Multi Agent Workflow

Multi-Agent Workflow

Overview

This skill enables sophisticated parallel development workflows using multiple Claude instances working concurrently in separate git worktrees. The workflow consists of a manager session (current session), worker agents performing parallel implementations, and observer subagents monitoring progress and collecting results.

Architecture

Manager Session (Current Session)

• Orchestrates the entire workflow
• Distributes tasks to multiple worker agents
• Evaluates implementation results
• Selects and merges the best implementation

Worker Agents (Git Worktrees)

• Execute in isolated git worktrees
• Implement the same task using different approaches
• Work independently and in parallel
• Each worker has its own branch

Observer Subagent

• Monitors worker progress and status
• Collects implementation results
• Performs initial quality assessment
• Reports consolidated findings to manager

When to Use This Skill

Use this skill when:

• Multiple implementation approaches should be explored simultaneously
• Complex tasks benefit from parallel A/B testing of different solutions
• You need to compare different algorithms or architectural patterns
• Time-sensitive development requires faster iteration through parallelization
• Exploring trade-offs between different technical approaches

Workflow Steps

1. Task Distribution

The manager session:

• Receives implementation task from user
• Breaks down into parallelizable work units
• Sets up git worktrees for each worker
• Defines evaluation criteria

2. Parallel Execution

Worker agents:

• Receive specific implementation approach/variant
• Work in isolated git worktrees
• Implement, test, and commit independently
• Each uses different strategy or optimization

3. Monitoring & Collection

Observer subagent:

• Monitors all worker sessions
• Tracks completion status
• Collects implementation artifacts
• Runs preliminary quality checks (tests, lint, etc.)

4. Evaluation & Selection

Manager session:

• Reviews all implementations
• Compares based on criteria:
  • Code quality and maintainability
  • Performance metrics
  • Test coverage
  • Complexity and readability
  • Edge case handling
• Selects best implementation
• Merges into manager's target branch

Usage Instructions

Step 1: Initialize Multi-Agent Workflow

You (Manager): I need to implement [feature/fix] using multi-agent workflow.
Let's explore [N] different approaches:
1. Approach A: [description]
2. Approach B: [description]
3. Approach N: [description]

Evaluation criteria:
- [criterion 1]
- [criterion 2]
- [criterion N]

Step 2: Set Up Worktrees

The manager uses the setup script to create git worktrees:

./.claude/skills/multi-agent-workflow/scripts/setup-worktrees.sh \
  --base-branch "claude/multi-agent-workflow-$(date +%s)" \
  --num-workers 3 \
  --task-id "feature-implementation"

This creates:

• worktree-1/ with branch worker-1/feature-implementation
• worktree-2/ with branch worker-2/feature-implementation
• worktree-3/ with branch worker-3/feature-implementation

Step 3: Launch Worker Agents

The manager launches worker Claude instances in each worktree using Task tool:

Send a single message with multiple Task tool calls:
- Task 1: Worker agent 1 - Implement using approach A
- Task 2: Worker agent 2 - Implement using approach B
- Task 3: Worker agent 3 - Implement using approach C
- Task 4: Observer agent - Monitor all workers and collect results

Each worker receives:

• Task description
• Specific implementation approach
• Evaluation criteria
• Worktree path to work in

Step 4: Observer Monitoring

The observer subagent:

• Periodically checks worker progress
• Collects completed implementations
• Runs automated quality checks
• Compiles comparison report

Step 5: Evaluation and Selection

Manager receives observer report containing:

• Implementation summaries
• Test results
• Performance metrics
• Code quality analysis

Manager evaluates and selects best implementation:

./.claude/skills/multi-agent-workflow/scripts/evaluate-results.sh \
  --worktrees "worktree-1 worktree-2 worktree-3" \
  --criteria "performance,maintainability,test-coverage" \
  --output "evaluation-report.md"

Step 6: Accept Best Implementation

Manager merges selected implementation:

git checkout claude/multi-agent-workflow-[timestamp]
git merge --no-ff worker-2/feature-implementation
git worktree remove worktree-1 worktree-2 worktree-3

