# Do In Parallel

Run independent tasks concurrently across multiple files or targets using parallel...

- Repository: https://github.com/NeoLabHQ/context-engineering-kit (1797 stars)
- Category: AI and agents
- License: GPL-3.0
- Last updated: 2026-08-26
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add NeoLabHQ/context-engineering-kit --skill do-in-parallel -g -a claude-code -y`
Codex: `npx skills add NeoLabHQ/context-engineering-kit --skill do-in-parallel -g -a codex -y`
Cursor: `npx skills add NeoLabHQ/context-engineering-kit --skill do-in-parallel -g -a cursor -y`
Gemini CLI: `npx skills add NeoLabHQ/context-engineering-kit --skill do-in-parallel -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NeoLabHQ/context-engineering-kit`

## Description

Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# do-in-parallel

<task>
Launch multiple sub-agents in parallel to execute tasks across different files or targets. Analyze the task to select the right-sized model tier per target, perform requirement grouping analysis (repeatable, shared, or independent), generate quality-focused prompts with Zero-shot Chain-of-Thought reasoning and mandatory self-critique, then dispatch meta-judges based on grouping (one per group or per independent task, all in parallel), followed by implementors for each task in parallel, with LLM-as-a-judge verification using grouping-appropriate evaluation specs after each completes.
</task>

<context>
This command implements the **Supervisor/Orchestrator pattern** with parallel dispatch, **requirement grouping**, and **meta-judge → LLM-as-a-judge verification**. The primary benefit is **parallel execution** - multiple independent tasks run concurrently rather than sequentially, dramatically reducing total execution time for batch operations. Requirement grouping analysis reduces total agents by sharing meta-judges and judges across related tasks: repeatable groups (same task across targets) share one meta-judge spec, shared groups (interdependent tasks) use one combined judge.

The rest: https://github.com/NeoLabHQ/context-engineering-kit/blob/HEAD/antigravity/skills/do-in-parallel/SKILL.md