GitHub Gists: Notes and scripts for working with AI agents

I publish scripts, configuration examples, and workflow notes from my work with AI coding agents. The collection includes specification prompts, review workflows, and integration examples.

These are working notes and snippets to inspect and adapt, not a supported software library.

Browse the gists.

What You'll Find

Agent Orchestration Patterns

Code snippets demonstrating how to effectively coordinate multiple AI agents (Claude Code Web, GitHub Copilot, Gemini CLI) across different phases of development. Real examples from projects like TutorPro and Leaderboard Fantasy.

Prompt Engineering

Proven prompt templates and patterns for:

• Feature specification that AI agents can execute autonomously
• Breaking down complex tasks into agent-friendly chunks
• Quality gates and review prompts
• Context management for large codebases

Integration Snippets

Practical code for integrating AI capabilities into applications:

• Spring AI with Google Vertex AI Gemini
• Firebase with AI-driven features
• Real-time data processing with AI analysis
• Custom agent hooks and automation scripts

Development Workflow

Scripts and configurations that enable 10x+ developer velocity:

• CI/CD pipeline configurations for agent-generated code
• Local development environment setup
• Automated testing strategies for AI-assisted development
• Git workflows optimized for agent collaboration

Lessons Learned

Snippets that capture hard-won insights:

• Common agent hallucination patterns and how to prevent them
• Task granularity sweet spots (too large = confusion, too small = inefficiency)
• When to use which AI agent for specific tasks
• Quality assurance patterns for agent-generated code

How This Collection Grows

Every gist represents a real problem solved or pattern discovered while building production software with AI agents. These aren't theoretical examples—they're battle-tested snippets from shipping code.

As the AI landscape evolves and new patterns emerge, this collection will grow. The goal is simple: share what works, document what doesn't, and help others navigate the rapidly changing world of agentic AI development.

The Philosophy

Learning in public. Building with AI agents is still new territory. We're all figuring this out together. These gists are part of that journey—documenting what's working right now, while acknowledging it will all change again tomorrow.

Practical over perfect. These are working snippets, not polished libraries. They're meant to be adapted, not imported wholesale. Take what's useful, modify what isn't, and share your own improvements.

Velocity through orchestration. The snippets here support a central thesis: developer velocity is no longer about typing speed—it's about spec clarity, agent selection, and quality gates. The code here reflects that philosophy.

Using These Gists

Each gist includes:

• Context: What problem it solves or pattern it demonstrates
• Code: The actual implementation
• Usage notes: How to adapt it to your needs
• Learnings: What I discovered building it

Browse by tag, search by keyword, or just explore. If something helps you ship faster or build better, that's a win.

Related Projects

These gists are companion pieces to:

• TutorPro: Multi-tenant SaaS built with AI agent orchestration
• Leaderboard Fantasy: Fantasy golf platform and infrastructure migration case study
• AI Agents in Software Development: The full methodology behind the approach

Contributing Your Own

See a better way to do something? Have your own agent patterns to share? The beauty of gists is that anyone can fork, improve, and share. That's how we all get better at this.

Explore the gists: gist.github.com/jeremyronking

Questions or ideas? Open a discussion on one of the gists or reach out.