The dispatching-parallel-agents Skill — One Agent Per Problem Domain
How the dispatching-parallel-agents skill eliminates sequential investigation of independent failures by dispatching isolated agents to each problem domain simultaneously and integrating their results.
When six tests fail across three files after a major refactoring, the natural reflex is to fix them one at a time. If the failures are independent — different subsystems, different root causes — that sequential approach wastes time proportional to the number of problems. The dispatching-parallel-agents skill formalises the alternative: one isolated agent per independent problem domain, all dispatched at the same time.
The independence test
Before dispatching anything, the skill requires an independence check. The question is whether fixing one failure could affect another. If the failures share a root cause or touch the same code paths, they belong to a single agent. If they are genuinely independent — tool approval flow, batch completion behavior, and abort functionality, for example — each gets its own agent with its own isolated context window.
The skill also requires checking for shared state. Agents that would edit the same files or use the same resources interfere with each other and must run sequentially. Parallel dispatch is for domains where agents can work without coordination.
What each agent needs
The skill specifies four things a good agent prompt must include. A focused scope: one test file or one subsystem, not "all the failures." All context needed to understand the problem: the error messages, the test names, and what each test verifies. Explicit constraints: "do not change production code" or "fix tests only." A specific output format: a summary of root cause and what was changed, not just "done."
The skill names common mistakes with concrete corrections. "Fix all the tests" is too broad — the agent gets lost. "Fix agent-tool-abort.test.ts" is the right scope. Passing the error messages and test names in the prompt is the difference between an agent that finds the real issue and one that just increases timeouts.
A real example from the skill
The skill documents a specific session: six failures across three test files after a major refactoring. Three agents were dispatched in parallel. Agent 1 fixed timing issues in the abort test file by replacing arbitrary timeouts with event-based waiting. Agent 2 found an event structure bug in the batch completion file — a threadId field placed in the wrong location. Agent 3 added a wait for async tool execution in the race condition file. All three fixes were independent, no conflicts arose on integration, and the full test suite went green.
Integration and verification
When agents return, the orchestrator reads each summary, checks for conflicts, runs the full test suite, and spot-checks the changes. The skill notes that agents can make systematic errors — if all three agents applied the same wrong fix, the spot-check catches it before the session ends.
The skill is explicit about when not to use this pattern. Related failures that share a root cause should be investigated together. Exploratory debugging — where it is not yet known what is broken — needs a single agent with full context. Any situation where agents would edit the same files is sequential, not parallel.
Read what Datarim is for the full pipeline overview, or the /dr-do command post for where parallel agent dispatch fits in the implementation phase.