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Found 359 Skills
Orchestrates group discussions between installed BMAD agents, enabling natural multi-agent conversations where each agent is a real subagent with independent thinking. Use when user requests party mode, wants multiple agent perspectives, group discussion, roundtable, or multi-agent conversation about their project.
Senior Multi-Agent Systems (MAS) Architect for 2026. Specialized in Model Context Protocol (MCP) orchestration, Agent-to-Agent (A2A) communication, and recursive delegation frameworks. Expert in managing complex task handoffs, shared memory state, and parallel subagent execution for high-autonomy engineering missions.
Creates and orchestrates multi-agent pipelines on the iii engine. Use when building AI agent collaboration, agent orchestration, research/review/synthesis chains, or any system where specialized agents hand off work through queues and shared state.
Architecture patterns and best practices for giving AI agents email capabilities. Use when designing how agents send, receive, and manage email conversations, building two-way communication loops, implementing human-in-the-loop approval with drafts, choosing between WebSockets and webhooks, setting up multi-agent email topologies, handling OTP and verification flows, or securing agent email against prompt injection.
Plan how to slice a non-trivial coding task across parallel subagents. Returns a dispatch plan (file assignments, dependencies, output-format contracts) — the main Agent then executes it with the Agent tool + `isolation: "worktree"`. Invoke only when work justifies multi-agent overhead: (a) greenfield 0→1 across multiple independent modules, (b) change touches ≥3 modules, or (c) ≥5 files each with >50 lines of diff. Small changes write inline.
Execute tasks through competitive multi-agent generation, meta-judge evaluation specification, multi-judge evaluation, and evidence-based synthesis
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.
Orchestrate multi-agent collaborative document synthesis through 6 phases - Divergence, Synthesis, Commentary, Consolidation, Reality Check, Final Merge. Produces authoritative founding documents from complex multi-perspective inputs. Use for constitutional documents, architecture decisions, organizational charters, or any document requiring rigorous multi-perspective synthesis. Activates on "synthesize document", "multi-agent authorship", "collaborative synthesis", "founding document", "architecture document", "recursive synthesis", "constitutional document", "multi-perspective document". NOT for simple document writing, single-author tasks, quick summaries, or documents that don't require adversarial review.
Use the unified Opper SDKs (`opperai` package for both Python and TypeScript, with built-in agent support) for AI task completion, structured output with Pydantic / Zod / JSON Schema, knowledge base semantic search, streaming, tracing, tool use, and multi-agent composition. Use this skill whenever the user is writing Python or TypeScript code that imports `opperai`, builds an Opper agent, or asks how to do anything Opper-related in code — even if they don't explicitly name the SDK. Both languages live in one repo with parallel numbered examples; agents are part of the SDK, not a separate package.
Design multi-agent harnesses for long-running autonomous coding tasks. Covers generator/evaluator loops, context reset strategy, sprint contracts, and the planner-generator-evaluator architecture from Anthropic's harness research.
Guides the agent through building LLM-powered applications with LangChain and stateful agent workflows with LangGraph. Triggered when the user asks to "create an AI agent", "build a LangChain chain", "create a LangGraph workflow", "implement tool calling", "build RAG pipeline", "create a multi-agent system", "define agent state", "add human-in-the-loop", "implement streaming", or mentions LangChain, LangGraph, chains, agents, tools, retrieval augmented generation, state graphs, or LLM orchestration.
Exploratory discussion pattern for unsolved problems. Replicate the thinking of Staff+ engineers: "When there's no clear answer, expose blind spots by confronting diverse perspectives." True multi-agent discussions where experts directly engage with each other through team-based + messaging architecture.