ai-jury

v1.2.0 safe
4.0
Medium Risk

Cross-vendor multi-agent PR review jury — orchestrate native coding-agent CLIs (Claude Code, Codex, Antigravity) to mutually review and debate the same pull request.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of malicious intent with no obfuscation or credential risks. While there are potential risks related to network and shell usage, these seem to be functional requirements rather than indicators of malice.

  • Low obfuscation and credential risks
  • Potential benign use of network and shell functionalities
Per-check LLM notes
  • Network: The network calls could be legitimate if the package is intended to interact with external services or APIs.
  • Shell: The shell execution might indicate the package uses external tools or commands, but without context, it's hard to determine if it's benign or malicious.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, indicating a potentially new or less active account.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 30 test file(s) found

  • 30 test file(s) detected (e.g. test_adapters.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (31137 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

  • 207 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in berkayturanci/ai-jury
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • models" try: with urllib.request.urlopen(url, timeout=_VERSION_PROBE_TIMEOUT) as resp: # noq
  • try: with urllib.request.urlopen(url, timeout=_VERSION_PROBE_TIMEOUT) as resp: # noq
  • encode("utf-8") req = urllib.request.Request( self.completions_url(), dat
  • try: with urllib.request.urlopen(req, timeout=effective_timeout) as resp: # noqa: S3
  • }]}' with mock.patch("urllib.request.urlopen", return_value=_Resp(body)): r = self._a
  • one) with mock.patch("urllib.request.urlopen", side_effect=err): r = self._adapter().
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • try: proc = subprocess.run( self._version_argv(), captu
  • try: proc = subprocess.run( argv, input=stdin,
  • ATH") try: proc = subprocess.run( ["gh", *args], capture_output=True, text=True,
  • ATH") try: proc = subprocess.run( ["gh", *args], input=stdin_data, capture_output
✓ 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

Repository berkayturanci/ai-jury appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Berkay Turancı" 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 ai-jury 
Create a fully-functional mini-application called 'CodeReviewJury' using the Python package 'ai-jury'. This application will automate the process of reviewing Pull Requests (PRs) in software development projects by leveraging multiple AI agents to provide diverse opinions and critiques. The goal is to simulate a jury system where different AI agents representing various coding philosophies and expertise levels will review the same PR and provide feedback, ensuring a comprehensive and balanced assessment.

### Steps to Implement:
1. **Setup Environment**: Install Python and the 'ai-jury' package along with any other necessary dependencies.
2. **Define CLI Agents**: Use 'ai-jury' to define and configure the CLI agents (e.g., Claude Code, Codex, Antigravity) that will participate in the review process. Each agent should have its unique characteristics and capabilities.
3. **Integrate PR Parsing**: Develop functionality within 'CodeReviewJury' to parse PR details from a chosen source (GitHub, GitLab, etc.).
4. **Orchestration**: Utilize 'ai-jury' to orchestrate the agents to review the parsed PR content. Ensure that each agent provides independent feedback on the code changes.
5. **Feedback Aggregation**: Implement a mechanism to aggregate and present the feedback from all participating agents. This could include summarizing common points, highlighting disagreements, and suggesting improvements.
6. **User Interface**: Design a simple user interface (CLI or web-based) that allows users to input PR URLs and view the aggregated feedback.
7. **Testing and Validation**: Test 'CodeReviewJury' with real-world PR examples to ensure it functions as expected and provides valuable insights.
8. **Documentation**: Write documentation explaining how to use 'CodeReviewJury', including setup instructions, usage guidelines, and troubleshooting tips.

### Suggested Features:
- **Agent Customization**: Allow users to customize the behavior of each agent based on their specific needs.
- **Conflict Resolution**: Implement algorithms to resolve conflicts between differing agent opinions.
- **Integration with CI/CD**: Integrate 'CodeReviewJury' into Continuous Integration/Continuous Deployment (CI/CD) pipelines to automatically run PR reviews during the build process.
- **Reporting**: Generate detailed reports summarizing the review process and outcomes, which can be useful for developers and project managers.