全部能力

Multi-agent parallel orchestration for OpenClaw. Spawn AI agents as a team — parallel research, multi-model comparison, code pipelines. Proven 50-65% time…

Jeffrey Emanuel's multi-agent implementation workflow using NTM, Agent Mail, Beads, and BV. The execution phase that follows planning and bead creation.…

Use when animating multi-part objects, character appendages, fabric, hair, or any motion requiring realistic drag, momentum, and settling behavior.

Manage tasks via Overseer codemode MCP. Use when tracking multi-session work, breaking down implementation, or persisting context for handoffs.

Multi-angle codebase exploration spawning 3-5 parallel agents for code structure, data flow, architecture patterns, and health assessment. Generates ASCII…

Multi-agent workflow examples to work together on the OpenServ Platform. Covers agent discovery, multi-agent workspaces, task dependencies, and workflow…

Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when…

Use when you need multi-agent orchestration for OpenAI Codex CLI. Triggers on: omx, $plan, $ralph, $team, $autopilot, $deep-interview. v0.11.10 — 30+ agents,…

Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order,…

Multi-agent collaborative debate that produces visible round-by-round transcripts with genuine intellectual friction. All council members are custom-composed…

Rate-limit-resilient pipeline with checkpoint/resume for long multi-phase sessions. Saves progress to .claude/pipeline-state.json after each phase. Use when…

Configure the OpenTelemetry Collector with Sentry Exporter for multi-project routing and automatic project creation. Use when setting up OTel with Sentry,…

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.

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.

Expert hybrid cloud architect specializing in complex multi-cloud solutions across AWS/Azure/GCP and private clouds (OpenStack/VMware).

Multi-channel demand generation, paid media optimization, SEO strategy, and partnership programs for Series A+ startups

Multi-perspective code review that synthesizes findings from multiple reviewers into a unified report. Use when the user asks to review code changes, audit a…

Route multi-step marketing, product, or business challenges to the right sequence of ClawFu skills. Recommends which frameworks to combine, in what order, with…

Use this skill when working with the A2A (Agent-to-Agent) protocol - agent interoperability, multi-agent communication, agent discovery, agent cards, task lifecycle, streaming, and push notifications. Triggers on any A2A-related task including implementing A2A servers/clients, building agent cards, sending messages between agents, managing tasks, and configuring push notification webhooks.

Use this skill when designing AI agent architectures, implementing tool use, building multi-agent systems, or creating agent memory. Triggers on AI agents, tool calling, agent loops, ReAct pattern, multi-agent orchestration, agent memory, planning strategies, agent evaluation, and any task requiring autonomous AI agent design.