arthur-observability-sdk

v2.1.610 safe
3.0
Low Risk

Arthur Observability SDK — telemetry, prompt management, and framework instrumentation

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network, shell, and obfuscation activities. While the metadata quality is low and maintenance seems sparse, these factors alone do not suggest malicious intent.

  • Low network, shell, and obfuscation risk scores.
  • Poor metadata quality and low maintenance activity.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low maintenance and poor metadata quality, but there's no clear indication of malicious intent.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5339 chars)
○ 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

  • Type checker (mypy / pyright / pytype) referenced in project
○ 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

No suspicious network call patterns found

✓ 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: arthur.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with arthur-observability-sdk 
Create a mini-application called 'PromptTracker' using Python that leverages the 'arthur-observability-sdk' package to manage prompts and track their usage within the application. This application will serve as a tool for developers to test different prompts and monitor how they perform under various conditions. Here are the steps and features you need to implement:

1. **Setup**: Initialize your project with the necessary dependencies, including 'arthur-observability-sdk'. Ensure you have a clear structure for your project with separate folders for data, logs, and configurations.
2. **Prompt Management**: Implement a feature where users can input, store, and retrieve different prompts. Each prompt should have metadata such as creation date, user ID, and description.
3. **Telemetry Tracking**: Use 'arthur-observability-sdk' to log detailed telemetry data whenever a prompt is executed. Track metrics like execution time, success/failure status, and any errors encountered.
4. **Framework Instrumentation**: Integrate 'arthur-observability-sdk' to automatically instrument key parts of your application. This includes logging entry and exit points for functions and tracking resource usage.
5. **User Interface**: Develop a simple command-line interface (CLI) that allows users to interact with PromptTracker. Users should be able to add new prompts, run existing ones, and view detailed reports on their performance.
6. **Reporting**: Provide a feature that generates summary reports based on the telemetry data collected. These reports should highlight trends in prompt performance over time and identify any issues that occurred during execution.
7. **Configuration**: Allow users to customize the behavior of PromptTracker through configuration files. Options might include log level, data retention policies, and alert thresholds.
8. **Testing and Documentation**: Write unit tests to ensure all features work as expected and provide comprehensive documentation for setting up and using PromptTracker.

By following these steps and incorporating the functionalities provided by 'arthur-observability-sdk', PromptTracker will become a valuable tool for managing and analyzing prompts in development environments.

💬 Discussion Feed

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