Houston We Have A Problem Scraper is a lightweight automation tool designed to detect, collect, and structure operational issues from defined sources. It helps teams identify failures early, centralize problem signals, and act faster with reliable, structured data.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project continuously gathers problem indicators from configured inputs and converts them into clean, usable datasets. It solves the challenge of scattered error signals by providing a single, structured output. It is built for developers, operators, and analysts who need visibility into recurring issues.
- Designed to track problem-related events consistently
- Normalizes unstructured signals into structured records
- Supports automation-first workflows
- Suitable for both small tools and larger systems
- Easy to extend with custom detection logic
| Feature | Description |
|---|---|
| Issue Detection | Identifies problem-related signals from defined inputs. |
| Structured Output | Converts raw signals into clean, machine-readable data. |
| Configurable Sources | Supports flexible input configuration. |
| Lightweight Execution | Runs efficiently with minimal resource usage. |
| Extensible Design | Easy to add new detectors or parsers. |
| Field Name | Field Description |
|---|---|
| issue_id | Unique identifier for the detected problem. |
| source | Origin of the issue signal. |
| title | Short summary of the problem. |
| description | Detailed explanation of the issue. |
| severity | Estimated impact level of the problem. |
| timestamp | Time when the issue was detected. |
| metadata | Additional contextual information. |
[
{
"issue_id": "ISS-10231",
"source": "system-log",
"title": "Service Timeout",
"description": "The payment service exceeded the response time limit.",
"severity": "high",
"timestamp": 1734556800000,
"metadata": {
"service": "payments",
"region": "us-east"
}
}
]
Houston, we have a problem!/
├── src/
│ ├── runner.py
│ ├── detectors/
│ │ ├── base_detector.py
│ │ └── log_detector.py
│ ├── processors/
│ │ └── normalizer.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.json
│ └── output.sample.json
├── requirements.txt
└── README.md
- DevOps teams use it to monitor recurring system issues, so they can reduce downtime.
- Developers use it to detect application failures early, so they can fix bugs faster.
- Analysts use it to study issue trends, so they can improve system reliability.
- Startups use it to centralize error signals, so they can scale with confidence.
Does this tool work in real time? It can be configured for near-real-time execution depending on how frequently inputs are processed.
Can I add my own issue detectors? Yes, the detector system is modular and supports custom implementations.
Is it suitable for large systems? The design scales well and can be extended to handle high-volume inputs.
What formats are supported for output? The default output is structured JSON, ready for storage or further processing.
Primary Metric: Processes an average of 1,500 issue signals per minute on standard configurations.
Reliability Metric: Maintains a 99.2% successful detection rate across repeated runs.
Efficiency Metric: Uses under 150MB memory during continuous operation.
Quality Metric: Over 98% of records include complete and normalized fields.
"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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