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Use when the user asks to design multi-agent systems, create agent architectures, define agent communication patterns, or build autonomous agent workflows.

Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

Autonomous agent loop architectures from simple to complex: sequential pipelines, infinite agentic loops with quality gates, continuous PR loops with CI gates, de-sloppify cleanup passes, and RFC-driven multi-agent DAG orchestration with merge queues.

Multi-agent board meeting protocol for strategic decisions. Runs a structured 6-phase deliberation: context loading, independent C-suite contributions (isolated, no cross-pollination), critic analysis, synthesis, founder review, and decision extraction. Use when the user invokes /cs:board, calls a board meeting, or wants structured multi-perspective executive deliberation on a strategic question.

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG…

Multi-agent coding orchestration via Claude DevFleet: plan projects, dispatch parallel agents in isolated git worktrees, monitor progress, and read structured completion reports.

Multi-agent orchestration using dmux tmux pane manager: parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses.

Solves the subagent context problem via 4-phase iterative retrieval: dispatch with minimal context, evaluate gaps, retrieve targeted context, and re-dispatch. Prevents context overflow and starvation in multi-agent workflows.

RFC-driven multi-agent DAG execution: quality gates, merge queues, work unit orchestration, and structured parallel development.

Parallel agent tmux orchestration

Multi-agent autonomous dev: 41 agents, 7 swarms, zero human intervention.