home / skills / vadimcomanescu / codex-skills / dispatching-parallel-agents

dispatching-parallel-agents skill

/skills/.experimental/ai/dispatching-parallel-agents

This skill helps orchestrate parallel agents by splitting tasks, defining clear scopes, and safely merging results for coherent final output.

npx playbooks add skill vadimcomanescu/codex-skills --skill dispatching-parallel-agents

Review the files below or copy the command above to add this skill to your agents.

Files (3)

SKILL.md

743 B

---
name: dispatching-parallel-agents
description: Use when splitting complex work into parallelizable tasks and coordinating multiple agents. Focus on clear task boundaries, shared context, and consolidation of results.
---

# Dispatching Parallel Agents

Break large tasks into parallel units with clear boundaries and merge outputs safely.

## Quick Start
1) Split the goal into independent tasks.
2) Assign each agent a clear scope and output format.
3) Merge results and resolve conflicts.

## Guardrails
- Avoid overlapping scopes to reduce conflicting edits.
- Require each agent to state assumptions and sources.
- Consolidate into a single coherent output before shipping.

## References
- Extended examples: `references/examples.md`

Overview

This skill helps split complex work into parallelizable tasks and coordinate multiple agents to run them concurrently. It emphasizes clear task boundaries, shared context, and safe consolidation of results to produce a single coherent deliverable. Use it to speed up throughput while minimizing conflicts and duplication.

How this skill works

You decompose a high-level goal into independent subtasks and define precise scopes and output formats for each agent. Agents run in parallel, declare their assumptions and sources, and return structured outputs. A consolidation step merges results, detects conflicts, and applies resolution rules to deliver a unified final output.

When to use it

Large projects where different parts can be completed independently (e.g., content sections, data pulls, feature modules).
When you need faster turnaround by parallelizing work across multiple agents or workers.
Tasks that require distinct expertise or separated data inputs to reduce single-agent complexity.
When a strict output schema and conflict resolution are required for reliable aggregation.

Best practices

Define non-overlapping scopes and explicit acceptance criteria for every subtask.
Specify structured output formats (JSON, CSV, defined markdown templates) so consolidation can be automated.
Require agents to list assumptions, sources, and confidence scores to aid conflict resolution.
Use a dedicated consolidation agent or process to merge, deduplicate, and reconcile outputs.
Limit cross-dependencies between tasks; where dependencies exist, handle them through well-defined handoffs.

Example use cases

Producing a multi-chapter technical guide by assigning chapters to different agents and then merging edits into a single manuscript.
Running parallel data collection on multiple APIs, normalizing each result, and aggregating into a unified dataset.
Breaking a product launch into copywriting, asset creation, and QA tasks that run concurrently and are later integrated.
Parallelizing bug triage: individual agents validate, classify, and propose fixes, then a consolidator prioritizes and combines patches.

FAQ

How do you prevent conflicting outputs from agents?

Prevent conflicts by enforcing non-overlapping scopes, structured outputs, and requiring agents to state assumptions and sources. Use a consolidation step that applies deterministic rules to resolve overlaps.

What format should agents use for outputs?

Use machine-readable, schema-driven formats like JSON or CSV, or a fixed markdown template. Consistent schemas make merging and validation reliable and automatable.