AI Analysis
Final verdict: SUSPICIOUS
The package has a moderate risk score due to its metadata characteristics, including a lack of maintainer history and a single version release, which raises concerns about its legitimacy.
- Lack of maintainer history
- Single version release
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
- Shell: No shell execution detected, indicating the package does not execute external commands, reducing potential risks.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows several red flags including a lack of maintainer history, single version release, and an author with minimal information, indicating potential risk.
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
Email domain looks legitimate: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor 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 agent-recorder
Create a mini-application named 'CodeLogger' that integrates the 'agent-recorder' package to log all interactions with AI coding assistants like Claude Code and Codex into a local SQLite database. This application should provide users with a seamless way to track their coding sessions over time, allowing them to revisit previous queries and responses for reference or learning purposes. Step-by-Step Guide: 1. Set up the initial project structure including necessary files and folders. 2. Install and import the 'agent-recorder' package. 3. Design a user-friendly CLI interface for initiating and ending recording sessions. 4. Implement functionality to save each query and response from the AI assistant into the SQLite database. 5. Add a feature to search through the recorded sessions using keywords or dates. 6. Enhance the application by adding options to export sessions to different formats (e.g., CSV, JSON). 7. Ensure the application logs errors gracefully and provides informative messages to the user. 8. Test the application thoroughly to ensure it works as expected under various scenarios. Suggested Features: - Interactive help menu accessible via CLI commands. - Ability to tag sessions for easier categorization. - Support for multiple databases to organize data by projects or topics. - Automatic session backups to prevent data loss. - Option to disable logging temporarily based on user preference.