arbiter-score

v0.6.0 suspicious
7.0
High Risk

Deterministic, polyglot code quality scoring with governance integration

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks across multiple categories, particularly concerning its metadata and credential handling. While some activities like shell executions and network calls can be legitimate, the lack of detailed documentation and community engagement raises concerns about its authenticity and safety.

  • High metadata risk due to new account and minimal activity
  • GitHub token access increases credential exposure risk
Per-check LLM notes
  • Network: The network calls may be legitimate if the package is designed to interact with external services, but further investigation into the purpose of these requests is needed.
  • Shell: Shell executions related to git and ruff seem to be part of code quality assurance or version control operations, but their necessity and context within the package should be verified.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The code is accessing an environment variable for a GitHub token which could indicate legitimate use but also poses a risk if not handled securely.
  • Metadata: The package is suspicious due to lack of maintainer history, minimal repository engagement, and a new account with limited packages.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/hummbl-dev/arbiter#readme
  • Detailed PyPI description (4743 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

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

Active multi-contributor project

  • 5 unique contributor(s) across 94 commits in hummbl-dev/arbiter
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • (body, secret) req = urllib.request.Request(url, data=body, headers=headers, method="POST")
  • , method="POST") with urllib.request.urlopen(req, timeout=10) as resp: return resp.st
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • rom git diff result = subprocess.run( ["git", "-C", str(repo_path), "diff", "--name-o
  • y staged result = subprocess.run( ["git", "-C", str(repo_path), "diff", "--st
  • out applying.""" result = subprocess.run( ["ruff", "check", "--fix", "--diff", str(repo_path)
  • es to a repo.""" result = subprocess.run( ["ruff", "check", "--fix", "--unsafe-fixes", str(re
  • , str(dest)] result = subprocess.run(cmd, capture_output=True, text=True, timeout=300) if
  • _url, str(dest)] result = subprocess.run(cmd, capture_output=True, text=True, timeout=300) if res
⚠ Credential Harvesting score 10.0

Found 4 credential access pattern(s)

  • warnings = [] token = os.environ.get("GITHUB_TOKEN", "") # Query open PRs url = f"https://api.github.c
  • """ token = token or os.environ.get("GITHUB_TOKEN") repos = search_top_repos( language=language,
  • umber}" token = token or os.environ.get("GITHUB_TOKEN") headers = { "Accept": "application/vnd.github
  • return [] token = os.environ.get("GITHUB_TOKEN", "") url = ( f"https://api.github.com/search/i
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: hummbl.io>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 arbiter-score 
Develop a comprehensive code quality analysis tool named 'CodeGuard' using the Python package 'arbiter-score'. This tool will serve as a robust solution for developers to assess the quality of their code across multiple programming languages, ensuring it adheres to best practices and governance standards. Here's a detailed breakdown of what 'CodeGuard' should accomplish:

1. **Project Setup**: Begin by setting up your development environment with Python and installing 'arbiter-score' along with any other necessary dependencies.
2. **Code Scoring Functionality**: Implement a core feature where users can input the path to a file or directory containing source code. 'CodeGuard' will then analyze the code using 'arbiter-score', providing a deterministic score based on predefined quality metrics and governance rules.
3. **Multi-Language Support**: Ensure that 'CodeGuard' supports at least three different programming languages (e.g., Python, JavaScript, Java). Each language should have its own set of specific quality criteria, reflecting best practices within that language community.
4. **Detailed Report Generation**: After analyzing the code, 'CodeGuard' should generate a detailed report highlighting areas of strength and weakness in the codebase. This report should include suggestions for improvement and explanations of how each metric contributes to the overall score.
5. **Customizable Governance Rules**: Allow users to customize governance rules according to their organization's policies. Users should be able to define which metrics are prioritized and how they influence the final score.
6. **Integration Capabilities**: Explore integrating 'CodeGuard' with popular version control systems like Git to automatically run code quality checks whenever changes are committed or merged.
7. **User Interface**: Develop a simple yet effective command-line interface (CLI) for 'CodeGuard'. Consider adding basic UI elements for easier interaction if time permits.
8. **Testing and Documentation**: Rigorously test 'CodeGuard' to ensure accuracy and reliability. Provide comprehensive documentation detailing setup, usage, and customization options.

Throughout the development process, focus on leveraging 'arbiter-score' to handle the complex task of scoring code quality deterministically and integrating governance rules seamlessly. Your goal is to create a tool that not only identifies issues but also educates users on how to improve their coding practices.

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