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BytesAgain β€Ί Skills β€Ί Agent Team Orchestration
πŸ¦€ ClawHub

Agent Team Orchestration

by @amdf01-debug

Set up and coordinate multi-agent teams with defined roles, task workflows, handoff protocols, and quality review checkpoints.

Versionv1.0.0
Downloads646
#clawhub
TERMINAL
clawhub install sw-agent-team-orch

πŸ“– About This Skill

Agent Team Orchestration Skill

Trigger

Set up multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows.

Trigger phrases: "multi-agent team", "agent orchestration", "set up agents", "task routing", "agent handoff", "agent coordination"

Process

1. Define roles: What each agent specialises in 2. Task lifecycle: inbox β†’ spec β†’ build β†’ review β†’ done 3. Handoff protocol: How agents pass work between each other 4. Quality gates: Review checkpoints before work moves forward 5. Shared state: How agents share context and artifacts

Team Architecture Template

# Agent Team: [Name]
Roles


Manager Agent


  • Routes incoming tasks to specialists
    • 

  • Reviews completed work before delivery
    • 

  • Escalates blocked tasks to human
    • 

  • Model: [recommended model for this role]
    • Specialist Agent: [Role Name]
    

  • Handles: [task types]
    • 

  • Outputs: [deliverable format]
    • 

  • Quality bar: [minimum criteria]
    • 

  • Model: [recommended model]
    • Task Lifecycle
    
1. Inbox: New task arrives β†’ Manager triages
2. Assigned: Manager routes to specialist with brief
3. In Progress: Specialist works, updates shared state
4. Review: Manager (or reviewer agent) checks output
5. Revision: If quality gate fails β†’ back to specialist with notes
6. Done: Approved β†’ delivered to requester
    Handoff Protocol
    

  • Include: task description, context, acceptance criteria, deadline
    • 

  • Never: assume context from previous tasks β€” always be explicit
    • 

  • Format: structured JSON or markdown brief
    • Quality Gates
    

  • [ ] Output matches acceptance criteria
    • 

  • [ ] No hallucinated data
    • 

  • [ ] Formatting matches specification
    • 

  • [ ] All links/references verified
    • 

  • [ ] Spell-checked and proofread
    •

    Rules

  • One task per agent at a time (focus > multitasking)
  • Always include acceptance criteria in task briefs
  • Shared state in files, not in agent memory (survives restarts)
  • Model selection matters: use cheap models for bulk, expensive for judgment

    • πŸ”’ Constraints

  • One task per agent at a time (focus > multitasking)
  • Always include acceptance criteria in task briefs
  • Shared state in files, not in agent memory (survives restarts)
  • Model selection matters: use cheap models for bulk, expensive for judgment