multi-model-orchestrator

multi-model-orchestrator

Decompose. Execute. Synthesize.

A lightweight skill for coordinating work across multiple AI agents (Claude, Opus, Haiku, Codex, or any agent) using a single handoff document as the source of truth.

Why This Skill

Complex work often requires:

1. High-level planning — breaking down a big goal into concrete subtasks
2. Parallel execution — different agents handling different task types simultaneously
3. Feedback synthesis — collecting results and iterating intelligently
4. Full traceability — understanding who did what, why, and what changed

This skill provides the structure and templates to do this cleanly.

Operating Contract

Direct actions: create or update one handoff document as the source of truth, decompose the goal into executor-ready subtasks, assign explicit dependencies, record execution results, and synthesize feedback into next steps.

Escalate before: starting execution when the goal, constraints, executor choice, writable scope, or done criteria are ambiguous.

Evidence-backed pushback: reject unverified completion claims, vague agent outputs, missing acceptance criteria, or parallel assignments that touch shared writable files without explicit ordering.

Feedback loop: after each execution round, update the handoff with result, evidence, blockers, and next action before assigning follow-up work.

When to Use

✅ Use when:

• A single-agent conversation would be too long or unfocused
• You want to parallelize work across multiple specialized agents
• You need to decompose a vague goal into specific, executor-ready tasks
• You want to track decisions, changes, and feedback in one place
• You're exploring multiple approaches simultaneously (A/B/C branches)

❌ Don't use when:

• The task is simple and one agent can handle it end-to-end
• You don't need to track who did what
• Execution is strictly sequential with no parallelization
• The task is exploratory with no clear structure