anomalo

v0.42.1 safe
3.0
Low Risk

Python bindings for the Anomalo API

🤖 AI Analysis

Final verdict: SAFE

The package is assessed as safe with a moderate metadata risk due to the maintainer's limited presence, but other risks are minimal.

  • Low risk of network exploitation
  • No shell execution or obfuscation detected
  • Minimal credential risk
Per-check LLM notes
  • Network: The network call pattern suggests the package may be making external requests, which could be legitimate if it's designed to fetch data or updates.
  • Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk to secrets or credentials.
  • Metadata: The maintainer has only one package and lacks a GitHub repository, which may indicate a less experienced or potentially suspicious account.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 93 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • n.txt" response = requests.get(url) if not response.ok: raise R
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: anomalo.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Anomalo" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anomalo 
Create a real-time anomaly detection mini-application using the 'anomalo' Python package. This application will monitor a set of time-series data streams for any unusual patterns or anomalies, which could indicate issues such as sudden spikes in traffic, unexpected drops in performance metrics, or other irregularities that may require immediate attention.

### Project Overview:
- **Name**: Real-Time Anomaly Detector
- **Objective**: To develop a tool that can ingest live data from various sources (e.g., network traffic, server logs, user activity), process it through the 'anomalo' API, and alert users via email or SMS when anomalies are detected.

### Core Features:
1. **Data Ingestion**: Implement functionality to pull data from multiple sources (CSV files, REST APIs, databases).
2. **Real-Time Processing**: Use the 'anomalo' package to analyze incoming data streams in real-time, identifying potential anomalies based on statistical deviations.
3. **Visualization**: Provide a simple web interface where users can view the data trends and see highlighted anomalies.
4. **Alerting System**: Integrate with services like Twilio or SendGrid to send alerts (email/SMS) when anomalies are detected.
5. **Configuration Management**: Allow users to customize the sensitivity of anomaly detection, set up different thresholds for different data types, and define specific alert conditions.

### How to Utilize 'anomalo':
- Import the necessary modules from the 'anomalo' package at the beginning of your script.
- Initialize the client with your API credentials.
- Define functions to handle data ingestion, processing, and visualization.
- Use the 'anomalo.detect_anomalies()' method to identify anomalies in the data stream.
- Implement logic to trigger alerts based on the results of the anomaly detection.
- Ensure that all components are modular and reusable so that new data sources or alert mechanisms can be easily integrated in the future.

💬 Discussion Feed

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