Dispatch multiple agents in parallel to investigate and fix independent issues.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "Dispatching Parallel Agents" skill from askskill: 1. Download https://raw.githubusercontent.com/obra/clank/main/skills/collaboration/dispatching-parallel-agents/SKILL.md 2. Save it as ~/.claude/skills/dispatching-parallel-agents/SKILL.md 3. Reload skills and tell me it's ready
Please assign these four independent issues to separate agents for parallel analysis: frontend build failure, backend unit test failure, database migration error, and CI deployment timeout. Have each agent output the root cause, reproduction steps, and fix recommendations, then combine everything into a prioritized summary table.
A consolidated report with each issue’s analysis, recommended fixes, and priority ranking.
Assign the order, payment, and notification modules to different agents because they are independent. Each agent should inspect defects, propose code changes, and generate a patch. Then merge the results, check for cross-impact, and provide integration recommendations.
Module-by-module fixes and patch suggestions, plus final merge guidance and risk checks.
Create multiple agents to research logging systems, job queues, and caching solutions in parallel. Each agent should compare three candidate tools on pros and cons, cost, deployment complexity, and best-fit scenarios, then return the findings in a consistent format for technical decision-making.
A consistently structured comparison report across options to support fast technical selection.
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.
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:
Don't use when:
Group failures by what's broken:
Each domain is independent - fixing tool approval doesn't affect abort tests.
Each agent gets:
// 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
When agents return:
Good agent prompts are:
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.
❌ 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"
…
Write evergreen comments focused on what and why, not historical context.
Compare 2-3 approaches before execution to choose a stronger solution.
Plan with pseudocode first, refine approaches, then translate into working code.
Search past Claude Code chats to recover facts, decisions, and context.
Design systems by hiding implementation details behind domain-level interfaces.
Name code by domain meaning to improve clarity and team alignment.
Delegate coding tasks to OpenAI subagents with parallel, cheap, verified orchestration.
Coordinate multiple Claude Code agents for collaborative development and automated code review.
Resume sessions and run parallel autonomous agents for faster Claude Code workflows.
Research, plan, and execute large code changes with parallel PR-producing agents.
Speed up complex tasks with parallel execution while preserving correctness.
Retrieve past agent fixes and share new solutions for faster debugging.