aitaem

v0.3.1 safe
4.0
Medium Risk

AITÆM: All Interesting Things Are Essentially Metrics - A Python library for generating data insights

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across various categories such as network, shell, and obfuscation. However, the metadata risk score is moderately high due to the maintainer's incomplete author information.

  • Low risk scores in network, shell, and obfuscation categories
  • Metadata risk due to incomplete maintainer information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function properly.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author name is missing or very short and appears to be associated with only one package, suggesting potential low activity or newness.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 25 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 25 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://chaturv3di.github.io/aitaem
  • Detailed PyPI description (4750 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

  • 92 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in chaturv3di/aitaem
  • Single author but highly active (100 commits)

🔬 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: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository chaturv3di/aitaem appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aitaem 
Create a web-based application using Flask that leverages the 'aitaem' Python package to analyze and visualize data insights from user-uploaded datasets. This application should allow users to upload CSV files, select specific columns for analysis, and receive visualized insights such as trend lines, correlation matrices, and outlier detection. The application should also provide downloadable reports in PDF format summarizing the key findings.

Steps to follow:
1. Set up a basic Flask web server.
2. Integrate file upload functionality allowing users to upload CSV files.
3. Implement a feature where users can select specific columns from the uploaded dataset for analysis.
4. Use 'aitaem' to process the selected data, generating metrics and insights.
5. Visualize these insights using libraries like Matplotlib or Seaborn, presenting them on the web app's interface.
6. Provide options to download the visualizations and a summary report in PDF format.
7. Ensure the application is user-friendly, with clear instructions and error handling for invalid uploads or selections.

Features:
- User authentication and session management
- Real-time visualization updates as users select different columns
- Email notification upon completion of analysis with a link to the generated report
- Integration with a cloud storage service for saving and sharing reports

How 'aitaem' is utilized:
- Utilize 'aitaem' functions to calculate various metrics such as mean, median, mode, standard deviation, etc., from the selected columns.
- Use 'aitaem' to detect outliers and anomalies within the dataset.
- Leverage 'aitaem' for generating correlation coefficients between selected variables and use these to create a correlation matrix.
- Apply 'aitaem' to identify trends over time if the dataset includes a temporal component.