dispatching-parallel-agents

SKILL.md

Dispatching Parallel Agents

Overview

You delegate tasks to specialized agents with isolated context. By precisely crafting their instructions and context, you ensure they stay focused and succeed at their task. They should never inherit your session's context or history — you construct exactly what they need. This also preserves your own context for coordination work.

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
digraph when_to_use {
    "Multiple failures?" [shape=diamond];
    "Are they independent?" [shape=diamond];
    "Single agent investigates all" [shape=box];
    "One agent per problem domain" [shape=box];
    "Can they work in parallel?" [shape=diamond];
    "Sequential agents" [shape=box];
    "Parallel dispatch" [shape=box];

    "Multiple failures?" -> "Are they independent?" [label="yes"];
    "Are they independent?" -> "Single agent investigates all" [label="no - related"];
    "Are they independent?" -> "Can they work in parallel?" [label="yes"];
    "Can they work in parallel?" -> "Parallel dispatch" [label="yes"];
    "Can they work in parallel?" -> "Sequential agents" [label="no - shared state"];
}

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

// 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

4. Review and Integrate

When agents return:

Read each summary

Verify fixes don't conflict

Run full test suite

Integrate all changes

Agent Prompt Structure

Good agent prompts are:

Focused - One clear problem domain

Self-contained - All context needed to understand the problem

Specific about output - What should the agent return?

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

1. "should abort tool with partial output capture" - expects 'interrupted at' in message
2. "should handle mixed completed and aborted tools" - fast tool aborted instead of completed
3. "should properly track pendingToolCount" - expects 3 results but gets 0

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
   - Fixing bugs in abort implementation if found
   - Adjusting test expectations if testing changed behavior

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

❌ No constraints: Agent might refactor everything
✅ Constraints: "Do NOT change production code" or "Fix tests only"

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

When NOT to Use

Related failures: Fixing one might fix others - investigate together first
Need full context: Understanding requires seeing entire system
Exploratory debugging: You don't know what's broken yet
Shared state: Agents would interfere (editing same files, using same resources)

Real Example from Session

Scenario: 6 test failures across 3 files after major refactoring

Failures:

agent-tool-abort.test.ts: 3 failures (timing issues)

batch-completion-behavior.test.ts: 2 failures (tools not executing)

tool-approval-race-conditions.test.ts: 1 failure (execution count = 0)

Decision: Independent domains - abort logic separate from batch completion separate from race conditions

Dispatch:

Agent 1 → Fix agent-tool-abort.test.ts
Agent 2 → Fix batch-completion-behavior.test.ts
Agent 3 → Fix tool-approval-race-conditions.test.ts

Results:

Agent 1: Replaced timeouts with event-based waiting

Agent 2: Fixed event structure bug (threadId in wrong place)

Agent 3: Added wait for async tool execution to complete

Integration: All fixes independent, no conflicts, full suite green

Time saved: 3 problems solved in parallel vs sequentially

Key Benefits

Parallelization - Multiple investigations happen simultaneously

Focus - Each agent has narrow scope, less context to track

Independence - Agents don't interfere with each other

Speed - 3 problems solved in time of 1

Verification

After agents return:

Review each summary - Understand what changed

Check for conflicts - Did agents edit same code?

Run full suite - Verify all fixes work together

Spot check - Agents can make systematic errors

Real-World Impact

From debugging session (2025-10-03):

6 failures across 3 files

3 agents dispatched in parallel

All investigations completed concurrently

All fixes integrated successfully

Zero conflicts between agent changes