AuthenticTechnology

AuthenticTechnology / Agentic-Engineering

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How we use AI agents (Claude Code, Codex CLI, Gemini CLI) as first-class engineering teammates

15
1
100% credibility
Found Feb 20, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

This repository provides real-world examples, templates, and workflows for configuring AI coding assistants to function as collaborative multi-agent engineering teams.

How It Works

1
🔍 Discover Smart AI Teammates

You find a collection of tips and ready-made guides for turning AI helpers into a full engineering team that builds software together.

2
📥 Grab the Guides

Download the example instructions and skill files to use in your own projects.

3
📋 Set Up Your Layers

Place the universal, project-wide, and app-specific guides so your AI sees the right smarts at the right time.

4
⭐ Activate Special Skills

Type a special phrase like a team huddle, and your AI springs into action with reviews or fixes.

5
🤖 Watch the Team Collaborate

See multiple AI helpers working side-by-side, catching mistakes and improving your work like real coworkers.

6
🚀 Launch Your Creation

Follow the full workflow to turn ideas into a finished, reviewed project ready to share.

🎉 Build Faster and Smarter

Your software is complete, polished, and live—achieved with the power of your AI engineering crew.

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AI-Generated Review

What is Agentic-Engineering?

This Shell-based repo from AuthenticTechnology delivers ready-to-use configs and slash commands for Claude Code, Codex CLI, and Gemini CLI, turning AI agents into full engineering teammates for agentic AI engineering workflows. It solves the chaos of solo AI prompting by layering context across global, workspace, and repo scopes, enabling multi-agent code reviews and self-improving codebases. Developers get slash commands like /team_three_review for parallel model critiques and /ship for end-to-end implementation-to-PR flows.

Why is it gaining traction?

Unlike basic prompt libraries, it hooks devs with battle-tested multi-model parallelism—Claude, Codex, and Gemini catching bugs no single agent spots—and trigger words like "loopy" for autonomous iteration or "triple force" for merged analyses. The agentic context engineering shines in real production setups, accelerating agentic software engineering without custom scripting. Small teams notice faster ships and fewer regressions right away.

Who should use this?

Solo full-stack devs or small startup teams building React Native/Node.js apps who rely on Claude Code for daily coding. Backend engineers handling data pipelines via agentic AI for data engineering, or frontend folks automating reviews in monorepos. Ideal for anyone experimenting with agentic engineering workflows before scaling to platforms.

Verdict

Grab the global configs if you're on Claude Code—they're the most portable win despite 13 stars and 1.0% credibility signaling early maturity. Docs are solid with redacted production examples, but expect tweaks for your stack; test in a sandbox first.

(198 words)

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