What is the benefit of the compact YAML format?

It significantly reduces token consumption in the agent's context window while providing a standardized structure that is easy for AI to parse and follow.

Can this skill be used for any programming language?

Yes, the format is language-agnostic. It includes a mapping system to assign specialized agents (like Django, FastAPI, or React experts) to specific tasks regardless of the stack.

How do wave dependencies work in this format?

Tasks are organized into waves (1, 2, 3, etc.). Tasks in higher waves only begin once their dependencies in previous waves are marked as complete, preventing race conditions.

Why is the BOUNDARY directive important?

The BOUNDARY directive explicitly lists files that the agent must not modify, ensuring that agents working in parallel do not accidentally overwrite each other's work.

Parallel Task Formatter

Name: Parallel Task Formatter
Author: jpoutrin

byjpoutrin

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协作工具

Standardizes multi-agent development tasks using a compact YAML format to define scope, boundaries, and dependencies.

关于

The Parallel Task Format skill provides a structured, token-efficient YAML specification for defining development tasks intended for parallel execution by multiple AI agents. It streamlines the orchestration of complex projects by enforcing consistent schemas for component ownership, wave-based dependencies, and strict file boundaries (CREATE, MODIFY, BOUNDARY). This skill is essential for developers using multi-agent workflows who need to ensure that different agents operate within their assigned domains without conflicting, while maintaining clear verification criteria through integrated requirements and checklists.

主要功能

Compact YAML frontmatter for optimized token usage and fast parsing
Domain-specific agent assignment mapping for expert-level task routing
2 GitHub stars
Wave-based dependency management to sequence parallel execution
Explicit scope control via CREATE, MODIFY, and BOUNDARY directives
Integrated contract referencing to ensure cross-task consistency

使用场景

Structuring large-scale migrations using wave-based task dependencies
Orchestrating complex feature development across multiple AI agents simultaneously
Defining strict file-level boundaries to prevent code overwrites in large monorepos

关于

主要功能

Compact YAML frontmatter for optimized token usage and fast parsing
Domain-specific agent assignment mapping for expert-level task routing
2 GitHub stars
Wave-based dependency management to sequence parallel execution
Explicit scope control via CREATE, MODIFY, and BOUNDARY directives
Integrated contract referencing to ensure cross-task consistency

使用场景

Structuring large-scale migrations using wave-based task dependencies
Orchestrating complex feature development across multiple AI agents simultaneously
Defining strict file-level boundaries to prevent code overwrites in large monorepos