AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network, shell, obfuscation, and credential handling. However, the metadata risk score is elevated due to the new maintainer account and missing PyPI classifiers, which warrants closer scrutiny.
- New maintainer account
- Lack of PyPI classifiers
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: Low risk but requires further investigation due to the new maintainer account and lack of PyPI classifiers.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
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 yaogdu/AgentLedger appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "AgentLedger Contributors" 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 agentledger-postgres
Your task is to develop a mini-application called 'AgentLog' that leverages the 'agentledger-postgres' package to manage logs of user activities in a PostgreSQL database. This application will serve as a simple yet effective logging system for tracking actions performed by users within a web application or service. Here are the key steps and features you'll need to implement: 1. **Setup**: Begin by installing the necessary packages including 'agentledger-postgres'. Ensure you have PostgreSQL set up on your local machine or a remote server. 2. **Database Integration**: Use 'agentledger-postgres' to integrate your application with the PostgreSQL database. This involves setting up the connection parameters and configuring the state store. 3. **Logging Mechanism**: Design a logging mechanism where every action performed by a user (e.g., login, logout, file creation, deletion) gets logged into the database. Each log entry should include details such as the user ID, timestamp, and description of the action. 4. **User Interface**: Create a basic UI (using Flask or Django) where administrators can view these logs. Implement features to filter logs based on date range, user ID, and type of action. 5. **Security Measures**: Since this application deals with sensitive data, ensure that proper security measures are in place. For instance, use environment variables to manage database credentials and secure connections. 6. **Error Handling**: Implement robust error handling to manage exceptions related to database operations and ensure the application remains stable even under unexpected conditions. 7. **Testing**: Write tests using unittest or pytest to validate the functionality of your application, especially focusing on database interactions. The goal is to create a functional, secure, and efficient logging system that showcases the capabilities of 'agentledger-postgres' in managing state and storing logs in a PostgreSQL database.