agent-builder

Guide creation and refinement of Claude Code subagents with system prompts and tool configuration.

1|1|Updated Oct 30, 2025
One-click install
npx skills add https://github.com/NangoHQ/ai-ide-prompts --skill agent-builder
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-builder
Source: https://github.com/NangoHQ/ai-ide-prompts/tree/main/.claude/skills/agent-builder-skill
Command: npx skills add https://github.com/NangoHQ/ai-ide-prompts --skill agent-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Building effective Claude Code subagents requires careful design, prompt engineering, and tool configuration. This Skill provides expert guidance to create specialized AI assistants that excel at complex, multi-step workflows, ensuring they are efficient, reliable, and produce high-quality results.

Core Features & Use Cases

  • Agent Design Principles: Learn to define clear agent scopes, single responsibilities, and measurable outcomes for optimal performance.
  • System Prompt Engineering: Master the art of writing detailed system prompts that define agent roles, processes, output formats, and quality standards.
  • Tool & Model Configuration: Understand how to grant minimal necessary tool access and select the appropriate Claude model (Opus, Sonnet, Haiku) for each agent's task.
  • Use Case: A developer wants to create a specialized "code-reviewer" agent. This Skill guides them through defining its role, specifying review categories (quality, security, performance), configuring Read, Grep, and Bash(git *) tools, and structuring its output for actionable feedback.

Quick Start

Use the agent-builder skill to help me create a new Claude subagent. I want it to specialize in debugging TypeScript errors.

Frequently Asked Questions about agent-builder

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

FAQPage Schema
How do I design a Claude subagent for a specific task?

Design a Claude subagent by defining its single responsibility, scope boundaries, and measurable outcomes. Write a detailed system prompt specifying the agent's role, processes, and output format, then configure minimal necessary tool access and select the appropriate Claude model (Opus, Sonnet, or Haiku) based on task complexity.

What should I include in a system prompt for a subagent?

A subagent system prompt must define the agent's role, its core responsibilities, step-by-step processes it follows, expected output formats, and quality standards. Include specific examples of inputs and desired outputs, constraints on behavior, and any domain-specific terminology or requirements the agent needs to understand.

How do I decide which Claude model to use for my subagent?

Select Claude model based on task complexity and latency requirements. Use Haiku for simple, fast tasks with minimal reasoning; Sonnet for balanced performance on moderately complex workflows; Opus for reasoning-intensive, multi-step problems requiring deep analysis and reliability.

What tools should I grant to a subagent?

Grant only the minimum tools necessary for the subagent to complete its task. Common patterns include Read for file access, Grep for searching content, and Bash for git operations. Define tool permissions narrowly—for example, restrict Bash to git commands only—to maintain security and prevent unintended actions.

Can I use multiple subagents together in a workflow?

Yes, subagents can delegate tasks to other specialized subagents through defined delegation patterns. Structure your architecture so each subagent owns a single responsibility, with clear handoff points and standardized input/output formats between agents to ensure modular, reliable multi-agent workflows.

Why does my subagent produce inconsistent outputs?

Inconsistent outputs typically stem from vague system prompts, unclear output format specifications, or mismatched model selection. Tighten your system prompt with explicit examples of desired output, define a rigid output schema, and verify the model you chose matches your task's reasoning requirements.