home / skills / bobmatnyc / claude-mpm-skills / dispatching-parallel-agents
This skill coordinates multiple Claude agents to investigate independent failures in parallel, delivering clear summaries and verified fixes for each domain.
npx playbooks add skill bobmatnyc/claude-mpm-skills --skill dispatching-parallel-agentsReview the files below or copy the command above to add this skill to your agents.
---
name: Dispatching Parallel Agents
description: Use multiple Claude agents to investigate and fix independent problems concurrently
when_to_use: when facing 3+ independent failures that can be investigated without shared state or dependencies
version: 2.0.0
progressive_disclosure:
entry_point:
summary: "Dispatch one agent per independent problem domain to solve multiple unrelated failures concurrently"
when_to_use: "When you have 3+ independent failures in different test files/subsystems with no shared state or dependencies"
quick_start: "1. Identify independent domains 2. Create focused agent tasks 3. Dispatch in parallel 4. Review summaries 5. Verify no conflicts and integrate"
references:
- coordination-patterns.md
- agent-prompts.md
- examples.md
- troubleshooting.md
---
# Dispatching Parallel Agents
## Overview
When multiple unrelated failures occur (different test files, different subsystems, different bugs), investigating them sequentially wastes time. Each investigation is independent and can happen in parallel.
**Core principle:** Dispatch one agent per independent problem domain. Let them work concurrently.
## When to Use This Skill
Activate this skill when facing:
- **3+ test files failing** with different root causes
- **Multiple subsystems broken** independently
- **Each problem is self-contained** - can be understood without context from others
- **No shared state** between investigations
- **Clear domain boundaries** - fixing one won't affect others
**Don't use when:**
- Failures are related (fix one might fix others)
- Need to understand full system state first
- Agents would interfere with each other (editing same files)
- Exploratory debugging (don't know what's broken yet)
## The Iron Law
```
One agent, one problem domain, one clear outcome.
Never overlap scopes. Never share state. Always integrate consciously.
```
## Core Principles
### Independence is Key
Problems must be truly independent - no shared files, no related root causes, no dependencies between fixes.
### Focus Over Breadth
Each agent gets narrow scope: one test file, one subsystem, one clear goal. Broad tasks lead to confusion.
### Clear Output Required
Every agent must return a summary: what was found, what was fixed, what changed. No silent fixes.
### Conscious Integration
Don't blindly merge agent work. Review summaries, check conflicts, run full suite, verify compatibility.
## Quick Start
### 1. Identify Independent Domains
Group failures by what's broken:
```
File A tests: Tool approval flow
File B tests: Batch completion behavior
File C tests: Abort functionality
```
Each domain is independent - fixing tool approval doesn't affect abort tests.
### 2. Create Focused Agent Tasks
Each agent gets:
- **Specific scope:** One test file or subsystem
- **Clear goal:** Make these tests pass
- **Constraints:** Don't change other code
- **Expected output:** Summary of what you found and fixed
**→** [agent-prompts.md](references/agent-prompts.md) for prompt templates and examples
### 3. Dispatch in Parallel
```typescript
// In Claude Code / AI environment
Task("Fix agent-tool-abort.test.ts failures")
Task("Fix batch-completion-behavior.test.ts failures")
Task("Fix tool-approval-race-conditions.test.ts failures")
// All three run concurrently
```
**→** [coordination-patterns.md](references/coordination-patterns.md) for dispatch strategies
### 4. Review and Integrate
When agents return:
- Read each summary - understand what changed
- Verify fixes don't conflict - check for same file edits
- Run full test suite - ensure compatibility
- Spot check changes - agents can make systematic errors
**→** [troubleshooting.md](references/troubleshooting.md) for conflict resolution
## Decision Tree
```
Multiple failures?
└→ Are they independent?
├→ NO (related) → Single agent investigates all
└→ YES → Can they work in parallel?
├→ NO (shared state) → Sequential agents
└→ YES → Parallel dispatch ✓
```
## Key Benefits
1. **Parallelization** - Multiple investigations happen simultaneously
2. **Focus** - Each agent has narrow scope, less context to track
3. **Independence** - Agents don't interfere with each other
4. **Speed** - N problems solved in time of 1
## Navigation
### Pattern Reference
- **[Coordination Patterns](references/coordination-patterns.md)** - Dispatch strategies, domain identification, integration workflows
### Agent Management
- **[Agent Prompts](references/agent-prompts.md)** - Prompt structure, templates, common mistakes, constraints
### Learning Resources
- **[Examples](references/examples.md)** - Real-world scenarios, case studies, time savings analysis
### Problem Solving
- **[Troubleshooting](references/troubleshooting.md)** - Conflict resolution, verification strategies, common pitfalls
### Related Skills
When dispatching parallel agents, consider these complementary skills (available in the skill library):
- **pm-workflow**: PM coordination and task management - manage multiple agent workstreams effectively
- **test-driven-development**: TDD patterns that benefit from parallel fixing - understand test failures before parallelizing
- **verification-before-completion**: Integration verification - ensure parallel agent work integrates correctly
## Key Reminders
1. **Independence is mandatory** - Related failures need single-agent investigation
2. **Focus beats breadth** - Narrow scope per agent prevents confusion
3. **Always verify integration** - Don't blindly merge agent work
4. **Clear outputs required** - Every agent returns summary of changes
5. **Parallelization has overhead** - Only worth it for 3+ independent problems
## Red Flags - STOP
**STOP immediately if:**
- Agents are editing the same files (scope overlap)
- Fixes from one agent break another's work (hidden dependencies)
- Problem domains cannot be clearly separated (not independent)
- Agents return no summary (can't verify changes)
- Integration requires major refactoring (conflicts)
**When in doubt:** Start with one agent, understand the landscape, then dispatch if truly independent.
## Integration with Other Skills
**Prerequisite:** Basic understanding of problem domains and test structure
**Complementary:** PM-Workflow skill for coordinating multiple agents (see skill library)
**Domain-specific:** Testing skills for understanding test failures (available in skill library)
## Real-World Impact
From debugging session (2025-10-03):
- **6 failures** across 3 test files
- **3 agents** dispatched in parallel
- **All investigations** completed concurrently
- **Zero conflicts** between agent changes
- **Time saved:** 3 problems solved in parallel vs sequentially
**→** [examples.md](references/examples.md) for detailed case study
This skill shows how to dispatch multiple Claude agents to investigate and fix independent problems concurrently. It emphasizes one agent per problem domain, narrow scopes, and conscious integration of results. The goal is to reduce time-to-resolution when multiple unrelated failures occur.
Identify independent failure domains (different test files, subsystems, or bugs). Create focused agent tasks with a single clear goal, constraints, and expected output, then run those agents in parallel. When agents finish, review each summary, verify changes, run the full test suite, and integrate deliberately to avoid conflicts.
What if agents edit the same file?
Stop parallel work on overlapping files. Convert to sequential investigation or coordinate explicit handoffs to avoid conflicts.
How many agents should I run in parallel?
Prefer parallelizing when you have 3+ truly independent problems. Consider overhead—don’t parallelize trivial or related failures.