AI Analysis
Final verdict: SUSPICIOUS
The package shows some potential risks, particularly due to the unavailability of its repository and the single-package maintainer profile, which may hint at less transparency and higher risk.
- Metadata risk due to missing repository
- Single-package maintainer profile
Per-check LLM notes
- Network: The observed network calls are typical for packages that interact with external APIs or services, but further investigation is needed to ensure the legitimacy of the URLs and API endpoints.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
- Metadata: The repository is not found and the maintainer has only one package, which could indicate potential risks.
Package Quality Overall: Low (2.0/10)
○ 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 (1479 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
score 4.5
Found 3 network call pattern(s)
} response = requests.get(url, headers=self.headers, params=params) response.r} response = requests.post(url, headers=self.headers, json=payload) response.rant/status" response = requests.get(url, headers=self.headers) response.raise_for_status
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: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Miten Solanki" 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 algogtt
Create a fully-functional mini-app that allows users to export their trading strategies from AlgoGTT using the Python package 'algogtt'. Your app should be designed as a command-line interface (CLI) tool where users can input their strategy details and have them exported directly to a specified file format (e.g., JSON). Here’s a step-by-step guide on how to build this mini-app: 1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed and create a virtual environment. Use pip to install the 'algogtt' package. 2. **Project Structure**: Define a clean project structure. Include necessary directories such as 'src', 'tests', and 'docs'. In the 'src' directory, create modules for handling CLI interactions, strategy export logic, and error handling. 3. **CLI Interface**: Develop a CLI interface using Python's built-in argparse module. This interface should accept user inputs like strategy name, description, and any other relevant parameters required by the 'algogtt' package. 4. **Integration with 'algogtt'**: Utilize the 'algogtt' package to connect to the AlgoGTT Strategy Export API. Implement functions within your code that take user inputs and use the 'algogtt' methods to export the strategy data. Ensure you handle authentication and authorization properly according to the API documentation. 5. **File Output**: Once the strategy is successfully exported, your app should output it to a file in the desired format. Provide options for the user to specify the file path and format (JSON). 6. **Error Handling and Logging**: Implement robust error handling mechanisms to catch and log any issues that occur during the process, such as network errors, invalid inputs, or API response errors. 7. **Testing**: Write unit tests for each of your functions using unittest or pytest frameworks. Test different scenarios including valid inputs, invalid inputs, and edge cases. 8. **Documentation**: Finally, document your code and provide usage instructions. Create a README file explaining how to set up and run the application, including setup steps, examples, and troubleshooting tips. Suggested Features: - Support for exporting multiple strategies at once. - Option to save exported files in different formats (CSV, YAML). - Interactive help and error messages for better user experience. - Integration with logging frameworks for detailed logs.
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