use-agents-dispatch

mtthsnc's avatarfrom mtthsnc

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

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When & Why to Use This Skill

The use-agents-dispatch skill is a powerful orchestration pattern designed to maximize efficiency by parallelizing independent tasks. It enables the simultaneous dispatch of multiple specialized agents to handle distinct problem domains—such as unrelated bug fixes or isolated test failures—without the bottlenecks of sequential processing. This approach significantly reduces total resolution time and ensures each agent remains focused on a narrow, well-defined scope.

Use Cases

  • Parallel Debugging: Simultaneously investigating and fixing multiple unrelated test failures across different files or subsystems to accelerate the development cycle.
  • Independent Module Maintenance: Dispatching separate agents to address non-overlapping issues in distinct areas like UI components, API endpoints, and database scripts.
  • Concurrent Code Auditing: Assigning different agents to perform security or style reviews on multiple independent repositories or directories at the same time.
  • Distributed Data Processing: Running multiple agents to extract or clean data from different sources where the processing logic for one does not depend on the other.
nameuse-agents-dispatch
descriptionUse when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

When you have multiple unrelated failures (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

Use when:

  • 3+ test files failing with different root causes
  • Multiple subsystems broken independently
  • Each problem can be understood without context from others
  • No shared state between investigations

Don't use when:

  • Failures are related (fix one might fix others)
  • Need to understand full system state
  • Agents would interfere with each other

The Pattern

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

3. Dispatch in Parallel

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

4. Review and Integrate

When agents return:

  • Read each summary
  • Verify fixes don't conflict
  • Run full test suite
  • Integrate all changes

Good Agent Prompts

  1. Focused - One clear problem domain
  2. Self-contained - All context needed to understand the problem
  3. Specific about output - What should the agent return?

Example:

Fix the 3 failing tests in src/agents/agent-tool-abort.test.ts:

1. "should abort tool with partial output capture"
2. "should handle mixed completed and aborted tools"
3. "should properly track pendingToolCount"

These are timing/race condition issues. Your task:

1. Read the test file and understand what each test verifies
2. Identify root cause - timing issues or actual bugs?
3. Fix by replacing arbitrary timeouts with event-based waiting

Do NOT just increase timeouts - find the real issue.

Return: Summary of what you found and what you fixed.

Common Mistakes

❌ Too broad: "Fix all the tests" - agent gets lost ✅ Specific: "Fix agent-tool-abort.test.ts" - focused scope

❌ No context: "Fix the race condition" - agent doesn't know where ✅ Context: Paste the error messages and test names

❌ Vague output: "Fix it" - you don't know what changed ✅ Specific: "Return summary of root cause and changes"

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 - 3 problems solved in time of 1

Verification

After agents return:

  1. Review each summary - Understand what changed
  2. Check for conflicts - Did agents edit same code?
  3. Run full suite - Verify all fixes work together
  4. Spot check - Agents can make systematic errors
use-agents-dispatch – AI Agent Skills | Claude Skills