multi-agent-coordination

# Multi-Agent Coordination

**Version:** 1.0.0
**Purpose:** Patterns for coordinating multiple agents in complex workflows
**Status:** Production Ready

## Overview

Multi-agent coordination is the foundation of sophisticated Claude Code workflows. This skill provides battle-tested patterns for orchestrating multiple specialized agents to accomplish complex tasks that are beyond the capabilities of a single agent.

The key challenge in multi-agent systems is **dependencies**. Some tasks must execute sequentially (one agent's output feeds into another), while others can run in parallel (independent validations from different perspectives). Getting this right is the difference between a 5-minute workflow and a 15-minute one.

This skill teaches you:
- When to run agents in **parallel** vs **sequential**
- How to **select the right agent** for each task
- How to **delegate** to sub-agents without polluting context
- How to manage **context windows** across multiple agent calls

## Core Patterns

### Pattern 1: Sequential vs Parallel Execution

**When to Use Sequential:**

Use sequential execution when there are **dependencies** between agents:
- Agent B needs Agent A's output as input
- Workflow phases must complete in order (plan → implement → test → review)
- Each agent modifies shared state (same files)

**Example: Multi-Phase Implementation**

```
Phase 1: Architecture Planning
  Task: api-architect
    Output: ai-docs/architecture-plan.md
    Wait for completion ✓

Phase 2: Implementation (depends on Phase 1)
  Task: backend-developer
    Input: Read ai-docs/architecture-plan.md
    Output: src/auth.ts, src/routes.ts
    Wait for completion ✓

Phase 3: Testing (depends on Phase 2)
  Task: test-architect
    Input: Read src/auth.ts, src/routes.ts
    Output: tests/auth.test.ts
```

**When to Use Parallel:**

Use parallel execution when agents are **independent**:
- Multiple validation perspectives (designer + tester + reviewer)
- Multiple AI models reviewing same c