AI Analysis
The package exhibits a moderate level of network risk due to its unexpected external API calls, which could potentially be used for unauthorized data collection. Additionally, the metadata risk is elevated due to the sparsity and potential inactivity of the author's account.
- Moderate network risk due to unexpected API calls
- Elevated metadata risk due to sparse author details
Per-check LLM notes
- Network: The package makes unexpected network calls to external APIs which may indicate unauthorized data collection or exfiltration.
- Shell: No shell execution patterns were detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: The author's details are sparse and the account seems new or inactive, raising some suspicion.
Heuristic Checks
Found 3 network call pattern(s)
esponse = uf.process_response(requests.get( f"https://account.pretendo.cc/v1/api/miis?pesponse = uf.process_response(requests.get( f"https://account.spfn.net/v1/api/miis?pidsmii_data_response = requests.get( f"https://account.spfn.net/api/v2/users/{pl
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: protonmail.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Splatoon Match Logger Application using the Splatlogger package. This application will serve as a tool for Splatoon players to track their gaming statistics and network activity. Hereβs a detailed plan on how to build it: 1. **Project Overview**: Your task is to develop a Python-based application that leverages the Splatlogger package to capture and log data from Splatoon matches. The app should be user-friendly and capable of running on both Windows and Linux systems. 2. **Core Features**: - **Match Logging**: Automatically capture match results, including win/loss status, stage, mode, and time played. - **Player ID Tracking**: Retrieve and log the Principal ID (PID) and Network ID of the player and opponents. - **Data Storage**: Save all logged information into a local SQLite database for easy retrieval and analysis. - **User Interface**: Implement a simple command-line interface for users to interact with the application. - **Customization Options**: Allow users to configure settings such as logging frequency and data retention policies. 3. **Utilizing Splatlogger**: - Use `splatlogger.get_match_info()` to fetch match details after each game session. - Employ `splatlogger.get_player_id()` to gather the PID and Network ID. - Ensure proper error handling and logging mechanisms are in place for robustness. 4. **Development Steps**: - **Setup**: Install necessary Python packages including Splatlogger, SQLite, and any other dependencies. - **Database Design**: Define the schema for the SQLite database to store match logs and player IDs. - **Logging Mechanism**: Develop the logic to automatically log match data post-game using Splatlogger functions. - **CLI Implementation**: Create a CLI where users can start, stop, and configure the logging process. - **Testing**: Thoroughly test the application under various scenarios to ensure reliability. - **Documentation**: Write clear documentation explaining how to install, use, and customize the application. 5. **Optional Enhancements**: - Add support for exporting logs to CSV or JSON files. - Implement a feature to visualize match data over time using matplotlib or similar libraries. - Integrate with Discord to send notifications about match outcomes. This project aims to provide Splatoon enthusiasts with a powerful yet straightforward tool to enhance their gaming experience through data analytics.
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