alephantai-analytics-api

v0.1.1 suspicious
4.0
Medium Risk

(No description)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of network activity which requires further investigation to ensure it is not engaging in unauthorized data transmission or other malicious activities.

  • Moderate network risk due to HTTP request patterns
  • Lack of package description raises concern about transparency
Per-check LLM notes
  • Network: The observed network patterns are typical for making HTTP requests, which could be part of the package's intended functionality, but should be reviewed for destination and frequency.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Low (3.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
○ 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
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 410 type-annotated function signatures detected in source
○ 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 score 6.0

Found 4 network call pattern(s)

  • is not None else httpx.Client(timeout=_defaulted_timeout, follow_redirects=follow_redirect
  • is not None else httpx.Client(timeout=_defaulted_timeout), timeout=_defaulted_
  • is not None else httpx.AsyncClient(timeout=_defaulted_timeout, follow_redirects=follow_redirect
  • is not None else httpx.AsyncClient(timeout=_defaulted_timeout), timeout=_defaulted_
✓ 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 alephantai-analytics-api 
Create a web-based dashboard using Flask that integrates with the 'alephantai-analytics-api' package to provide real-time analytics for a fictional e-commerce store. This application should allow users to visualize sales trends, customer traffic, and product performance over time. Here’s a detailed plan on how to approach this project:

1. **Setup Environment**: Install Python and necessary packages such as Flask, alephantai-analytics-api, and any other required libraries for data visualization like Plotly or Matplotlib.
2. **Database Setup**: Although 'alephantai-analytics-api' doesn't have a description, assume it provides access to an analytics database or API. Set up a mock dataset or use the actual API provided by 'alephantai-analytics-api' to simulate e-commerce data.
3. **API Integration**: Integrate the 'alephantai-analytics-api' into your Flask application. Use its endpoints to fetch data about sales, customer interactions, and product views.
4. **Dashboard Design**: Design a user-friendly dashboard using HTML/CSS/JavaScript. Include sections for daily sales summary, top-selling products, and customer engagement metrics.
5. **Data Visualization**: Implement visualizations for the fetched data. For example, use line graphs for sales trends, pie charts for market share of different products, and bar charts for comparing sales across different categories.
6. **Real-Time Updates**: Ensure that the dashboard updates in real-time or at regular intervals to reflect the latest data from 'alephantai-analytics-api'.
7. **User Authentication**: Implement basic authentication to secure the dashboard. Only authorized users should be able to view the analytics.
8. **Testing**: Thoroughly test the application to ensure all features work correctly and efficiently handle large datasets.
9. **Deployment**: Deploy the application on a platform like Heroku or AWS to make it accessible online.

This project aims to showcase the capabilities of 'alephantai-analytics-api' in providing valuable insights through real-time analytics and to demonstrate proficiency in building web applications with Flask.

💬 Discussion Feed

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