ai-afterimage

v0.7.1 suspicious
3.0
Low Risk

Episodic memory for Claude Code - persistent memory of code written across sessions with churn tracking

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct risks but the incomplete metadata and possibly inactive author raise concerns about its provenance and long-term maintenance.

  • Incomplete author information
  • Possibly inactive project
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution detected, indicating no immediate risk of executing arbitrary commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is incomplete and they may be new or inactive, which raises some suspicion but not enough to conclusively indicate malice.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (23886 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 647 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 30 commits in DragonShadows1978/AI-AfterImage
  • Single author but highly active (30 commits)

🔬 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: afterimage.dev>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository DragonShadows1978/AI-AfterImage appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 ai-afterimage 
Create a Python-based mini-application named 'CodeChronicle' which leverages the 'ai-afterimage' package to maintain a persistent record of all code snippets written during any session, even after the session ends. This application will serve as a personal coding diary that not only stores your code but also tracks changes made over time, allowing you to see the evolution of your projects. Here are the key features and steps to implement this application:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing the 'ai-afterimage' package alongside other necessary libraries such as SQLite for database operations.

2. **User Interface**: Design a simple yet effective command-line interface (CLI) where users can input their code snippets, view past entries, and search through their coding history.

3. **Code Snippet Storage**: Implement functionality within 'CodeChronicle' to store each code snippet along with metadata such as timestamp, author name, and tags. Use 'ai-afterimage' to ensure that these records persist across different sessions.

4. **Churn Tracking**: Utilize 'ai-afterimage's capabilities to track modifications and deletions in code snippets, providing users with insights into how their code has evolved over time. This feature should allow users to compare different versions of the same code snippet.

5. **Search Functionality**: Develop a robust search mechanism that enables users to find specific code snippets based on keywords, tags, or timestamps. Ensure that this search function is efficient and user-friendly.

6. **Backup and Restore**: Include an option for users to manually trigger backups of their coding history and restore it if needed. This ensures data safety and allows for easy recovery in case of accidental loss.

7. **Integration with External Tools**: Consider integrating 'CodeChronicle' with popular version control systems like Git, allowing users to synchronize their coding history directly from repositories.

8. **Testing and Documentation**: Before finalizing the application, conduct thorough testing to ensure all features work as expected. Provide comprehensive documentation detailing how to use each feature effectively.

By following these steps, 'CodeChronicle' will become a valuable tool for developers looking to keep track of their coding journey, learn from past mistakes, and continuously improve their skills.