Houston We Have A Problem Scraper is a lightweight data collection tool designed to detect, log, and structure problem signals from target sources. It helps teams quickly identify issues, analyze patterns, and act on reliable, structured data.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project focuses on systematically extracting problem-related signals and transforming them into clean, usable datasets. It solves the challenge of manually tracking issues across sources by automating detection and organization, making it ideal for developers, analysts, and operations teams.
- Collects structured indicators related to system or content issues
- Normalizes raw inputs into analysis-ready data
- Designed for automation-friendly and repeatable workflows
- Suitable for monitoring, diagnostics, and reporting pipelines
| Feature | Description |
|---|---|
| Automated Detection | Identifies problem-related signals without manual review. |
| Structured Output | Produces clean, well-organized datasets for analysis. |
| Configurable Inputs | Allows flexible targeting of different data sources. |
| Scalable Execution | Handles small tests and large monitoring runs reliably. |
| Error Handling | Continues execution while logging partial failures. |
| Field Name | Field Description |
|---|---|
| source_url | URL or identifier where the issue was detected. |
| issue_type | Categorized type of detected problem. |
| message | Human-readable description of the issue. |
| severity | Estimated impact level of the problem. |
| detected_at | Timestamp when the issue was captured. |
| raw_payload | Original unprocessed data for traceability. |
houston-we-have-a-problem-scraper/
├── src/
│ ├── runner.py
│ ├── detectors/
│ │ ├── issue_detector.py
│ │ └── severity_classifier.py
│ ├── processors/
│ │ └── normalizer.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.json
│ └── output.sample.json
├── requirements.txt
└── README.md
- Developers use it to detect recurring issues, so they can debug systems faster.
- Data analysts use it to analyze problem trends, so they can identify root causes.
- Operations teams use it to monitor signals, so they can respond before escalation.
- Product teams use it to track failures, so they can improve reliability.
What kind of problems can this scraper detect? It is designed to capture structured problem signals such as error messages, anomaly indicators, or predefined issue patterns depending on configuration.
Is this suitable for continuous monitoring? Yes, it can be integrated into scheduled or automated workflows for ongoing detection.
Can the output be integrated with dashboards or alerts? The structured format makes it easy to connect with analytics tools, dashboards, or notification systems.
Primary Metric: Processes hundreds of issue signals per minute under standard workloads.
Reliability Metric: Maintains a high success rate with graceful handling of partial failures.
Efficiency Metric: Optimized for low memory usage during continuous runs.
Quality Metric: Produces consistently structured records with minimal data loss.
"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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