aimz

v0.12.0 suspicious
4.0
Medium Risk

Scalable probabilistic impact modeling

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal direct risks but raises concerns due to missing maintainer information and potential inactivity, suggesting possible supply-chain risks.

  • Metadata risk due to missing maintainer's author name
  • New or inactive account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell executions detected, indicating no immediate risk of unauthorized system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activities aimed at stealing secrets or credentials.
  • Metadata: The maintainer's author name is missing and the account seems new or inactive, raising some concerns but not definitive proof of malice.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 19 test file(s) found

  • 19 test file(s) detected (e.g. test_array_loader.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "documentation" -> https://aimz.readthedocs.io/
  • Detailed PyPI description (4713 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

  • 145 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in markean/aimz
  • Two distinct contributors found

🔬 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: lilly.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository markean/aimz 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 aimz 
Create a mini-application named 'ImpactAssessor' that leverages the 'aimz' package for scalable probabilistic impact modeling. This application will enable users to input various environmental factors (such as pollution levels, deforestation rates, and climate change indicators) to assess their potential impacts on specific regions over time. The goal is to provide policymakers, researchers, and environmentalists with a tool that can help predict and mitigate adverse effects of these factors.

Step 1: Define the application's structure and user interface. The UI should allow users to select different scenarios (e.g., increased industrial activity, reduction in emissions), input data for these scenarios, and choose a geographical area of interest.

Step 2: Utilize the 'aimz' package to model the probabilistic impacts based on the user inputs. Ensure that the application can handle large datasets efficiently due to the scalable nature of 'aimz'.

Step 3: Implement a feature to visualize the results using matplotlib or another plotting library. Users should be able to see graphs showing predicted impacts over time for each scenario.

Step 4: Include an export functionality that allows users to save their models and results in a format like CSV or JSON for further analysis.

Suggested Features:
- Scenario comparison: Allow users to compare different scenarios side by side.
- Historical data integration: Provide an option to integrate historical data for more accurate predictions.
- Customizable impact metrics: Enable users to define custom impact metrics relevant to their study.
- Real-time updates: If possible, implement a feature where the application fetches real-time data from reliable sources to update the impact models dynamically.

How 'aimz' is utilized: The 'aimz' package will be the backbone of the probabilistic modeling process. It will be used to create, train, and evaluate models based on the user-provided data. The scalability aspect of 'aimz' ensures that the application remains efficient even when dealing with extensive datasets or complex models.

💬 Discussion Feed

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