alterlab

v2.3.0 safe
3.0
Low Risk

AlterLab Python SDK

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk indicators across network, shell, and metadata checks. There is no strong evidence to suggest malicious intent or supply-chain attack.

  • Low network and shell risks
  • Single package from maintainer
Per-check LLM notes
  • Network: The observed network call patterns are likely for legitimate functionality, possibly API interactions.
  • Shell: No shell execution patterns detected, suggesting low risk of direct system command execution.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other red flags are present.

📦 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

  • 66 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 3.0

Found 2 network call pattern(s)

  • _delay self.client = httpx.AsyncClient( base_url=self.base_url, headers={
  • } async with httpx.AsyncClient( base_url=self._base_url, timeout=None )
✓ 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: alterlab.io

✓ 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 "AlterLab" 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 alterlab 
Create a mini-application named 'AlterLab Explorer' that leverages the core functionalities of the 'alterlab' Python package to explore and manipulate data in an interactive and user-friendly manner. This application will serve as a powerful tool for data scientists and researchers who need to quickly analyze and visualize complex datasets. Here are the key steps and features your project should include:

1. **Setup Environment**: Ensure you have Python installed along with the 'alterlab' package. If not already installed, use pip to install it.

2. **Data Importation**: Allow users to import various types of data files (CSV, Excel, SQL databases) into the application. Use 'alterlab' functions to efficiently load these datasets into memory.

3. **Data Manipulation**: Implement a feature where users can apply common data manipulation techniques such as filtering, sorting, and aggregation using 'alterlab'. For example, allow users to filter out rows based on specific conditions or sort data based on certain columns.

4. **Interactive Exploration**: Provide an interface where users can interactively explore the data through dynamic charts and graphs. Utilize 'alterlab' capabilities to generate real-time visualizations based on user interactions.

5. **Custom Analysis**: Enable users to define custom analysis scripts within the app using 'alterlab'. These scripts could perform more advanced operations like regression analysis, clustering, etc., depending on the user's needs.

6. **Export Results**: Finally, let users export their analyzed results back into different file formats or directly to cloud storage services, again utilizing 'alterlab' for efficient data handling.

By following these steps, you'll create a versatile and intuitive tool that showcases the power and flexibility of the 'alterlab' package in real-world applications.

💬 Discussion Feed

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