Loading...
Loading...
Found 338 Skills
Advanced context engineering techniques for AI agents. Token-efficient plugins improving output quality through structured reasoning, reflection loops, and multi-agent patterns.
Multi-agent coordination expert for agent-swarm MCP. Use when the user asks about swarm coordination, delegating tasks to agents, checking swarm status, agent messaging, or managing multi-agent workflows.
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.
Multi-agent content workflow for social media. Use when creating content ideas, validating concepts, planning production, checking trends, or aligning with brand voice. Triggers on content ideas, TikTok/Instagram/YouTube, validation, trends.
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".
Inter-agent communication patterns including message passing, shared memory, blackboard systems, and event-driven architectures for LLM agentsUse when "agent communication, message passing, inter-agent, blackboard, agent events, multi-agent, communication, message-passing, events, coordination" mentioned.
Coordinate AI agent teams via a Kanban task board with local JSON storage. Enables multi-agent workflows with a Team Lead assigning work and Worker Agents executing tasks via heartbeat polling. Perfect for building AI agent command centers.
LangGraph workflow patterns for state management, routing, parallel execution, supervisor-worker, tool calling, checkpointing, human-in-loop, streaming, subgraphs, and functional API. Use when building LangGraph pipelines, multi-agent systems, or AI workflows.
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.
One-click initialization of a multi-agent repository from the Antigravity template. Use this skill when users want to scaffold a new project quickly (`quick` mode) or with runtime defaults (`full` mode) including LLM provider profile, MCP toggle, swarm preference context, sandbox type, and optional git init.
Researches topics and trends for blog content with parallel multi-agent execution. USE WHEN orchestrator invokes research phase OR user says 'research topic', 'find trends', 'gather information for blog'.
Collaborative multi-agent planning with iterative deliberation. Use when creating complex plans that benefit from multiple specialist perspectives, cross-review, and consensus-building through discussion rounds.