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ashaykubal
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subagent-prompting

Standardize sub-agent invocations with a 4-part prompt template and F# pipeline notation.

2|Updated Feb 9, 2026
Search Tags:#workflow automation#prompt engineering#ai development#agent orchestration#multi-agent systems#sub-agent prompting
One-click install
npx skills add https://github.com/ashaykubal/essential-agents-skills --skill subagent-prompting
Or copy as Structured Prompt for Agentβ–Ό
Please help me install this Agent Skill.
Skill: subagent-prompting
Source: https://github.com/ashaykubal/essential-agents-skills/tree/main/skills/subagent-prompting
Command: npx skills add https://github.com/ashaykubal/essential-agents-skills --skill subagent-prompting

SYSTEM DOCUMENTATION & REQUIREMENTS

πŸ’‘ This Skill includes references (resource) components.

What problem does it solve?

This Skill standardizes the way you invoke sub-agents, ensuring consistent inputs and predictable outputs for complex multi-agent workflows.

Core Features & Use Cases

  • Structured Prompting: Utilizes a 4-part template (GOAL, CONSTRAINTS, CONTEXT, OUTPUT) for clear agent instructions.
  • Pipeline Visualization: Uses F# conceptual notation to map out sequential agent tasks.
  • Agent Selection: Provides guidance on choosing between custom and built-in agents.
  • Use Case: When building a complex feature, you can use this Skill to orchestrate multiple specialized agents (e.g., a code reviewer, a test writer, and a debugger) in a defined sequence to achieve the desired outcome.

Quick Start

Use the subagent-prompting skill to define a GOAL to identify security vulnerabilities in the auth module.

Frequently Asked Questions about subagent-prompting

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

FAQPage Schema
How do I structure prompts for multi-agent systems to get predictable outputs?β–Ό

To get predictable outputs from multi-agent systems, structure sub-agent prompts using a 4-part template: GOAL, CONSTRAINTS, CONTEXT, and OUTPUT. This standardized format ensures deterministic inputs and consistent responses when orchestrating specialized AI agents.

What is the best way to orchestrate specialist AI agents in a sequential workflow?β–Ό

The best way to orchestrate specialist AI agents in a sequential workflow is by using conceptual F# pipeline notation. This maps out sequential agent tasks and pairs with structured prompting to ensure deterministic inputs and predictable outputs across the entire multi-agent pipeline.

How do I design multi-agent workflows with conditional branching?β–Ό

You design multi-agent workflows with conditional branching by applying a standardized 4-part prompt template to define deterministic inputs. Specifying GOAL, CONSTRAINTS, CONTEXT, and OUTPUT ensures predictable routing and outputs when directing tasks between specialist sub-agents.

When should I use custom agents versus built-in agents for workflow automation?β–Ό

Choose between custom and built-in agents based on your workflow automation complexity. Use the sub-agent prompting template to define deterministic inputs and evaluate whether your specific multi-agent systems require tailored specialist capabilities or standard built-in functionalities.

Do I need any specific frameworks or dependencies to start orchestrating AI agents with structured prompts?β–Ό

No specific frameworks or dependencies are required to start orchestrating AI agents with structured prompts. You only need to apply the 4-part GOAL, CONSTRAINTS, CONTEXT, OUTPUT template and conceptual F# pipeline notation to visualize and execute your multi-agent workflow.

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