ailake

v0.0.10 suspicious
5.0
Medium Risk

Unified tabular + vector storage in a single Iceberg-compatible file

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low technical risks but raises concerns due to missing author information and a non-existent git repository, suggesting potential unreliability or lack of transparency.

  • missing author name
  • non-existent git repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • 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.
  • Metadata: The package has red flags including a missing author name and a non-existent git repository, suggesting potential unreliability.

📦 Package Quality Overall: Low (2.0/10)

○ 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 (2800 chars)
○ 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

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 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 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 ailake 
Create a mini-application named 'AquaInsight' that leverages the 'ailake' Python package to provide advanced data analysis and visualization capabilities for water quality monitoring data. The application should allow users to upload their own datasets in CSV format, store them using the unified tabular and vector storage capabilities provided by 'ailake', and perform various analyses on the data. Here are the key functionalities that AquaInsight should support:

1. **Data Import**: Users should be able to import their water quality datasets from CSV files. The application will use the 'ailake' package to store this data in a way that combines both tabular and vector formats within a single Iceberg-compatible file.

2. **Data Visualization**: Implement interactive visualizations for key water quality parameters such as pH levels, dissolved oxygen, and turbidity. These visualizations should help users understand trends over time and compare different locations or sampling periods.

3. **Statistical Analysis**: Provide basic statistical summaries for each parameter (mean, median, standard deviation). Use 'ailake' to efficiently query and retrieve necessary data for these calculations.

4. **Anomaly Detection**: Incorporate an anomaly detection feature that flags unusual readings based on predefined thresholds or statistical methods. This will help in identifying potential issues in real-time.

5. **User Interface**: Develop a user-friendly web interface using a framework like Streamlit or Flask. Ensure that the UI allows easy navigation between data import, visualization, and analysis sections.

6. **Export Results**: Allow users to export their analyzed data and visualizations in formats like PDF or Excel for further reporting or record-keeping purposes.

The 'ailake' package plays a crucial role in this project by enabling efficient data storage and retrieval operations. By leveraging its unified storage approach, the application can handle large datasets more effectively and offer faster response times during analysis and visualization tasks.

💬 Discussion Feed

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