agentversion

v0.1.0 suspicious
6.0
Medium Risk

An open specification for versioning agent runtimes and keeping datasets valid.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity but has a high metadata risk due to the unavailability of the repository and the newness of the maintainer. This raises concerns about potential supply-chain attacks.

  • High metadata risk due to missing repository
  • New maintainer with limited history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires them for functionality.
  • Shell: No shell execution detected, which is normal unless the package requires it for its intended use.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
  • Metadata: The repository is not found, and the maintainer seems to be new with limited history, raising some suspicion.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • Test runner config found: pyproject.toml
  • 16 test file(s) detected (e.g. test_audit_v020.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/decimal-labs/agentversion/tree/main/spec
  • Detailed PyPI description (12471 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

  • 66 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 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 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Decimal AI" 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 agentversion 
Develop a mini-application called 'VersionGuard' that ensures the integrity and compatibility of datasets across different versions of agent runtimes using the 'agentversion' package. This application will serve as a tool for developers and data scientists who work with evolving datasets and need to maintain compatibility with their existing tools and workflows.

**Core Features:**
1. **Dataset Version Tracking:** Implement functionality that allows users to specify a dataset and track its version history, ensuring that the dataset remains compatible with the current runtime environment.
2. **Runtime Compatibility Check:** Utilize the 'agentversion' package to check if a given dataset is compatible with the current runtime version. If not, provide suggestions on how to update the dataset or the runtime to ensure compatibility.
3. **Version Migration Tool:** Create a feature that automatically migrates datasets from one version to another, ensuring that the dataset remains valid and usable within the new runtime environment.
4. **Compatibility Report Generation:** Develop a feature that generates a detailed report outlining the compatibility status of each dataset with the current runtime version, including any necessary actions for maintaining compatibility.

**How to Use 'agentversion':** 
The 'agentversion' package will be used to define and manage versioning schemas for both datasets and runtimes. It will help in determining whether a dataset is valid for a specific runtime version and guide the migration process between different versions. Additionally, it will be crucial in generating compatibility reports based on the defined versioning schemas.