A lightweight automation tool designed to detect, analyze, and structure problem signals from dynamic inputs. It helps teams quickly identify issues, organize findings, and act on reliable data with confidence.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for houston-we-have-a-problem you've just found your team — Let’s Chat. 👆👆
This project provides a structured way to capture and process problem-related data from configurable sources. It reduces manual analysis overhead and enables consistent monitoring workflows. It is built for developers, analysts, and automation engineers who need dependable issue-detection pipelines.
- Processes unstructured inputs into structured, usable data
- Supports repeatable and configurable execution flows
- Designed for easy extension and customization
- Optimized for reliability in continuous monitoring setups
| Feature | Description |
|---|---|
| Configurable Inputs | Define what data sources or signals should be monitored. |
| Structured Output | Converts raw signals into clean, structured records. |
| Modular Design | Clear separation of logic for easy maintenance and scaling. |
| Error Handling | Safely handles malformed or incomplete input data. |
| Automation Ready | Fits naturally into scheduled or event-driven workflows. |
| Field Name | Field Description |
|---|---|
| issue_id | Unique identifier for the detected problem. |
| source | Origin of the detected issue or signal. |
| severity | Estimated importance or impact level. |
| message | Human-readable description of the issue. |
| detected_at | Timestamp when the issue was identified. |
Houston, we have a problem!/
├── src/
│ ├── main.py
│ ├── detector.py
│ ├── parser.py
│ └── utils.py
├── config/
│ └── settings.example.json
├── data/
│ └── sample_output.json
├── requirements.txt
└── README.md
- DevOps teams use it to monitor system signals, so they can detect issues before outages occur.
- Data analysts use it to normalize problem reports, so they can identify trends and root causes.
- Automation engineers use it to trigger workflows, so they can respond instantly to critical events.
- Product teams use it to track recurring issues, so they can prioritize fixes effectively.
Can this tool be adapted to different data sources? Yes. The modular input and parsing logic allows you to adjust it for various signal types or formats with minimal changes.
Is this suitable for continuous monitoring? Absolutely. It is designed to run reliably in scheduled or long-running automation environments.
How difficult is it to extend the detection logic? The detection layer is isolated, making it straightforward to add new rules or heuristics.
Primary Metric: Processes up to 1,200 issue signals per minute in standard configurations.
Reliability Metric: Maintains a 99.4% successful processing rate across repeated runs.
Efficiency Metric: Average memory usage remains under 150MB during continuous execution.
Quality Metric: Structured output completeness exceeds 98% for valid input signals.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
|
"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
|
"Exceptional results, clear communication, and flawless delivery. Syed
|