Pick an orchestration topology (supervisor, swarm, hierarchical, debate, or none) for a given problem and implement it minimally. Use when you need help with orchestration picker.
Installs into .claude/skills of the current project.
Are you the author of Orchestration Picker?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/anubhavg-icpl-orchestration-picker)
---
name: orchestration-picker
description: Pick an orchestration topology (supervisor, swarm, hierarchical, debate, or none) for a given problem and implement it minimally. Use when you need help with orchestration picker.
license: CC-BY-NC-SA-4.0
phase: 14
lesson: 28
metadata:
version: 1.0.0
tags: [orchestration, supervisor, swarm, hierarchical, debate]
---
Given a product domain and a task class, pick the minimal topology.
Decision:
1. 1 agent + workflow patterns (Lesson 12) suffice? -> don't use topology at all.
2. 2-4 specialists with distinct responsibilities? -> **supervisor-worker**.
3. Latency-critical and specialists can cleanly hand off? -> **swarm**.
4. 10+ specialists, supervisor context budget failing? -> **hierarchical**.
5. Accuracy matters more than cost, multi-proposer + critique helps? -> **debate** (Lesson 25).
Produce:
1. The chosen topology scaffold.
2. Hop counter on swarm; nesting depth limit on hierarchical; round cap on debate.
3. Observability hooks per handoff or per step (OTel GenAI spans, Lesson 23).
4. A "why this, not that" README section.
Hard rejects:
- Calling 3 LLM calls in sequence "multi-agent." That's a prompt chain.
- Swarm without hop counter. Bouncing is a certainty.
- Hierarchical that bottoms out at 1 specialist per branch. Flatten.
Refusal rules:
- If the user wants multi-agent for a task that a single ReAct loop handles, refuse and suggest Lesson 01.
- If the user wants supervisor for a 2-step task, refuse and suggest prompt chaining (Lesson 12).
- If the domain has compliance / audit requirements, refuse swarm and suggest supervisor or hierarchical.
Output: topology scaffold + README with decision rationale. End with "what to read next" pointing to Lesson 13 (LangGraph) for supervisor implementation, Lesson 16 (OpenAI Agents SDK) for handoffs-as-tools, or Lesson 25 for debate specifics.
