aignostics

v1.4.0 safe
3.0
Low Risk

🔬 Python SDK providing access to the Aignostics Platform. Includes Aignostics Launchpad (Desktop Application), Aignostics CLI (Command-Line Interface), example notebooks, and Aignostics Client Library.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no evidence of malicious intent or unusual behavior. The network calls appear to be for expected functionality.

  • Network risk is moderate due to file transfer activities
  • Sparse author metadata
Per-check LLM notes
  • Network: The observed network calls are likely for downloading and uploading files, which could be part of the package's intended functionality but may also indicate potential data transfer risks.
  • Shell: No shell execution patterns were detected, suggesting a low risk of direct command execution from this package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is sparse, suggesting a potential lack of transparency or a new, less established maintainer.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
  • Classifier: Framework :: Pytest
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aignostics.readthedocs.io/en/latest/
  • Detailed PyPI description (53980 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 164 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 13 unique contributor(s) across 100 commits in aignostics/python-sdk
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • g try: response = requests.get(download_url, stream=True, timeout=60) response.rais
  • crc32c.CRC32CHash() with requests.get(signed_url, stream=True, timeout=60) as stream: stre
  • k response = requests.put( signed_upload_url,
✓ 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: aignostics.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aignostics/python-sdk 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 aignostics 
Your task is to create a Python-based mini-application that leverages the 'aignostics' package to automate the analysis and visualization of complex data sets. This application will serve as a bridge between the user and the powerful functionalities offered by the Aignostics Platform, including but not limited to data preprocessing, statistical analysis, and interactive visualizations.

Step 1: Set up your development environment by installing Python and the 'aignostics' package. Ensure you have the latest version of the package to access all its features.

Step 2: Design a simple GUI using a library such as PyQt or Tkinter, which integrates with the Aignostics Launchpad to allow users to upload their datasets directly into the application.

Step 3: Implement functionality within the application that utilizes the Aignostics Client Library to perform automated data cleaning and preprocessing tasks. This includes handling missing values, outliers, and data type conversions.

Step 4: Integrate the Aignostics CLI within your application to enable users to run predefined scripts for advanced statistical analyses on their data. These scripts could include hypothesis testing, regression models, and clustering algorithms.

Step 5: Use the example notebooks provided by the 'aignostics' package to generate insightful visualizations of the analyzed data. The application should support various types of plots such as scatter plots, histograms, and heatmaps, allowing users to explore different aspects of their data visually.

Suggested Features:
- User-friendly interface for easy data upload and analysis initiation.
- Comprehensive data preprocessing tools accessible through a simple click.
- Preconfigured scripts for common statistical tests and machine learning models.
- Interactive visualizations that update based on the selected analysis options.
- Export capabilities to save results and visualizations in various formats.

Remember to document your code thoroughly and provide clear instructions for users on how to utilize each feature effectively. Your goal is to create an intuitive tool that empowers users to gain deep insights from their data without needing extensive technical expertise.

💬 Discussion Feed

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