🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to subprocess execution and incomplete metadata, raising concerns about its origin and potential for unexpected behavior.

Subprocess execution introduces potential for command injection.
Lack of author information and associated GitHub repository.

Per-check LLM notes

Network: No network calls detected, which is neutral.
Shell: Subprocess execution may indicate potential for command injection or unexpected behavior, requiring further investigation.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating low risk of unauthorized access.
Metadata: The package has no associated GitHub repository and the author information is lacking, which raises some concerns but not enough to conclusively determine it as malicious.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

Test runner config found: pyproject.toml
12 test file(s) detected (e.g. test_cli.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (22392 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

300 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

_path = tf.name result = subprocess.run([cfg.editor, tmp_path]) if result.returncode != 0:
try: result = subprocess.run( ["claude", "mcp", "list"],
= open(log_p, "a") proc = subprocess.Popen( cmd, start_new_session=True, stdin=

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ 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-houkai

Create a personalized learning assistant app that leverages the 'ai-houkai' package for managing user study sessions. This app will help users enhance their learning efficiency by providing tailored content based on their past interactions and performance. Here’s a detailed plan for building this app:

1. **User Authentication**: Implement a simple registration and login system where users can create accounts and log in.
2. **Study Session Management**: Users can start new study sessions where they input the topic they are studying and the materials they are using. The app will store this information.
3. **Long-Term Memory System**: Utilize the 'ai-houkai' package to implement a long-term memory system. This system will periodically review the stored study session data, applying decay to less frequently accessed topics and reflecting on more relevant ones. This ensures that users are reminded of important but older topics while also focusing on recent studies.
4. **Performance Tracking**: Track each user's performance during study sessions. This includes time spent on topics, difficulty levels encountered, and any notes or feedback provided by the user.
5. **Personalized Recommendations**: Based on the user's performance and the long-term memory system's reflections, generate personalized recommendations for future study sessions. These recommendations could include revisiting certain topics, suggesting additional resources, or highlighting areas that need more focus.
6. **Dashboard Interface**: Develop a dashboard where users can view their progress, see upcoming recommended study topics, and manage their study sessions.
7. **Integration with External Learning Resources**: Allow users to import or link external learning materials such as PDFs, YouTube videos, or web pages. The app will then use these materials in conjunction with the 'ai-houkai' long-term memory system to provide a more comprehensive learning experience.
8. **Feedback Loop**: Incorporate a feedback loop where users can rate the effectiveness of the study sessions and the relevance of the recommended topics. Use this feedback to improve the recommendation algorithm over time.

By following these steps, you will create a powerful learning tool that not only helps users manage their study sessions but also enhances their learning efficiency through personalized, context-aware recommendations.