Multi Agent Collaboration Orchestrator
Design multi-agent workflows with roles, task decomposition, shared state, handoffs, verification, budgets, conflict handling, and human control.
Multi Agent Collaboration Orchestrator is a free AI skill for agent systems & llm workflows. Design multi-agent workflows with roles, task decomposition, shared state, handoffs, verification, budgets, conflict handling, and human control. It works with Claude, ChatGPT and is ready to use out of the box.
About this skill
Multi Agent Collaboration Orchestrator helps teams determine when multiple specialized agents are useful and how they should cooperate. It defines roles, authority, task routing, shared context, communication, verification, conflict resolution, budgets, stopping conditions, observability, and human approval.
What it does
The skill analyzes the goal, task dependencies, tools, risk, data, and evaluation needs; compares single-agent and multi-agent options; designs the collaboration protocol; and produces an implementation and test plan that avoids unnecessary agent complexity.
What is included
- Single versus multi-agent decision
- Agent role definitions
- Task and authority model
- Shared state and context
- Handoff protocol
- Verification and conflict resolution
- Budgets and stop rules
- Evaluation and observability
How to use it
1. Download the multi-agent-collaboration-orchestrator-SKILL.md file 2. Upload it to your AI workspace 3. Provide the objective, tasks, tools, data, risks, and evaluation criteria 4. Add budget and human-control requirements 5. Use the design to implement and test the workflow
Examples
Design a multi-agent system for researching a market, evaluating evidence, building a business case, and producing an executive report.
A complete orchestration design with planner, researcher, verifier, analyst, and editor roles, shared state, handoffs, evidence rules, conflict resolution, budgets, human checkpoints, evaluation, and failure recovery.
FAQ
What is this skill for?
Are multiple agents always better?
How do agents share information?
How are disagreements resolved?
Can it control cost?
How is this different from a basic agent workflow?
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