Design a small team of AI agents to tackle a complex task in parallel — who does what, how they hand off, and how to keep them coordinated — instead of one overloaded agent doing everything serially. Use when asked how do I use multiple AI agents, set up an agent team, orchestrate agents for, or run agents in parallel. Produces a decomposition of the task into agent roles, a coordination pattern (parallel vs sequential, how outputs combine), the context each agent needs (and what to keep isolated), a review/quality step, and the guardrails to keep it from going off the rails — practical multi-agent design for real tasks.
“How do I use multiple AI agents to build this?”“Set up an agent team to research and write this report.”“Orchestrate several agents for this complex task.”“Should this be one agent or a team, and how do I structure it?”“Design a parallel agent workflow for this.”
What to give it
▸The task — the complex thing you want a team to tackle
▸Your setup — the AI tool/framework you're using (Claude Code sub-agents, an agent framework, or manual multi-chat)
▸The subtasks — the natural pieces, if you can see them
▸Quality bar & stakes — how much the output matters (drives the review rigor)
▸Constraints — cost, time, and how much human oversight you want
✅ The bar it holds itself to
Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.
✓Checks whether a team is actually warranted (vs one agent)
✓Decomposes into focused agent roles
✓Deliberately designs isolated vs shared context (the key advantage)
✓Defines the coordination pattern and how outputs combine
✓Includes a review/integration pass
✓Adds guardrails against drift and runaway loops
⚠️ What it refuses to do
**Using a team** for a task one agent handles better.
**Agents with muddy, shared context** (loses the whole advantage).