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mtsatryan
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agent-generator

Generates deployment-ready AI agents with code, prompts, and tests from user-defined requirements and templates.

1|Updated Apr 23, 2026
Search Tags:#validation#templating#code-generation#dsl#system-design#agent-generation
One-click install
npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill agent-generator-mtsatryan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-generator
Source: https://github.com/mtsatryan/openclaw-ai-agents/tree/main/agent-generator
Command: npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill agent-generator-mtsatryan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Streamline the design and deployment of AI agents by enabling dynamic generation, templating, and DSL-based definitions, reducing manual engineering and accelerating consistency across projects.

Core Features & Use Cases

  • Dynamic agent generation from domain and requirements using templates, patterns, and DSLs
  • Code generation and scaffolding with best practices and automated validation
  • End-to-end agent delivery including prompts, examples, and test cases

Quick Start

Provide your domain and requirements, then run the agent-generator to produce a deployment-ready agent specification

Frequently Asked Questions about agent-generator

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

FAQPage Schema
How do I generate production-grade AI agents from domain requirements?▼

To generate production-grade AI agents, provide your domain and requirements to trigger automated pattern selection, capability composition, and prompt, code, and test generation for end-to-end deployment.

What is DSL-based agent generation and how does it streamline deployment?▼

DSL-based agent generation uses domain-specific language definitions and templates to automate design, reducing manual engineering and accelerating consistency across deployed projects.

Do I need an OpenAI-compatible model to scaffold and validate AI agents?▼

Yes, you need an OpenAI-compatible model and a configured toolchain to validate and assemble the deployed agent artifacts during the scaffolding process.

Can I use automated agent generation for cloud automation and data science contexts?▼

Yes, automated agent generation applies across software engineering, cloud automation, data science, and product contexts to deliver ready-to-deploy agent implementations tailored to those environments.

What is the best way to automate agent creation with templated workflows?▼

Automating agent creation with templated workflows involves analyzing requirements, selecting patterns, and composing capabilities to produce prompts, code, and tests for immediate deployment.

Why does agent generation require a configured toolchain to validate artifacts?▼

Agent generation requires a configured toolchain to validate artifacts because the system must assemble and verify the generated prompts, code, and test cases before producing end-to-end deployment specifications.

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