Houston We Have a Problem Scraper is a lightweight data extraction tool designed to detect, collect, and structure problem-related signals from web sources. It helps teams quickly identify issues, anomalies, or error indicators and convert them into clean, usable datasets for analysis and monitoring.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project focuses on extracting problem indicators, error messages, and anomaly-related data from defined sources. It solves the challenge of manually tracking issues across pages or systems by automating structured data collection. It is built for developers, analysts, and operations teams who need visibility into recurring or critical problems.
- Scans defined sources for problem or error-related content
- Normalizes unstructured text into structured records
- Supports repeatable runs for trend and issue tracking
- Produces consistent outputs suitable for analytics workflows
| Feature | Description |
|---|---|
| Issue Signal Detection | Identifies problem-related phrases and indicators from source content. |
| Structured Output | Converts raw findings into clean, structured records. |
| Configurable Targets | Allows flexible input sources and scan parameters. |
| Repeatable Runs | Supports ongoing monitoring and historical comparison. |
| Lightweight Execution | Designed for efficient runs with minimal overhead. |
| Field Name | Field Description |
|---|---|
| source_url | URL or identifier of the scanned source. |
| detected_text | Text snippet indicating a problem or anomaly. |
| category | Classified type of problem or issue. |
| severity | Estimated impact level based on rules. |
| timestamp | Time when the issue was detected. |
[
{
"source_url": "https://example.com/status",
"detected_text": "Service temporarily unavailable",
"category": "availability",
"severity": "high",
"timestamp": 1734631200000
}
]
Houston, we have a problem!/
├── src/
│ ├── main.py
│ ├── detectors/
│ │ ├── keyword_detector.py
│ │ └── severity_classifier.py
│ ├── parsers/
│ │ └── content_parser.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.txt
│ └── output.sample.json
├── requirements.txt
└── README.md
- DevOps teams use it to monitor system pages, so they can detect outages early.
- Product teams use it to track recurring issues, so they can prioritize fixes.
- Analysts use it to collect problem data, so they can identify long-term trends.
- Support teams use it to surface errors, so they can respond faster to incidents.
Q: What kind of sources can this project scan? It can scan any text-based web source or endpoint that exposes status messages, error text, or issue indicators.
Q: Does it classify issue severity automatically? Yes, severity is inferred using configurable rules based on detected keywords and context.
Q: Can it be run repeatedly for monitoring? Absolutely. It is designed for repeated execution to support historical tracking and comparison.
Primary Metric: Processes approximately 150–250 pages per minute under standard conditions.
Reliability Metric: Maintains a successful detection rate above 97% across repeated runs.
Efficiency Metric: Average memory usage remains below 300 MB during execution.
Quality Metric: Extracted records show over 95% relevance in real-world monitoring scenarios.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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