Jump to related tools in the same category or review the original source on GitHub.

Git & GitHub @arminnaimi Updated 2/13/2026

Agent Team Orchestration OpenClaw Plugin & Skill | ClawHub

Looking to integrate Agent Team Orchestration into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate git & github tasks instantly, without having to write custom tools from scratch.

What this skill does

Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.

Install

npx clawhub@latest install agent-team-orchestration

Full SKILL.md

Open original
Metadata table.
namedescription
agent-team-orchestrationOrchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.

SKILL.md content below is scrollable.

Agent Team Orchestration

Production playbook for running multi-agent teams with clear roles, structured task flow, and quality gates.

Quick Start: Minimal 2-Agent Team

A builder and a reviewer. The simplest useful team.

1. Define Roles

Orchestrator (you) — Route tasks, track state, report results
Builder agent     — Execute work, produce artifacts

2. Spawn a Task

1. Create task record (file, DB, or task board)
2. Spawn builder with:
   - Task ID and description
   - Output path for artifacts
   - Handoff instructions (what to produce, where to put it)
3. On completion: review artifacts, mark done, report

3. Add a Reviewer

Builder produces artifact → Reviewer checks it → Orchestrator ships or returns

That's the core loop. Everything below scales this pattern.

Core Concepts

Roles

Every agent has one primary role. Overlap causes confusion.

Role Purpose Model guidance
Orchestrator Route work, track state, make priority calls High-reasoning model (handles judgment)
Builder Produce artifacts — code, docs, configs Can use cost-effective models for mechanical work
Reviewer Verify quality, push back on gaps High-reasoning model (catches what builders miss)
Ops Cron jobs, standups, health checks, dispatching Cheapest model that's reliable

Read references/team-setup.md when defining a new team or adding agents.

Task States

Every task moves through a defined lifecycle:

Inbox → Assigned → In Progress → Review → Done | Failed

Rules:

  • Orchestrator owns state transitions — don't rely on agents to update their own status
  • Every transition gets a comment (who, what, why)
  • Failed is a valid end state — capture why and move on

Read references/task-lifecycle.md when designing task flows or debugging stuck tasks.

Handoffs

When work passes between agents, the handoff message includes:

  1. What was done — summary of changes/output
  2. Where artifacts are — exact file paths
  3. How to verify — test commands or acceptance criteria
  4. Known issues — anything incomplete or risky
  5. What's next — clear next action for the receiving agent

Bad handoff: "Done, check the files." Good handoff: "Built auth module at /shared/artifacts/auth/. Run npm test auth to verify. Known issue: rate limiting not implemented yet. Next: reviewer checks error handling edge cases."

Reviews

Cross-role reviews prevent quality drift:

  • Builders review specs — "Is this feasible? What's missing?"
  • Reviewers check builds — "Does this match the spec? Edge cases?"
  • Orchestrator reviews priorities — "Is this the right work right now?"

Skip the review step and quality degrades within 3-5 tasks. Every time.

Read references/communication.md when setting up agent communication channels.Read references/patterns.md for proven multi-step workflows.

Reference Files

File Read when...
team-setup.md Defining agents, roles, models, workspaces
task-lifecycle.md Designing task states, transitions, comments
communication.md Setting up async/sync communication, artifact paths
patterns.md Implementing specific workflows (spec→build→test, parallel research, escalation)

Common Pitfalls

Spawning without clear artifact output paths

Agent produces great work, but you can't find it. Always specify the exact output path in the spawn prompt. Use a shared artifacts directory with predictable structure.

No review step = quality drift

"It's a small change, skip review." Do this three times and you have compounding errors. Every artifact gets at least one set of eyes that didn't produce it.

Agents not commenting on task progress

Silent agents create coordination blind spots. Require comments at: start, blocker, handoff, completion. If an agent goes silent, assume it's stuck.

Not verifying agent capabilities before assigning

Assigning browser-based testing to an agent without browser access. Assigning image work to a text-only model. Check capabilities before routing.

Orchestrator doing execution work

The orchestrator routes and tracks — it doesn't build. The moment you start "just quickly doing this one thing," you've lost oversight of the rest of the team.

When NOT to Use This Skill

  • Single-agent setups — Just follow standard AGENTS.md conventions. Team orchestration adds overhead that solo agents don't need.
  • One-off task delegation — Use sessions_spawn directly. This skill is for sustained workflows with multiple handoffs.
  • Simple question routing — If you're just forwarding a question to a specialist, that's a message, not a workflow.

This skill is for sustained team workflows — recurring collaboration patterns where agents depend on each other's output over multiple tasks.

Original Repository URL: https://github.com/openclaw/skills/blob/main/skills/arminnaimi/agent-team-orchestration
Latest commit: https://github.com/openclaw/skills/commit/13fa9a8fc3cb782904e68cd5c20c6a0acee59682

Related skills

If this matches your use case, these are close alternatives in the same category.

agent-commons

Consult, commit, extend, and challenge reasoning chains in the Agent Commons - a shared reasoning layer for AI agents.

agentdo

Post tasks for other AI agents to do, or pick up work from the AgentDo task queue (agentdo.dev). Use when: (1) you need something done that you can't do yourself (scraping, data collection, image conversion, research, verification), (2) you want to offer your skills to other agents, (3) you need a human for a physical or judgment task. Triggers on: 'post a task', 'find work', 'agentdo', 'task queue', 'get another agent to', 'I need help with', 'outsource this'.

agentgate

API gateway for personal data with human-in-the-loop write approval. Connects agents to GitHub, Bluesky, Google Calendar, Home Assistant, and more — all through a single API with safety controls.

airadar

Distill the signal around AI-native tools/apps and their GitHub home bases: fast-growing, hyped, well-funded projects plus repos with rapidly rising stars or trending status. Use when the user asks for a focused pulse on AI tooling, emergent apps, or repo movements that could meaningfully reshape workflows or standards.

alex-session-wrap-up

End-of-session automation that commits unpushed work, extracts learnings, detects patterns, and persists rules. Uses gpt-4o-mini for pattern detection. Runs at session end or on-demand.

amazon-product-api-skill

This skill helps users extract structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications. Use this skill when users want to search for products on Amazon, find the best selling brand products, track price changes for items, get a list of categories with high ratings, compare different brand products on Amazon, extract Amazon product data for market research, look for products in a specific language or marketplace, analyze competitor pricing for keywords, find featured products for search terms, get technical specifications like material or color for product lists.