AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network, shell, obfuscation, and credential misuse. However, the metadata risk is moderately high due to the maintainer's new or inactive account and lack of detailed author information, raising concerns about potential malicious intent.
- Moderate metadata risk due to maintainer's new or inactive account
- Lack of detailed author information increases suspicion
Per-check LLM notes
- Network: Network calls are expected for packages interacting with external APIs like lichess.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has a new or inactive account and lacks a proper author name, which may indicate a less established or potentially suspicious presence.
Package Quality Overall: Low (4.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
1 test file(s) detected (e.g. test_client.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (4846 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
9 type-annotated function signatures (partial)
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 7 commits in aryanhasgithub/aiolichessTwo distinct contributors found
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
: self._session = aiohttp.ClientSession() return self._session async def _request(self,
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
Repository aryanhasgithub/aiolichess appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 aiolichess
Create a Python-based asynchronous application named 'LichessGameAnalyzer' using the 'aiolichess' library. This application will allow users to fetch and analyze their recent chess games played on Lichess. Here's a step-by-step guide to building this app: 1. **Setup**: Install the required packages including 'aiolichess'. Ensure your environment is set up for asyncio. 2. **Authentication**: Implement OAuth2 authentication flow to authorize the app to access user data from Lichess. 3. **Data Fetching**: Use 'aiolichess' to asynchronously fetch the user's recent games. Display basic game information such as date, opponent, result, and time control. 4. **Game Analysis**: For each fetched game, asynchronously request detailed analysis data from Lichess. Extract key statistics like opening, average move time, and accuracy. 5. **Visualization**: Create a simple CLI interface where users can interactively view their game summaries and detailed analyses. Optionally, implement a basic web interface using Flask. 6. **Export Functionality**: Allow users to export their game data into a CSV file for further analysis outside of the app. 7. **Error Handling**: Implement robust error handling to manage issues such as network errors, rate limits, and invalid responses from the Lichess API. 8. **Testing**: Write unit tests for critical functionalities to ensure reliability and maintainability of the application. Suggested Features: - A command to refresh OAuth tokens when they expire. - An option to filter games by specific criteria (e.g., by date range, opening type). - Integration with a chess engine to perform deeper analysis on specific moves. - Visual representation of game progress through a graph showing win/loss streaks or performance trends over time. This project will showcase your ability to work with asynchronous APIs, handle user input and output, and integrate external services into a cohesive application.
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