tmdgusya

tmdgusya / engineering-discipline

Public

SKILL for engineering-discipline

tmdgusya.github.ioengineering-discipline
44
9
100% credibility
Found Apr 05, 2026 at 44 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
HTML
AI Summary

A plugin providing structured workflows and skills for AI coding tools to plan, implement, review, debug, clean, and optimize software changes reliably.

How It Works

1
🔍 Find the Helper

You discover a set of smart guides that make AI assistants follow good rules when building or fixing software.

2
📥 Add It In

You quickly connect these guides to your AI coding companion so it can use them right away.

3
💭 Share Your Idea

You simply describe what you want, like adding a feature, fixing a glitch, or speeding things up.

4
Figure Out the Size
🚀
Quick Fix

It plans the steps, makes the changes, and double-checks everything in one smooth flow.

📅
Big Adventure

It maps out milestones, works steadily over time, and saves progress so nothing gets lost.

5
🔧 Magic Happens

Special helpers kick in automatically to clean messy code, hunt down bugs, or make things faster.

6
🔍 Review and Tidy

Your AI looks everything over, simplifies where needed, and ensures it all works perfectly.

🎉 Job Done Right

You end up with clean, reliable software that runs great and does exactly what you wanted.

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Star Growth

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AI-Generated Review

What is engineering-discipline?

This GitHub skill turns AI coding agents like Claude Code, Cursor, and Gemini CLI into disciplined engineers by chaining workflows from vague requests to verified code. It handles clarification, complexity-based planning, worker-validator execution, isolated reviews, systematic debugging, AI slop cleanup, and measurement-driven optimization—solving the chaos of unguided AI code gen. Built as HTML skills with TypeScript examples, it installs via plugin marketplaces or npx, enforcing engineering discipline codes across disciplines ranked by difficulty or salary.

Why is it gaining traction?

Unlike basic prompts, it auto-triggers on phrases like "long run" for multi-day milestone orchestration with checkpoints, or "simplify" for parallel reuse/quality checks—delivering production-ready outputs without babysitting. Devs love the guardrails like Rob Pike's rules for perf bottlenecks and condition-based waiting for flaky tests, plus info-isolated validation that catches slop early. As a GitHub skill marketplace entry for Anthropic Claude and similar, it fits seamlessly into skill directories, boosting reliability in AI-driven workflows.

Who should use this?

Backend devs using Claude Code for API refactors or long-running features, frontend teams debugging flaky tests in Cursor, or any engineer ranking engineering disciplines by salary who wants systematic debugging over guesswork. Ideal for solo coders handling complex tasks solo or teams enforcing discipline in AI-assisted sprints.

Verdict

Try it if you're deep in AI coding agents—solid docs and quick installs make the 44 stars and 1.0% credibility score forgivable for an early MIT-licensed plugin. Still maturing, so pair with manual reviews until adoption grows.

(178 words)

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