agent-coordination-patterns

# Agent Coordination Patterns

## Description

Coordination patterns for sequential, parallel, and iterative agent workflows. Defines how agents work together, communicate findings, and maintain context across handoffs in multi-agent systems.

## When to Use

Automatically apply this skill when:
- Designing multi-agent workflows
- Coordinating agent handoffs
- Planning agent dependencies
- Integrating agent outputs
- Building complex workflows
- Optimizing agent collaboration

## Core Principle: Hybrid Context Sharing

Two complementary layers work together:

**1. Transparency Layer** (File-Based)
- Human-inspectable files
- Real-time progress visibility
- Easy debugging and inspection
- Clear agent coordination

**2. Intelligence Layer** (Knowledge Graph)
- Historical learning
- Pattern recognition
- Cross-session persistence
- Audit trails

## Agent Integration Protocol

### Phase 1: Pre-Execution Context Reading

Before starting work, agents MUST:

1. **Read workflow context** → Understand overall objective
2. **Check agent queue** → Know dependencies and position
3. **Review shared data** → Get requirements and standards
4. **Read dependency outputs** → Build on previous work

**Example Flow**:
```
User delegates to python-developer:

1. Read current-workflow.md → "Building REST API"
2. Read agent-queue.md → "research-assistant completed"
3. Read inter-agent-context.json → "Tech: FastAPI + PostgreSQL"
4. Read research-assistant-output.md → "Requirements defined"
5. Begin implementation with full context
```

### Phase 2: Progress Reporting During Execution

Continuously communicate state:

- Update progress file every 5-10 minutes
- Report current activity clearly
- List completed steps
- Note any blockers immediately
- Estimate remaining time

### Phase 3: Post-Execution Output Writing

Produce standardized results:

- Document all accomplishments
- Record technical decisions
- List created artifacts
- Note known issues
- Provide handoff guidance

## Coordinati