antupy

v0.6.2 safe
3.0
Low Risk

Toolkit for energy engineering

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all evaluated categories with no direct evidence of malicious activities. The only notable concern is the metadata risk due to low activity and a new maintainer.

  • No network or shell execution detected
  • No signs of code obfuscation or credential harvesting
  • Metadata risk due to low activity and new maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The low activity and new maintainer suggest potential risk, but there's no concrete evidence of malicious intent.

📦 Package Quality Overall: Low (4.2/10)

○ 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 (5591 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

  • 188 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 85 commits in DavidSaldivia/antupy
  • Small but multi-author team (3–4 contributors)

🔬 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: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "David Saldivia" 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 antupy 
Develop a comprehensive mini-application that assists engineers and researchers in the field of renewable energy with their daily tasks. This application will utilize the 'antupy' toolkit, which provides essential functions for energy engineering calculations and analyses. Here’s a detailed breakdown of the project requirements and steps to implement it:

1. **Project Title**: Renewable Energy Analysis Tool (REAT)
2. **Core Functionality**:
   - Calculate the efficiency of various renewable energy sources (solar, wind, hydro).
   - Analyze energy consumption patterns and suggest improvements.
3. **Features**:
   - User-friendly interface allowing input of specific parameters such as location, time of year, and type of energy source.
   - Graphical representation of energy production and consumption over time.
   - Comparative analysis between different energy sources based on user inputs.
4. **Implementation Steps**:
   - **Step 1**: Set up the project environment by installing necessary libraries including 'antupy'.
   - **Step 2**: Create a data model using 'antupy' functionalities to calculate efficiencies and analyze data.
   - **Step 3**: Develop a graphical user interface (GUI) where users can input parameters and visualize results.
   - **Step 4**: Integrate 'antupy' methods into the backend logic to perform real-time calculations and analyses.
5. **Utilization of 'antupy' Package**:
   - Use 'antupy' for core calculations related to energy conversion efficiencies, solar irradiance, wind speed impact, etc.
   - Leverage 'antupy' tools for data analysis to predict trends and optimize energy usage.
6. **Additional Enhancements**:
   - Include a feature to save and load analysis results.
   - Allow exporting graphs and reports in PDF format.
7. **Testing & Validation**:
   - Validate the application by comparing its outputs against known benchmarks and theoretical values provided by 'antupy'.
8. **Deployment**:
   - Prepare a documentation detailing the setup process, usage instructions, and API if applicable.
   - Deploy the application either as a desktop application or a web-based tool depending on user preference.

💬 Discussion Feed

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