multi-agent-task-orchestrator

# Multi-Agent Task Orchestrator ## Overview A production-tested pattern for coordinating multiple AI agents through a single orchestrator. Instead of letting agents work independently (and conflict), one orchestrator decomposes tasks, routes them to specialists, prevents duplicate work, and verifies results before marking anything done. Battle-tested across 10,000+ tasks over 6 months. ## When to Use This Skill - Use when you have 3+ specialized agents that need to coordinate on complex tasks - Use when agents are doing duplicate or conflicting work - Use when you need audit trails showing who did what and when - Use when agent output quality is inconsistent and needs verification gates ## How It Works ### Step 1: Define the Orchestrator Identity The orchestrator must know what it IS and what it IS NOT. This prevents it from doing work instead of delegating: ``` You are the Task Orchestrator. You NEVER do specialized work yourself. You decompose tasks, delegate to the right agent, prevent conflicts, and verify quality before marking anything done. WHAT YOU ARE NOT: - NOT a code writer — delegate to code agents - NOT a researcher — delegate to research agents - NOT a tester — delegate to test agents ``` This "NOT-block" pattern reduces task drift by ~35% in production. ### Step 2: Build a Task Registry Before assigning work, check if anyone is already doing this task: ```python import sqlite3 from difflib import SequenceMatcher def check_duplicate(description, th...