and hwc-stimulus-fundamentals for non-stream Stimulus fundamentals.
Multi-agent orchestration workflow for deep research: Split a research objective into parallel sub-objectives, run sub-processes using Claude Code non-interactive mode (`claude -p`); prioritize installed skills for network access and data collection, followed by MCP tools; aggregate sub-results with scripts and refine them chapter by chapter, and finally deliver "finished report file path + summary of key conclusions/recommendations". Applicable scenarios: systematic web/data research, competitor/industry analysis, batch link/dataset shard retrieval, long-form writing and evidence integration, or scenarios where users mention "deep research/Deep Research/Wide Research/multi-agent parallel research/multi-process research".
Generate comprehensive documentation with intelligent orchestration and parallel execution