agent-sdlc

Guide AI agent design and implementation with evidence-based lifecycle knowledge.

Updated Jul 5, 2026
One-click install
npx skills add https://github.com/kwcantrell/rusty-agent --skill agent-sdlc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-sdlc
Source: https://github.com/kwcantrell/rusty-agent/tree/main/.agents/skills/agent-sdlc
Command: npx skills add https://github.com/kwcantrell/rusty-agent --skill agent-sdlc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the complexities of building, evaluating, deploying, and operating AI agents by providing a structured knowledge base and workflow guidance.

Core Features & Use Cases

  • Comprehensive Knowledge Base: Includes 36 first-party sources on AI software development lifecycle.
  • Concept Pages: Features 23 concept pages with citation links.
  • Evaluation and Verification: Facilitates design decisions through evidence-backed knowledge.
  • Integration with Playbooks: Works alongside harness-engineering playbooks for practical applications.

Quick Start

Load the agent-sdlc skill for research or design work on agent architecture within the rusty-agent repository.

Frequently Asked Questions about agent-sdlc

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

FAQPage Schema
What is the AI agent development lifecycle and how do I structure it?▼

The AI agent development lifecycle encompasses building, evaluating, deploying, and operating AI software. This Skill provides a structured knowledge base of 36 first-party sources and 23 concept pages to guide your architecture design and implementation decisions.

How do I design and implement AI agents using evidence-backed guidance?▼

You can design AI agents using 23 concept pages with citation links that facilitate design decisions through evidence-backed knowledge. This integrates directly with practical workflows and harness-engineering playbooks for enhanced decision-making.

Does this AI agent architecture knowledge base work with existing engineering playbooks?▼

Yes, this knowledge base integrates with harness-engineering playbooks for practical applications. It works alongside your existing workflows to deliver evidence-based insights when researching or designing agent architecture.

What's the best way to evaluate and verify AI agent design decisions?▼

The best way to evaluate AI agent design decisions is leveraging a curated knowledge base of 36 first-party sources. It facilitates design decisions through evidence-backed knowledge and citation links for comprehensive verification.

Can I use this for researching agent architecture within a specific repository?▼

Yes, you can load this Skill for research or design work on agent architecture within the rusty-agent repository. It delivers evidence-based insights tailored to your AI software development lifecycle needs.

When do I need a structured knowledge base for AI software deployment?▼

You need a structured knowledge base for AI software deployment when addressing the complexities of building, evaluating, deploying, and operating AI agents. It provides workflow guidance and evidence-based insights to navigate these challenges effectively.