mduongvandinh

mduongvandinh / engineering-failures-bible

Public

engineering failures

agent antigravity claude skills
19
4
89% credibility
Found Feb 23, 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

A set of guides packaged as skills for AI coding assistants to scan projects in six programming languages for around 800 common engineering failure patterns across 12 categories like security and performance.

How It Works

1
🔍 Find the helpful guide

You come across this collection of tips for spotting common mistakes in code across different languages.

2
📥 Download it easily

You grab the whole collection onto your computer with a quick download.

3
🛠️ Add to your AI helper

You run a simple setup tool that places these guides right where your smart coding assistant can use them.

4
💻 Open your project

You load up your own code project in your AI coding companion.

5
🔎 Request a full check

You type a simple phrase like '/ef-yourlanguage' and it scans everything for problems.

6
📋 Review the insights

You get a neat report listing issues by seriousness, with friendly suggestions on how to fix them.

✅ Better, safer code

Your project now runs smoother, stays secure, and is easier to maintain long-term.

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AI-Generated Review

What is engineering-failures-bible?

This repo packs ~800 common software engineering failure patterns—like memory leaks, race conditions, and security holes—into agent skills for AI coding tools such as Claude Code, Cursor, and Google Antigravity. Type a slash command like `/ef-rust` in your project, and it scans for issues across 12 domains in Rust, Go, PHP, Node.js, .NET, or Java Spring Boot, spitting out severity-ranked reports with fixes. It's your bible of engineering failures examples and case studies, tailored for code audits without touching your files.

Why is it gaining traction?

Unlike generic linters, it bundles language-specific anti-patterns from real-world flops—think concurrency bugs echoing engineering failures in history or the last decade—into dead-simple AI triggers that filter by domain or severity. The shell installer symlinks skills for instant updates via git pull, and it integrates tools like cargo clippy or PHPStan for deeper checks. Devs dig the regex-powered scans that catch N+1 queries or unbounded caches before prod disasters.

Who should use this?

Backend teams building in Rust, Go, or Spring Boot who rely on Cursor/Claude for daily coding and want quick prod-readiness audits. Security leads scanning for injections/CSRF in Node.js/PHP apps, or leads enforcing standards across polyglot repos. Skip if you're not using those AI agents or prefer full IDE analyzers.

Verdict

Grab it if you're in the supported langs and AI workflow—solid docs and MIT license make it easy to extend with custom patterns, despite 19 stars signaling early maturity. Low 0.8999999761581421% credibility score means vet findings manually, but it's a practical github engineering handbook for failure-prone code.

(198 words)

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