
Dispatching Parallel Agents
FreeOptimize debugging by running tasks concurrently.
Free · Opens the source repo
What Dispatching Parallel Agents does
The Dispatching Parallel Agents skill is designed to enhance the efficiency of debugging processes by allowing multiple independent tasks to be handled simultaneously. When faced with multiple unrelated failures—such as different test files or subsystems that are malfunctioning—this skill enables developers to assign dedicated agents to each issue, thereby eliminating the time wasted on sequential investigations. Each agent operates independently, focusing on a specific problem domain without the risk of interference from others.
This skill is particularly useful in scenarios where failures do not share a common root cause. By identifying independent domains, developers can group failures and create focused tasks for each agent. For instance, if there are three test files failing due to distinct reasons, the skill allows for the dispatch of one agent per file. This approach not only streamlines the debugging process but also ensures that each agent can work concurrently, significantly speeding up the resolution of issues.
The structured approach of this skill emphasizes the importance of specificity in agent prompts. Clear instructions help agents understand their tasks without ambiguity, ensuring that they return concise summaries of findings and fixes. This method reduces the risk of common mistakes, such as assigning overly broad tasks or failing to provide adequate context. Overall, Dispatching Parallel Agents is a valuable tool for developers looking to optimize their debugging workflows and improve their productivity by leveraging parallel processing.
When to use it
Use this skill when you have multiple independent failures that can be resolved without shared state or dependencies.
When not to use it
Avoid using this skill when failures are related or require a full understanding of the system state.
What you can build with it
Debugging Multiple Test Failures
When encountering several test failures across different files, dispatch agents to resolve each one concurrently.
Independent Subsystem Issues
If multiple subsystems are malfunctioning independently, assign agents to address each subsystem's problems without interference.
Efficient Bug Fixing
When bugs are identified in unrelated areas of code, use this skill to quickly assign and resolve each issue in parallel.
How to install Dispatching Parallel Agents
View source1. Install with the skills CLI
npx skills add davila7/claude-code-templates/dispatching-parallel-agents --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by davila7Dispatching Parallel Agents
Overview
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
Frequently asked questions about Dispatching Parallel Agents
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