ai-prophet-core

v0.1.5 suspicious
4.0
Medium Risk

AI Prophet, utils, types and HTTP client

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some concerning patterns that suggest potential misuse or obfuscation practices, although the exact nature and intent remain unclear. Further investigation is recommended.

  • incomplete network call implementations
  • possible attempts to obfuscate sensitive information
Per-check LLM notes
  • Network: The package makes network calls which are likely intended for API interactions, but the code snippets provided are incomplete and may indicate improper handling or potential misuse.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The observed pattern may indicate an attempt to hide the private key, but it could also be a standard practice for handling sensitive cryptographic keys.
  • Credentials: No clear signs of credential harvesting were detected.
  • Metadata: The package shows some low-effort signs but lacks clear red flags.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • Test runner config found: pyproject.toml
  • 15 test file(s) detected (e.g. test_arena.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://www.prophetarena.co
  • Detailed PyPI description (3772 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 5.0

Partial type annotation coverage

  • 147 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 7.5

Found 5 network call pattern(s)

  • None self._session = requests.Session() if not self._api_key_id: logger.warni
  • elative_path}" response = requests.get( url, params={"ref": branch}, header
  • None self._session = requests.Session() # ---------------------------------------------------
  • = self.api_key return httpx.Client( base_url=self.base_url, timeout=sel
  • else None resp = httpx.get(url, timeout=15, headers=headers) data = resp.co
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • lization key_bytes = base64.b64decode(self._private_key_base64) self._private_key = serial
✓ 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 "AI Prophet Team" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-prophet-core 
Create a mini-application called 'ProphetPredictor' using the Python package 'ai-prophet-core'. This application will serve as a simple time series forecasting tool, utilizing the utilities, types, and HTTP client functionalities provided by 'ai-prophet-core'. The goal of 'ProphetPredictor' is to enable users to input historical data (such as stock prices, weather patterns, or website traffic), and receive forecasts for future trends based on the data provided.

### Key Features:
1. **Data Input Interface**: Users should be able to upload CSV files containing historical data. The application should validate the format of the CSV file to ensure it contains the necessary columns for time series analysis (e.g., date and value).
2. **Forecast Generation**: Utilize the 'ai-prophet-core' package to process the uploaded data and generate forecasts. The application should allow users to specify parameters such as the forecast period, seasonality mode, and changepoint prior.
3. **Visualization**: Display the forecast results visually using charts. Users should be able to view the historical data alongside the predicted future trends.
4. **Export Results**: Provide an option for users to export the forecasted data back into a CSV file.
5. **Documentation**: Include comprehensive documentation on how to use the application, including setup instructions and example datasets.

### Steps to Implement:
1. **Setup Environment**: Install the required packages, including 'ai-prophet-core', pandas, matplotlib for data manipulation and visualization, and Flask for the web interface.
2. **Web Interface Development**: Use Flask to create a simple web application where users can upload their CSV files and configure forecast parameters.
3. **Data Processing**: Validate the uploaded CSV files and prepare them for forecasting using the functionalities from 'ai-prophet-core'.
4. **Forecasting Logic**: Implement the logic to generate forecasts using 'ai-prophet-core', allowing for customization of forecast settings.
5. **Visualization**: Integrate matplotlib to display the forecast results alongside the historical data.
6. **Export Functionality**: Develop a feature to export the forecast data into a new CSV file that users can download.
7. **Testing and Documentation**: Test the application thoroughly and document each step of the process, including how to run the application and interpret the results.

By following these steps, you will create a useful tool for anyone interested in predicting future trends based on past data.