auto-insights-generator

v0.1.0 suspicious
4.0
Medium Risk

Automated EDA for pandas DataFrames, powered by the Anthropic API.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, obfuscation, and credential activities. However, the metadata suggests a low-effort and potentially inactive maintainer, which raises suspicion.

  • Low metadata quality
  • Potentially inactive maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution detected, indicating the package does not execute system commands, which is expected unless command-line interactions are part of its functionality.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low effort and possibly inactive maintainer, raising suspicion but not definitive evidence of malice.

📦 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

  • Brief PyPI description (323 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

  • 82 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

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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 auto-insights-generator 
Create a data analysis mini-app using the 'auto-insights-generator' Python package, which automates Exploratory Data Analysis (EDA) tasks for pandas DataFrames via the Anthropic API. This app will allow users to upload CSV files, perform automated EDA, and visualize key insights from their data without writing extensive code. Here’s a detailed plan on how to build it:

1. **Setup Environment**: Start by setting up a virtual environment and installing necessary packages including 'auto-insights-generator', pandas, streamlit (for the web interface), and matplotlib/seaborn for data visualization.

2. **Design User Interface**: Use Streamlit to design a simple yet intuitive user interface where users can upload their CSV files directly from their browsers. Ensure there’s a button to initiate the EDA process after file upload.

3. **Implement File Upload & Processing**: After a file is uploaded, read the CSV into a pandas DataFrame. Validate the file format and notify the user if the upload was successful or not.

4. **Automated EDA Execution**: Utilize 'auto-insights-generator' to automatically generate insights from the uploaded DataFrame. This includes statistical summaries, visualizations, and key observations about the dataset's structure and contents.

5. **Display Insights**: Present the generated insights back to the user through the Streamlit app. This could include tables, charts, and textual summaries of the EDA results. Make sure the display is interactive and allows users to explore different aspects of the data.

6. **Error Handling & Feedback**: Implement robust error handling to manage any issues during file processing or EDA generation. Provide clear feedback messages to guide users through potential problems or limitations.

7. **Testing & Optimization**: Test the application thoroughly with various datasets to ensure reliability and accuracy of the generated insights. Optimize the performance and user experience based on initial testing feedback.

8. **Deployment**: Deploy the final version of your app on a platform like Heroku or AWS so it can be accessed by anyone interested in performing quick and automated EDA on their datasets.

By following these steps, you’ll create a powerful tool that democratizes access to data insights, making complex data analysis more accessible and straightforward.

💬 Discussion Feed

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