parallelization

Run independent LLM subtasks concurrently and merge results with deterministic logic.

Updated Apr 29, 2026
One-click install
npx skills add https://github.com/dev-khoi/AURA-conHack-2026 --skill parallelization-dev-khoi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: parallelization
Source: https://github.com/dev-khoi/AURA-conHack-2026/tree/main/.opencode/skills/parallelization
Command: npx skills add https://github.com/dev-khoi/AURA-conHack-2026 --skill parallelization-dev-khoi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Sequential LLM calls waste time when a task can be split into independent units, and single-pass scoring lacks the reliability of ensemble judgment. This Skill provides a structured pattern for splitting work, running branches concurrently, and merging outputs deterministically. ## Core Features & Use Cases - Sectioning: Split a task into independent slices (document chunks, test cases, tool queries) and process each in its own branch. - Voting and Ensemble Scoring: Run multiple judges on the same candidate answer in parallel and aggregate scores by voting or ranking. - Failure and Cost Guardrails: Checklists covering timeouts, retries, partial-failure policies, rate-limit fit, and cost caps per branch. - Use Case: You need to summarize a 50-page report. Split it into per-section branches, summarize each concurrently, then merge the section summaries into one final output with deterministic join logic. ## Quick Start Apply the parallelization pattern to split my task into independent subtasks, run them concurrently, and merge the results.

Frequently Asked Questions about parallelization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run LLM subtasks in parallel?▼

Split the task into independent units with stable inputs, run each branch concurrently, then collect and merge outputs. Set per-branch timeouts, retries, and normalize outputs to a shared schema before merging with deterministic code.

What is the difference between sectioning and voting in parallel agents?▼

Sectioning assigns each branch a different slice of work, such as one document chunk per branch. Voting runs multiple branches on the same input so several judges can score or rank a candidate answer for ensemble reliability.

When should I not use parallelization for LLM workflows?▼

Avoid it when branches need shared evolving context, when one branch's output changes another, or when merge logic is complex. Rate limits can also erase concurrency gains, and coordination overhead may exceed the latency saved.

Why does my parallel LLM workflow have high latency or cost?▼

A single long-tail branch dominates total latency, and cost grows linearly with branch count. Set per-branch timeouts, cap concurrency to fit rate limits, and benchmark against a serial baseline before scaling branches.

How do I handle partial failures in parallel agent branches?▼

Define a retry and partial-failure policy before running branches, and log per-branch failures explicitly. Decide whether the merge step tolerates missing outputs or requires all branches to succeed, and never silently ignore failed branches.