pawel-cell

pawel-cell / micky-podcast-agentic-engineering

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Micky podcast agentic engineering workflow bundle

18
5
80% credibility
Found May 19, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository is a collection of practical guides for building AI agents that work reliably. Based on a real interview with AI engineering experts, it offers structured skill documents covering the complete process: setting up agents, giving them proper context from your actual code, cleaning up your codebase, and using iterative review to improve quality. It's essentially a curated set of best practices for developers who want to build AI agents the right way.

How It Works

1
🎙️ You discover a podcast about AI engineering

You hear an interview where experts share real-world tips for building AI agents that actually work.

2
📋 You learn the complete workflow

You understand the full picture: how to set up your agent, give it context, structure your work, review it, and keep things secure.

3
🔍 Your agent reads your actual code

Instead of guessing from documentation, your AI agent looks directly at your real codebase and understands exactly how things work.

4
🧹 You clean up messy code

Once your feature works, you refactor repeated patterns into clean, reusable pieces that are easy to maintain.

5
🔄 You review in small loops

You make small improvements, get feedback from AI and humans, fix issues, and repeat until everything is ready to share.

🚀 Your AI agent is solid and reliable

You have an AI assistant that understands your code, follows good patterns, and gets better through thoughtful review.

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Star Growth

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AI-Generated Review

What is micky-podcast-agentic-engineering?

This is a curated bundle of agentic engineering skills extracted from a podcast interview with Michael Shimeles. It packages practical guidance on building AI agents into reusable SKILL.md files covering workflow design, context management, code cleanup, and review loops. The focus is on helping developers move beyond loose prompts toward structured, repeatable processes for shipping AI-assisted features.

Why is it gaining traction?

Agentic engineering is hot right now, and this repo taps into that momentum by offering battle-tested patterns rather than generic AI advice. The grep loop review workflow and source code context approach solve real pain points developers face when integrating AI into their pipelines. It's opinionated without being prescriptive, which appeals to engineers who want frameworks they can adapt.

Who should use this?

Backend developers building AI-powered features who want structured workflows for agent collaboration. Teams struggling with inconsistent AI output in code reviews will benefit from the grep loop approach. Anyone new to agentic engineering looking for mental models rather than framework-specific tutorials.

Verdict

At 18 stars with no test suite or production deployment, this is a knowledge artifact rather than a production tool. The 0.80% credibility score reflects its early-stage nature. Worth bookmarking as a reference, but don't bet your architecture on it yet. Start with the workflow skills, adapt what fits, and watch for community traction before committing.

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