agent-creator

Create and refine domain-specific AI agent definitions with frontmatter manifests.

3|Updated Mar 20, 2026
One-click install
npx skills add https://github.com/alkofu/ai-tpk --skill agent-creator-alkofu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-creator
Source: https://github.com/alkofu/ai-tpk/tree/main/.claude/skills/agent-creator
Command: npx skills add https://github.com/alkofu/ai-tpk --skill agent-creator-alkofu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Agent creation and governance in a codebase is often inconsistent and error-prone. This Skill provides a repeatable pattern to define, document, and manage domain-specific AI agents, enabling rapid onboarding and safer evolution of agent definitions.

Core Features & Use Cases

  • Establish a standardized frontmatter-driven manifest for new agents (name, description, model, tools) and clear operational sections.
  • Guide users through a documented agent creation process: capture intent, research existing agents, define personality and workflow, structure agent files, and update documentation.
  • Support ongoing improvements by outlining documentation, templates, and lifecycle steps for updating or retiring agents.

Quick Start

Describe the desired agent behavior and run the agent-creator to generate a standardized agent definition.

Frequently Asked Questions about agent-creator

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

FAQPage Schema
How do I create a standardized AI agent definition in my repository?

AI agent definitions require a frontmatter-driven manifest containing fields like name, description, model, and tools. You can establish this by following a repeatable process that captures intent, defines workflows, and structures agent files within your repository.

What is the best way to document AI agent creation and governance in a codebase?

Documenting AI agent creation involves establishing a repeatable pattern with standardized frontmatter structure, clear operational sections, and lifecycle steps for updating or retiring domain-specific agents, which ensures safer evolution and rapid onboarding.

How do I update or retire existing AI agents in a codebase?

You update or retire existing AI agents by following outlined documentation, templates, and lifecycle steps designed for ongoing improvements. This process supports the safe evolution and governance of domain-specific agents within your codebase.

Can I use a modular manifest to define domain-specific AI agents?

Yes, you can use a modular manifest to define domain-specific AI agents. The manifest specifies required fields such as name, description, model, and tools, alongside clear operational sections for workflow guidance and documentation updates.

What should be included in the frontmatter structure for an AI agent?

The frontmatter structure for an AI agent should include modular manifest fields such as name, description, model, and tools. This standardization enables consistent operational sections and clearer governance across domain-specific agent definitions.