🤖 AI Analysis

Final verdict: SAFE

The package akaitools v0.1.1 exhibits minimal risk indicators, with no detected network calls, shell execution, or obfuscation. The primary concern is the presence of a non-HTTPS link and the maintainer having only one package, suggesting a potentially new or less active developer.

No network calls or shell executions detected
Non-HTTPS link present in metadata
Maintainer has only one package

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external API access.
Shell: No shell execution detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which might indicate a new or less active account. A non-HTTPS link is present but not directly related to the package's functionality.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (7402 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

56 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 94 commits in dogusariturk/akaitools
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 score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://kkr.issp.u-tokyo.ac.jp/

✓ Git Repository History

Repository dogusariturk/akaitools appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Doguhan Sariturk" 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 akaitools

Create a Python-based mini-application called 'AkaiAnalyzer' that serves as a comprehensive tool for analyzing output files generated by the AkaiKKR software, which is widely used in computational materials science. This application will utilize the 'akaitools' package to parse and interpret these complex files, providing users with insightful visualizations and summaries of the electronic structure data contained within them.

### Key Features:
1. **File Parsing**: Implement a feature that allows users to upload their AkaiKKR output files. Use the 'akaitools' package to parse these files and extract key information such as band structures, density of states, and eigenvalues.
2. **Visualization**: Develop an interactive visualization module using libraries like matplotlib or seaborn to display the parsed data in a user-friendly manner. Users should be able to view band structures, density of states plots, and other relevant graphs.
3. **Summary Reports**: Automatically generate summary reports based on the parsed data. These reports should include statistical analyses and insights into the electronic properties of the material being studied.
4. **Customization Options**: Allow users to customize the visualization settings, such as color schemes, line styles, and plot types, to suit their preferences and research needs.
5. **Export Functionality**: Provide options for exporting the visualizations and summary reports as high-quality images or PDF documents.
6. **Error Handling and Documentation**: Ensure robust error handling to manage common issues like corrupted files or missing data. Additionally, create comprehensive documentation to guide users through the setup and usage of the application.

### Utilizing 'akaitools':
- **Parsing Files**: Use the 'akaitools.read_file()' function to read and parse the input file. This function should return a structured data format (e.g., dictionary or pandas DataFrame) that contains all the necessary information for further analysis.
- **Data Extraction**: Leverage specific functions from 'akaitools', such as 'extract_band_structure()' and 'extract_density_of_states()', to retrieve targeted data segments from the parsed file.
- **Integration with Visualization Libraries**: Once the data is extracted, integrate it with popular Python visualization libraries to create dynamic and informative plots.
- **Report Generation**: Use the extracted data to populate templates for generating HTML or LaTeX-based summary reports.

This project aims to streamline the process of analyzing AkaiKKR output files, making it easier for researchers and students to gain valuable insights from their computational experiments.