agent-builder

Design Claude Code agents with experiential identities and structured prompts.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/101mare/skill-library --skill agent-builder-101mare
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-builder
Source: https://github.com/101mare/skill-library/tree/main/skills/meta/agent-builder
Command: npx skills add https://github.com/101mare/skill-library --skill agent-builder-101mare

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of creating AI agents that are not only functional but also highly effective and specialized, moving beyond generic instructions to deeply ingrained, research-backed identities.

Core Features & Use Cases

  • Experiential Identity Design: Learn to craft agent prompts that leverage specific experiences for improved accuracy (10-60%).
  • Anti-Pattern Avoidance: Understand and implement "what I refuse to do" sections to create reliable agents.
  • Multi-File Structure & Consolidation: Organize complex agents and merge specialized agents into more powerful, consolidated ones.
  • Use Case: When building a new AI assistant for code review, use this Skill to learn how to give it a specific, experienced identity that outperforms a generic "expert" label, leading to more insightful and accurate reviews.

Quick Start

Use the agent-builder skill to learn how to create a new agent for analyzing user feedback.

Frequently Asked Questions about agent-builder

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

FAQPage Schema
How do I design Claude Code agents with research-backed identities?

Design Claude Code agents by crafting prompts that leverage specific experiential identities, using structured frontmatter fields and multi-file organization to achieve 10-60% improved accuracy over generic expert labels.

What is the best way to structure multi-file agent prompts for Claude Code?

Structure multi-file agent prompts by organizing complex agents into separate files and applying consolidation patterns to merge specialized agents into more powerful, consolidated ones with optimized tool selection.

Why does my AI agent give generic responses instead of specialized behavior?

Generic responses occur when agents lack specific experiential identities. Fix this by implementing research-backed identity design with anti-pattern avoidance sections defining what the agent refuses to do for reliable specialization.

Can I consolidate multiple specialized agents into a single Claude Code agent?

Consolidate multiple specialized agents by applying structured consolidation patterns that merge distinct experiential identities into a unified agent while maintaining specialized behavior through carefully designed frontmatter fields.

Does prompt engineering with experiential identities actually improve agent accuracy?

Prompt engineering with experiential identities improves accuracy by 10-60% compared to generic expert labels, because deeply ingrained research-backed identities provide more insightful and accurate agent behavior for specialized tasks.

When should I add an anti-pattern section to my agent design?

Add an anti-pattern section when building reliable agents that require strict behavioral boundaries, defining what the agent refuses to do ensures consistent specialized behavior beyond what generic instructions provide.