antma

v0.2.0 suspicious
5.0
Medium Risk

Filesystem-first memory promotion engine for AI-native team memory.

🤖 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.

💬 Discussion Feed

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