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Found 418 Skills
Multi-agent systems with LangGraph - supervisor/swarm/handoff/router patterns, state coordination, Deep Agents, guardrails, testing, observability, deployment. Use when building multi-agent workflows, coordinating agents, or need cost-optimized orchestration. Uses Claude, DeepSeek, Gemini (no OpenAI).
Expertise in using open-multi-agent, a TypeScript framework for building production-grade multi-agent AI teams with task scheduling, dependency graphs, and inter-agent communication.
Evaluate options for a specific design decision node and recommend one with explicit trade-offs. Use when the design already exposes a concrete choice such as architecture style, state management approach, auth model, storage pattern, sync strategy, multi-agent coordination model, language or runtime, UI framework, data-layer library, or tooling selection. Trigger when the user needs structured comparison and recommendation for a bounded design decision. Do not use for broad design discovery, full-system decomposition, or final readiness review.
Design and coordinate multi-agent systems where specialized agents work together to solve complex problems. Covers agent communication, task delegation, workflow orchestration, and result aggregation. Use when building coordinated agent teams, complex workflows, or systems requiring specialized expertise across domains.
Generate declarative multi-agent systems (MAS) using POMASA pattern language. Use when building agent pipelines, orchestrating multiple AI agents, or creating research automation workflows. Supports patterns like Prompt-Defined Agent, Orchestrated Pipeline, Filesystem Data Bus, and Verifiable Data Lineage.
Generates production-ready FastGPT workflow JSON from natural language requirements. Uses AI-powered semantic template matching from built-in workflows (document translation, sales training, resume screening, financial news). Performs three-layer validation (format, connections, logic completeness). Supports incremental modifications to add/remove/modify nodes. Activates when user asks to "create FastGPT workflow", "generate workflow JSON", "design FastGPT application", or mentions workflow automation, multi-agent systems, or FastGPT templates.
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).
Guide for orchestrating Claude Code agent teams — multiple parallel Claude Code sessions coordinated by a team lead. Use this skill when the user mentions agent teams, teammates, parallel agents, multi-agent workflows, spawning agents, coordinating agents, delegate mode, plan approval for teammates, TeammateIdle or TaskCompleted hooks, or wants to break a task into parallel independent work streams. Also trigger on questions about tmux split-pane mode, in-process teammate mode, Shift+Up/Down agent switching, shared task lists, inter-agent messaging, or designing tasks for multi-agent decomposition. This is an experimental feature requiring CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS to be enabled.
Execute orchestrate multi-agent systems with handoffs, routing, and workflows across AI providers. Use when building complex AI systems requiring agent collaboration, task delegation, or workflow coordination. Trigger with phrases like "create multi-agent system", "orchestrate agents", or "coordinate agent workflows".
Coordinate complex work using a phase-gated, multi-agent engineering loop (audit → design → implement → review → validate → deliver). Use when you need to split a task into subsystems, run dual independent audits, reconcile findings into a confirmed issue list, delegate fixes in clusters, enforce dual-review PASS gates, and drive an end-to-end delivery. Prefer discovering and invoking other specialized skills when they can execute part of the work faster or more reliably.
Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.