🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with low risks across multiple categories. It shows normal API interactions and lacks any suspicious patterns.

Low network, shell, obfuscation, and credential risks.
Sparse metadata suggests a possibly new or less transparent author, but does not indicate malicious intent.

Per-check LLM notes

Network: The detected network calls appear to be part of normal API interactions, likely for statistical data reporting or SDK functionality.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's information is sparse, indicating potential lack of transparency or newness, but no other red flags are present.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://docs.ai-stats.phaseo.app
Detailed PyPI description (4369 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
183 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

4 unique contributor(s) across 100 commits in AI-Stats/AI-Stats
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

= "application/json" req = urllib.request.Request(url, data=payload, headers=request_headers, method=m
method=method.upper()) with urllib.request.urlopen(req) as resp: raw = resp.read().decode("utf-8")
}/content" response = httpx.get(url, headers=self._headers, timeout=self._timeout) r

✓ 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

Repository AI-Stats/AI-Stats 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 ai-stats-py-sdk

Create a real-time data analysis dashboard using the 'ai-stats-py-sdk' package. This mini-application will allow users to input various types of data streams and visualize them in real-time, providing instant insights and analytics. The app should have the following features:

1. **Data Input Interface**: Users should be able to upload CSV files or input raw data through a simple form interface.
2. **Real-Time Data Processing**: Use the 'ai-stats-py-sdk' to process incoming data in real-time, applying statistical models to predict trends and anomalies.
3. **Visualization Tools**: Implement interactive charts and graphs using libraries like Plotly or Matplotlib to display the analyzed data.
4. **User Authentication**: Allow users to create accounts and save their data sets and analysis configurations.
5. **Notification System**: Set up alerts for significant changes or anomalies detected in the data stream.
6. **Documentation and User Guide**: Provide comprehensive documentation and a user guide on how to use the dashboard effectively.

**How to Utilize 'ai-stats-py-sdk':**
- Import and initialize the SDK at the start of your application.
- Use the SDK's methods to ingest data from the user input.
- Apply statistical models provided by the SDK to analyze the data.
- Integrate the SDK's real-time processing capabilities to update visualizations dynamically as new data comes in.
- Leverage the SDK's anomaly detection features to trigger notifications.

This project aims to showcase the power of real-time data analysis and visualization, making complex data understandable and actionable.