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commands/multi-agent.md

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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