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Code Catastrophe Predictor

Code Catastrophe Predictor uses Claude Opus 4.6's adaptive thinking to imagine how production systems fail before they do.

Code Video Cerebral Valley

Core trick

Prompts Claude Opus to generate multi-step cascade failure scenarios for an architecture, then a second agent validates each to cut false positives.

Tags

dev toolssecurity

Description

Code Catastrophe Predictor uses Claude Opus 4.6's adaptive thinking to imagine how production systems fail before they do. Unlike traditional testing that only finds what you look for, it generates creative, multi-step cascade failures the kind that cost Knight Capital $440 million in 45 minutes or took down AWS S3 with a single typo. Production failures are creative cascading disasters involving race conditions, load spikes, and edge cases that static analysis and unit tests miss entirely. The Solution: Opus 4.6 analyzes your architecture and generates realistic catastrophe scenarios with step-by-step cascades, impact estimates, and historical precedent from real incidents. A multi-agent system validates each scenario to prevent false positives. I initially built personal projects Telegram bot,automated trading system) during the hackathon but pivoted to test Opus 4.6's creative reasoning, discovering it excels at adversarial production system analysis.
syedatif4118/Code-Catastrophe-Predictor★ 00 forksPythonCreated Feb 16, 2026 · last push Feb 16, 2026

README excerpt

# 💀 Code Catastrophe Predictor

> **Built for Anthropic Claude Code Hackathon 2026**
> Because the worst bugs are the ones you didn't think to test for.

[![Demo](https://img.shields.io/badge/Demo-Live-brightgreen)](https://youtu.be/hSk4iBB69iQ)
[![Claude](https://img.shields.io/badge/Claude-Opus%204.6-purple)](https://www.anthropic.com)

---

## 🎯 The Problem

In 2012, Knight Capital lost **$440 million in 45 minutes**. A single bug.

In 2020, a typo brought down **AWS S3**, taking half the internet with it.

In 2019, **Disney+ collapsed** on launch day. Eight hours. Gone.

**Traditional testing finds what you look for. But production failures? They're creative. Multi-step. Cascading. They exploit the gaps between your tests.**

---

## 💡 The Solution

**Code Catastrophe Predictor** uses Claude Opus 4.6 to imagine how your code fails in production:

✅ **Creative Production Disasters** - Not "null pointer exception" - multi-step cascade failures
✅ **Historical Proof** - Cites real incidents that match the failure pattern
✅ **Multi-Agent Review** - 3 AI experts debate and validate each scenario
✅ **Production-Ready** - CLI tool, web interface, CI/CD integration

---

## 🚀 Quick Start

### 1. Install Dependencies

```bash
pip install -r requirements_catastrophe.txt
```

### 2. Set Your API Key

```bash
export ANTHROPIC_API_KEY="your-key-here"
```

### 3. Run the Demo

#### Web Interface (Recommended)
```bash
streamlit run catastrophe_demo.py
```

#### Command Line
```bash
pytho

Team