KevinKE93

KevinKE93 / Dev_Agent_OPC

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A automatic dev agent from idea → spec → design → plan → build → test → review → ship and included many roly and responsibility. Add many feature and modified for OPC or small business team. Based on @addyosmani agent-skills

agent-skills automation-agent claude codex devops
18
2
100% credibility
Found May 11, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

Dev Agent OPC is a structured workflow framework that guides AI coding agents through verifiable phases from idea refinement to shipping production-ready software.

How It Works

1
🔍 Discover Dev Agent OPC

You hear about this helpful guide that turns chaotic AI coding into a smooth, step-by-step process for building real projects.

2
💭 Start a New Idea

Tell your AI helper to use this workflow for your project idea, picking a type like a user interface or simple tool.

3
🎨 Design with Care

Create clear designs and approved visuals first, ensuring everything looks right before building starts.

4
📋 Plan and Build Step by Step

Break it into small tasks, build pieces one by one, and run checks to keep quality high.

5
✅ Test and Review Thoroughly

Run tests, check for issues, and review everything to make sure it's solid and secure.

6
🚀 Ship Your Project

Pass the final checks and launch your finished project confidently with notes on how to update it later.

🎉 Enjoy Reliable Results

Your AI-built project is now live, professional, and ready for real use without surprises.

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Star Growth

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AI-Generated Review

What is Dev_Agent_OPC?

Dev Agent OPC is a shell-based workflow framework that turns AI coding agents into automatic developers, guiding them from raw idea to shipped product via structured phases like spec, design, plan, build, test, review, and ship. It enforces quality gates—such as approved UI design assets before coding and PDCA reviews before release—using a local CLI tool for checks and state management. Built on @addyosmani's agent-skills, it supports automatic GitHub commits, pushes, documentation, and releases in AI-driven flows.

Why is it gaining traction?

It stands out by adding verifiable delivery layers to agent skills, like strict UI asset checks and runtime hooks that prevent skipping tests or designs, making AI output production-ready without manual babysitting. Developers notice the seamless adapters for tools like Claude Code, Gemini CLI, and Codex, plus automatic GitHub release notes and dependency handling. The PDCA handoffs and ship checklists reduce rework in small-team sprints.

Who should use this?

Solo devs or small business teams building UI apps, APIs, or agent tools with AI assistants, especially those frustrated by hallucinated code skipping specs or tests. Frontend teams handling automatic device configuration or UI flows benefit from enforced design gates; backend folks get reliable planning for libraries and docs.

Verdict

Worth a spin for AI-heavy workflows—install via simple CLI commands and reference in prompts—but at 18 stars and 1.0% credibility, it's v0.2 early access with solid docs yet unproven at scale. Pair it with mature agents for best results.

(198 words)

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