Example Scenarios

Scenario 1: Algorithm Optimization

Task: Implement data processing pipeline Workers:

• Worker 1: Stream processing approach
• Worker 2: Batch processing approach
• Worker 3: Hybrid approach Criteria: Performance, memory usage, code simplicity

Scenario 2: UI Component Design

Task: Create user authentication form Workers:

• Worker 1: Material UI components
• Worker 2: Custom styled components
• Worker 3: Headless UI with Tailwind Criteria: Accessibility, bundle size, maintainability

Scenario 3: API Implementation

Task: Build REST API endpoint Workers:

• Worker 1: Express with middleware pattern
• Worker 2: Fastify with schema validation
• Worker 3: Next.js API routes Criteria: Performance, type safety, error handling

Scenario 4: Bug Fix Exploration

Task: Fix complex race condition Workers:

• Worker 1: Mutex locks approach
• Worker 2: Event queue approach
• Worker 3: Refactor to eliminate shared state Criteria: Correctness, simplicity, performance impact

Best Practices

Task Design

1. Clear Constraints: Define exact scope and boundaries for each worker
2. Distinct Approaches: Ensure each worker uses meaningfully different strategy
3. Measurable Criteria: Set objective, quantifiable evaluation metrics
4. Realistic Scope: Size tasks appropriately for parallel completion (30min-2hrs)

Worker Management

1. Isolation: Ensure workers don't interfere with each other
2. Clear Prompts: Provide specific implementation guidance to each worker
3. Resource Limits: Consider computational resources when setting worker count
4. Status Updates: Request periodic progress updates from workers

Observer Configuration

1. Monitoring Frequency: Balance between responsiveness and overhead
2. Quality Gates: Define automated checks (tests pass, lint clean, etc.)
3. Reporting Format: Structure observer reports for easy comparison
4. Early Termination: Allow observer to flag critical failures early

Evaluation Process

1. Objective Metrics: Prioritize quantifiable measurements
2. Code Review: Manually review final candidates
3. Testing: Run comprehensive test suites on all implementations
4. Documentation: Require workers to document their approach
5. Hybrid Solutions: Consider combining best aspects of multiple implementations

Implementation Templates

Worker Prompt Template

Located at: .claude/skills/multi-agent-workflow/templates/worker-prompt.md

Observer Prompt Template

Located at: .claude/skills/multi-agent-workflow/templates/observer-prompt.md

Scripts Reference

setup-worktrees.sh

Creates git worktrees for parallel development.

Usage:

./scripts/setup-worktrees.sh --base-branch <name> --num-workers <n> --task-id <id>

launch-worker.sh

Launches Claude worker instance in specified worktree.

Usage:

./scripts/launch-worker.sh --worktree <path> --task <description> --approach <strategy>

evaluate-results.sh

Evaluates and compares worker implementations.

Usage:

./scripts/evaluate-results.sh --worktrees <paths> --criteria <metrics> --output <file>

Troubleshooting

Worker Conflicts

If workers modify the same files unexpectedly:

• Review task scope definitions
• Ensure worktrees are properly isolated
• Check that workers are using correct branches

Observer Failures

If observer can't collect results:

• Verify worker completion status
• Check file paths and permissions
• Review observer monitoring configuration

Evaluation Ambiguity

If no clear best implementation emerges:

• Refine evaluation criteria
• Consider hybrid approach combining strengths
• Run additional targeted comparisons
• Consult with user on priorities

Tips

• Start with 2-3 workers for first multi-agent workflow
• Document evaluation criteria before launching workers
• Use observer to catch issues early rather than waiting for completion
• Consider running test suites as part of automated evaluation
• Keep implementation approaches distinct enough to yield meaningful comparison
• Archive all worker implementations for future reference, not just selected one

Integration with Other Skills

• parallel-explore: Use before multi-agent-workflow to identify implementation approaches
• code-review: Apply to all worker implementations before final selection
• testing: Ensure each worker includes comprehensive test coverage