analytics-toolkit

v1.3.7.3 suspicious
4.0
Medium Risk

Shared Python utilities for SQL, Excel, and date helpers.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of obfuscation or credential harvesting, which is positive. However, given that it's newly uploaded and the author has limited online presence, there's a slight increase in suspicion regarding its legitimacy and intentions.

  • Low obfuscation risk
  • Low credential risk
  • Metadata risk due to new upload and limited author information
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is newly uploaded and the author has few credentials, suggesting potential risk but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.0/10)

○ 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 (3219 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in Karapsin/analytics_toolkit
  • 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Karapsin/analytics_toolkit appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Package is very new: uploaded 2 day(s) ago
  • Author "analytics_toolkit contributors" 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 analytics-toolkit 
Create a small data analysis tool called 'DataInsight' using Python, which leverages the 'analytics-toolkit' package for its functionalities. This tool should allow users to perform basic data analysis tasks such as importing data from SQL databases and Excel files, filtering data based on specific dates, and generating summary statistics.

Step 1: Set up your development environment with Python and install the 'analytics-toolkit' package.
Step 2: Design a user-friendly command-line interface where users can interact with the tool.
Step 3: Implement functionality to connect to a SQL database and execute queries to fetch data. Use the 'analytics-toolkit' SQL utilities to simplify this process.
Step 4: Add support for reading Excel files directly into the application, utilizing the Excel utilities provided by 'analytics-toolkit'.
Step 5: Integrate date helper functions from 'analytics-toolkit' to allow users to filter data based on specific date ranges or periods.
Step 6: Develop features to calculate summary statistics like mean, median, mode, standard deviation, etc., on the imported data.
Step 7: Extend the application to visualize the analyzed data using simple plots or charts, though this visualization part does not need to use 'analytics-toolkit', you can use another library like matplotlib.

Suggested Features:
- Support for multiple SQL databases (e.g., MySQL, PostgreSQL)
- Ability to handle large datasets efficiently
- Enhanced date filtering options (e.g., last month, next week)
- Customizable summary statistics based on user input
- Exporting analysis results back to Excel or CSV files

Utilization of 'Analytics-Toolkit':
- SQL utilities for querying databases more easily and efficiently.
- Excel utilities for seamless data import/export operations.
- Date helper functions for flexible data filtering.

💬 Discussion Feed

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