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gh-linus-mcmanamey-unify-2-…/commands/multi-agent.md
2025-11-30 08:37:55 +08:00

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---
description: Discuss multi-agent workflow strategy for a specific task
argument-hint: [task-description]
allowed-tools: Read, Task, TodoWrite
---
# Multi-Agent Workflow Discussion
Prepare to discuss how you will use a multi-agent workflow to ${ARGUMENTS}.
## Instructions
1. **Analyze the Task**: ${ARGUMENTS}
- Break down the complexity
- Identify parallelizable components
- Determine if multi-agent approach is optimal
2. **Evaluate Approach**:
- Should this use `/background` (single agent) or `/orchestrate` (multiple agents)?
- How many agents would be optimal?
- What are the dependencies between subtasks?
3. **Design Strategy**:
- Outline the orchestration plan
- Define agent responsibilities
- Specify communication format (JSON responses)
- Identify quality gates
4. **Provide Recommendations**:
- Best command to use (`/background` vs `/orchestrate`)
- Number of agents (if using orchestrate: 2-8)
- Subtask breakdown
- Estimated completion time
- Resource requirements
## Output Format
Provide a structured discussion covering:
1. **Task Analysis**: Understanding of "${ARGUMENTS}"
2. **Complexity Assessment**: Simple/Moderate/High
3. **Recommended Approach**: `/background` or `/orchestrate`
4. **Agent Breakdown** (if orchestrate):
- Agent 1: Responsibility
- Agent 2: Responsibility
- ...
5. **Dependencies**: None / Sequential / Hybrid
6. **Estimated Time**: X minutes
7. **Next Steps**: Commands to run
## Examples
### Example 1: Simple Task
```
User: /aa_command "optimize the g_x_mg_statsclasscount table"
Output:
Task Analysis: Single gold table optimization - focused work
Complexity: Moderate
Recommended: /background
Reasoning: Single file, sequential optimization steps
Estimated Time: 15 minutes
Next Step: /background "Optimize g_x_mg_statsclasscount.py for performance"
```
### Example 2: Complex Task
```
User: /aa_command "implement data quality framework across all layers"
Output:
Task Analysis: Cross-layer feature implementation
Complexity: High
Recommended: /orchestrate
Agent Breakdown:
- Agent 1: Design quality schema and base classes
- Agent 2: Implement bronze layer validators
- Agent 3: Implement silver layer validators
- Agent 4: Implement gold layer validators
- Agent 5: Create validation tests
- Agent 6: Update documentation
Dependencies: Agent 1 must complete before 2-4 (hybrid approach)
Estimated Time: 45-60 minutes
Next Step: Create task file and run /orchestrate data_quality_framework.md
```
### Example 3: Medium Complexity
```
User: /aa_command "fix all linting errors in the silver layer"
Output:
Task Analysis: Code quality fixes across multiple files
Complexity: Moderate-High
Recommended: /orchestrate (if >15 files) or /background (if <15 files)
Agent Breakdown:
- Agent 1: Fix linting in silver_cms files
- Agent 2: Fix linting in silver_fvms files
- Agent 3: Fix linting in silver_nicherms files
Dependencies: None (fully parallel)
Estimated Time: 20-30 minutes
Next Step: /orchestrate "Fix linting errors: silver_cms, silver_fvms, silver_nicherms in parallel"
```
## Usage
```bash
# Discuss strategy for any task
/aa_command "optimize all gold tables for performance"
# Get recommendations for feature implementation
/aa_command "add monitoring and alerting to the pipeline"
# Plan refactoring work
/aa_command "refactor all ETL classes to use new base class pattern"
# Evaluate testing strategy
/aa_command "write comprehensive tests for the medallion architecture"
```
## Notes
- This command helps you plan before executing
- Use this to determine optimal agent strategy
- Creates a blueprint for `/background` or `/orchestrate` commands
- Considers parallelism, dependencies, and complexity
- Provides concrete next steps and command examples