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Found 518 Skills
Decompose complex tasks, design dependency graphs, and coordinate multi-agent work with proper task descriptions and workload balancing. Use this skill when breaking down work for agent teams, managing task dependencies, or monitoring team progress.
Q-learning, DQN, PPO, A3C, policy gradient methods, multi-agent systems, and Gym environments. Use for training agents, game AI, robotics, or decision-making systems.
Expert in load balancing and dynamic task allocation for multi-agent systems. Specializes in optimal routing based on agent capability, availability, and cost (Token Economics).
Multi-agent orchestration for complex tasks. Use when tasks require parallel work, multiple agents, or sophisticated coordination. Triggers include requests for features, reviews, refactoring, testing, documentation, or any work that benefits from decomposition into parallel subtasks. This skill defines how to orchestrate work using cc-mirror tasks for persistent dependency tracking and TodoWrite for real-time session visibility.
Coordinate multi-agent code review with specialized perspectives. Use when conducting code reviews, analyzing PRs, evaluating staged changes, or reviewing specific files. Handles security, performance, quality, and test coverage analysis with confidence scoring and actionable recommendations.
Create and manage AI agent sessions with multiple backends (SDK, Claude CLI, Codex, Cursor). Also supports multi-agent workflows with shared context, @mention coordination, and collaborative voting. Use for "start agent session", "create worker", "run agent", "multi-agent workflow", "agent collaboration", "test with tools", or when orchestrating AI conversations programmatically.
Multi-agent orchestration and state management.
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.
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.
Rates responses and plans against quality rubrics. Used for plan validation, response quality audits, and multi-agent consensus.
Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with multi-agent evaluation
Spec-Driven Development (SDD) methodology based on GitHub's SpecKit. Use for structured AI-assisted development with constitutional governance, phased workflows, and multi-agent coordination. Implements 7-phase process from constitution to implementation.