athenaeum

v0.6.1 suspicious
4.0
Medium Risk

Open source knowledge management pipeline — append-only intake, tiered compilation, configurable schemas

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some unusual behavior, particularly in setting up a local Git repository, and the maintainer's metadata lacks critical information such as an author name. These factors raise suspicion but do not conclusively indicate malicious intent.

  • Shell risk due to git commands
  • Incomplete maintainer metadata
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Git commands indicate the package may be setting up a local Git repository, which is unusual but not necessarily malicious.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author name is missing and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 34 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 34 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (16200 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

  • 836 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in Kromatic-Innovation/athenaeum
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • not git_dir.exists(): subprocess.run(["git", "init"], cwd=path, check=True, capture_output=True)
  • capture_output=True) subprocess.run(["git", "add", "."], cwd=path, check=True, capture_output=Tr
  • rue) try: subprocess.run( ["git", "commit", "-m", "Initialize knowled
  • return False result = subprocess.run( ["git", "status", "--porcelain"], cwd=str(k
  • (): return False subprocess.run( ["git", "add", "-A"], cwd=str(knowledge_roo
  • check=True, ) subprocess.run( ["git", "commit", "-m", message], cwd=str(k
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: kromatic.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Kromatic-Innovation/athenaeum 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 athenaeum 
Create a knowledge management system using the Python package 'athenaeum'. This system will serve as a personal digital notebook where users can store, organize, and retrieve information efficiently. The application should allow users to input various types of data such as notes, research papers, articles, and images. It should also provide functionalities for categorizing and searching through the stored information.

Step 1: Setup the Project Environment
- Initialize a new Python project and install the 'athenaeum' package.
- Set up a basic project structure including directories for configuration files, schema definitions, and compiled outputs.

Step 2: Define Data Schemas
- Use 'athenaeum' to define schemas for different types of data inputs. For example, create schemas for notes, which might include fields like title, content, tags, and timestamp; and for research papers, which could have additional fields like author, publication date, and DOI.
- Ensure that each schema is append-only, meaning once data is added, it cannot be deleted or altered.

Step 3: Implement Input Mechanisms
- Develop interfaces for users to input data according to the defined schemas. This could be done through a command-line interface (CLI) or a simple web-based form.
- Ensure that the data input process respects the integrity of the schemas and allows for easy categorization based on predefined tags or categories.

Step 4: Organize and Retrieve Data
- Utilize 'athenaeum's tiered compilation feature to organize the stored data into different tiers based on user-defined criteria (e.g., relevance, recency, category).
- Implement a search functionality that allows users to query the database using keywords, tags, or other metadata.

Step 5: Enhance User Experience
- Add features to enhance usability, such as a summary view of recent entries, a tag cloud showing frequently used tags, and a calendar view for notes tagged with dates.
- Consider integrating features like versioning to track changes over time and notifications for important updates or reminders.

Step 6: Test and Deploy
- Thoroughly test the application to ensure all functionalities work as expected.
- Once satisfied with the performance, deploy the application either locally or via a cloud service.

By following these steps and utilizing the core features of 'athenaeum', you'll create a robust and user-friendly knowledge management tool that helps users keep their information organized and easily accessible.

💬 Discussion Feed

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