Dispatching Parallel Agents

SkillProductivity

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

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Dispatching Parallel Agents skill

What this skill tells your AI

The instructions your AI receives, as published by hepai-lab/drsai in skills/superpowers/dispatching-parallel-agents/SKILL.md and read by ahel’s review.

Overview

You delegate tasks to specialized agents with isolated context via DrSai's Delegate tool. 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

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

Use DrSai's Delegate tool for each independent task:

Delegate(agent_type="general", description="Fix agent-tool-abort.test.ts failures", prompt="...")
Delegate(agent_type="general", description="Fix batch-completion-behavior.test.ts failures", prompt="...")
Delegate(agent_type="general", description="Fix tool-approval-race-conditions.test.ts failures", prompt="...")
; 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:

  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?
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 (race condition)

Action: 3 parallel Delegate dispatches (one per file)

Results: All three agents found and fixed root causes independently:

  • Replaced setTimeout with event-based waiting
  • Fixed missing tool execution in batch completion
  • Added proper synchronization for race condition

Integration:

  1. Read each summary - all found real bugs
  2. Checked for conflicts - agents touched different files
  3. Ran full test suite - all 6 failures resolved, 0 regressions
  4. Spot-checked changes - all fixes were minimal and correct

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

Signals

GitHub stars
24
Forks
5
Last commit
Sep 2026
Advanced
Item type
skill
Key
dispatching-parallel-agents-hepai-lab
Source
github.com/hepai-lab/drsai