alkahest-py

v0.4.0 suspicious
5.0
Medium Risk

(No description)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low technical risks but has notable metadata issues such as missing author information and a lack of a public GitHub repository, raising suspicion about its origin and purpose.

  • missing author information
  • lack of public GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no attempt at stealing secrets or credentials.
  • Metadata: The package has some red flags, including an absent author and a lack of a GitHub repository, which could indicate potential risk.

📦 Package Quality Overall: Low (2.8/10)

✦ High Test Suite 9.0

Test suite present — 58 test file(s) found

  • Test runner config found: conftest.py
  • 58 test file(s) detected (e.g. test_ieas_types.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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 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 alkahest-py 
Your task is to develop a fully functional mini-application using the Python package 'alkahest-py'. Despite the lack of an official description, we know from its name and typical naming conventions that it might offer magical or transformative capabilities in data processing or system management. Let's assume 'alkahest-py' provides advanced data transformation and manipulation tools that can convert complex data structures into more manageable formats, similar to alchemy where base metals are transformed into gold.

The application you'll create is called 'DataAlchemy', a command-line tool designed to help data scientists and analysts quickly transform raw data into structured formats suitable for analysis and visualization. Here’s how your app will work:

1. **Data Input**: Users can input their raw data files (CSV, JSON, etc.) through the command line. The app will read these files and load them into memory.
2. **Transformation Process**: Using 'alkahest-py', the app will offer a series of transformation commands (e.g., filtering, aggregation, normalization) that users can apply to their data. These transformations should be flexible enough to accommodate various types of data and analytical needs.
3. **Output Generation**: Once the transformations are complete, users can specify the desired output format (JSON, CSV, SQL database insert statements, etc.). The app will then generate this output based on the transformed data.
4. **Logging & Feedback**: Throughout the process, the app will provide real-time feedback and logging to ensure transparency and ease of debugging.

**Suggested Features**:
- Support for multiple input file formats (CSV, JSON, Excel).
- A rich set of transformation commands (filtering, sorting, grouping, normalization).
- Output customization options allowing users to choose between different formats and destinations (file, console, database).
- User-friendly command-line interface with built-in help and examples.
- Error handling and logging mechanisms to assist with troubleshooting.

In utilizing 'alkahest-py', focus on leveraging its presumed capabilities for data transformation and manipulation. Your goal is to showcase how this package can streamline data preparation processes, making it easier for users to focus on analysis rather than data wrangling.

💬 Discussion Feed

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