agent-delegation

Generate structured FOCUS/EXCLUDE prompts for coordinating multi-agent task delegation.

381|48|Updated Aug 12, 2025
One-click install
npx skills add https://github.com/rsmdt/the-startup --skill agent-delegation
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-delegation
Source: https://github.com/rsmdt/the-startup/tree/main/plugins/start/skills/agent-delegation
Command: npx skills add https://github.com/rsmdt/the-startup --skill agent-delegation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Manually breaking down complex tasks, coordinating multiple AI agents, and ensuring their work aligns with project goals is time-consuming and error-prone. This Skill automates that entire orchestration process, allowing you to focus on high-level strategy while AI agents handle the execution.

Core Features & Use Cases

  • Automated Task Decomposition: Breaks down large, complex tasks into smaller, manageable activities, identifying dependencies and optimal execution strategies (parallel vs. sequential).
  • Intelligent Agent Coordination: Generates precise FOCUS/EXCLUDE prompts for specialist agents, preventing scope creep, coordinating file creation to avoid collisions, and validating agent responses.
  • Use Case: You need to "Implement user authentication." This skill automatically decomposes it into "Design DB schema," "Create API endpoints," and "Build UI," then launches specialized agents in the correct sequence or in parallel, ensuring their work is coordinated and compliant.

Quick Start

Break down the task 'Implement a real-time chat feature' into distinct activities and prepare prompts for parallel agent execution, ensuring no file path collisions.

Frequently Asked Questions about agent-delegation

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

FAQPage Schema
How do I break down complex tasks into smaller activities for parallel agent execution?

Task decomposition identifies dependencies and execution strategies, splitting large goals into manageable activities. This skill automatically analyzes your objective, generates structured prompts for specialist agents, and determines optimal parallel or sequential execution to coordinate their work efficiently.

What's the best way to prevent file-path collisions when coordinating multiple AI agents?

File coordination patterns in agent delegation use structured prompts with FOCUS/EXCLUDE templates to assign distinct file paths and prevent conflicts. This skill generates these coordination rules upfront, ensuring agents work in parallel without overwriting each other's outputs.

How does task delegation prevent scope creep across multiple specialist agents?

Intelligent agent coordination uses precise FOCUS/EXCLUDE prompts that explicitly define boundaries for each specialist, preventing overlapping work. This skill validates scope automatically, ensuring agents stay focused on their assigned activities while maintaining alignment with project goals.

Can I automate the process of coordinating responses from parallel agents?

Yes. Agent orchestration automates response coordination by generating structured prompts upfront that define how parallel specialists should validate, format, and integrate their outputs. This skill handles dependency management and validation logic, eliminating manual coordination.

What retry strategies should I implement when delegating tasks to multiple agents?

Retry strategies in agent orchestration are explicit output templates and validation rules generated as part of task decomposition. This skill includes retry logic design in its coordination prompts, enabling agents to handle failures gracefully within your delegation workflow.

How do I design activity-based decomposition for workflow automation?

Activity-based decomposition breaks tasks into discrete, testable units by analyzing dependencies and execution order. This skill generates structured prompts that define each activity's inputs, outputs, and constraints, enabling efficient parallel-first execution across multiple specialist agents.