🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious intent or functionality but has a high metadata risk due to recent and rapid commit history, which may indicate unusual developer behavior.

Metadata risk is elevated due to recent repository creation and rapid commits.
No other significant risks detected.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package does not execute external commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository was created very recently and all commits occurred within a short period, indicating potential suspicious activity.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

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

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (7494 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Separate author ("ANTMA contributors") and maintainer ("THEINNOLAB") listed

◈ Medium Type Annotations 7.0

Partial type annotation coverage

Type checker (mypy / pyright / pytype) referenced in project
225 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 15 commits in THEINNOLAB/ANTMA
Single author with few commits — possibly a personal or throwaway project

🔬 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 score 5.0

Git history flags: Repository created very recently: 4 day(s) ago (2026-06-03T08:23:15Z)

Repository created very recently: 4 day(s) ago (2026-06-03T08:23:15Z)
All 15 commits happened within 24 hours

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "ANTMA contributors" 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 antma

Create a personal knowledge management system (PKMS) named 'AI-Brain' using the Python package 'antma'. This PKMS will allow users to store, organize, and retrieve information in a filesystem-first manner, making it easier for teams to collaborate on shared knowledge. Here are the steps and features you need to implement:

1. **Setup Environment**: Install the required packages including 'antma'. Ensure your Python environment is set up correctly.
2. **User Authentication**: Implement a simple user authentication mechanism allowing users to sign up and log in. Store credentials securely.
3. **Filesystem Structure**: Use 'antma' to create a structured filesystem where each user has their own directory. This directory will contain subdirectories for different categories of information such as 'Projects', 'Notes', 'Resources', etc.
4. **Content Management**: Allow users to upload documents, notes, and other resources into their respective directories. Each file should have metadata attached, such as tags, creation date, and description.
5. **Search Functionality**: Implement a search feature that allows users to find files based on keywords, tags, and dates. Utilize 'antma's capabilities to efficiently index and retrieve files from the filesystem.
6. **Collaboration Features**: Enable users to share directories or specific files with others, granting read/write permissions as needed. This feature should support real-time updates and notifications.
7. **Analytics Dashboard**: Provide a dashboard that displays usage statistics such as most accessed files, popular tags, and recent activity. Use 'antma' to track these metrics without compromising user privacy.
8. **Backup & Restore**: Incorporate a backup and restore functionality that periodically backs up user data and allows for restoration in case of loss.
9. **Integration with AI Services**: Explore integrating 'AI-Brain' with AI services like OpenAI's API to provide intelligent insights and recommendations based on stored data.

By following these steps and implementing these features, you will create a robust PKMS that leverages 'antma' to enhance user experience and efficiency in managing digital assets.