agent-qa-console

v0.8.2 suspicious
7.0
High Risk

Visual browser QA console for AI coding agents — live browser, element selection, source mapping, RTL/a11y/perf audits, and test-fix-verify loops.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple risk factors including potential unauthorized network activity and the capability to execute shell commands, which are both high-risk behaviors. Additionally, the lack of a repository and the new/inactive maintainer account increase suspicion.

  • High shell risk due to potential for arbitrary command execution
  • Medium network risk due to possible unauthorized data transmission
  • Metadata risk due to missing repository and inactive maintainer
Per-check LLM notes
  • Network: The package makes network calls which could be for legitimate purposes like API interaction or logging, but without context, there's a medium risk of unexpected data transmission.
  • Shell: Executing shell commands can be high risk as it allows for arbitrary command execution, which could potentially lead to system compromise or data exfiltration.
  • Metadata: The missing repository and the new/inactive maintainer account raise concerns about the legitimacy of this package.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • encode("utf-8") req = urllib.request.Request( self.api_url + path, data=b
  • try: with urllib.request.urlopen(req, timeout=30) as resp: text = res
  • annel try: req = urllib.request.Request( webhook_url, data=json.dump
  • son"}, ) with urllib.request.urlopen(req, timeout=10) as resp: return {"ok":
  • str], bytes, str]: req = urllib.request.Request(url, headers={"User-Agent": "qa-mcp passive security
  • **(headers or {})}) with urllib.request.urlopen(req, timeout=timeout) as resp: body = resp.r
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • return False proc = subprocess.run(argv, text=True, capture_output=True) out = ((proc.stdo
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • lanned} token = token or os.getenv("GITHUB_TOKEN") or os.getenv("GH_TOKEN") if not token: raise
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: brainboxai.com>

✓ 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

  • 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 agent-qa-console 
Create a web-based code review tool called 'CodeInsight' using Python's 'agent-qa-console' package. This tool aims to streamline the process of reviewing and debugging code within a web application environment, offering developers a comprehensive suite of tools to enhance their workflow. Here’s a detailed breakdown of the project steps and features:

1. **Setup Environment**: Begin by setting up your development environment with Python, ensuring you have the 'agent-qa-console' package installed. Use virtual environments to manage dependencies.

2. **Project Structure**: Design the basic structure of the 'CodeInsight' application, including frontend and backend components. The frontend will be responsible for user interactions, while the backend will handle data processing and integration with 'agent-qa-console'.

3. **Integration with 'agent-qa-console'**: Utilize the 'agent-qa-console' package to embed a live browser console into the 'CodeInsight' interface. This console will allow users to inspect elements, view source maps, perform accessibility (a11y), performance (perf), and RTL audits directly from the web application.

4. **Element Selection and Source Mapping**: Implement functionality that allows users to select HTML elements within the live browser console. Provide real-time updates of these selections and associated JavaScript source mappings to help developers understand where changes need to be made.

5. **Audit Tools**: Integrate audit tools such as accessibility, performance, and RTL checks into the 'CodeInsight' application. These tools should provide detailed reports and actionable insights to improve the quality of the code being reviewed.

6. **Test-Fix-Verify Loop**: Enable a seamless test-fix-verify loop within 'CodeInsight'. Users should be able to run tests, make necessary fixes, and verify changes immediately through the integrated console.

7. **User Interface**: Design an intuitive and user-friendly interface for 'CodeInsight'. Ensure that all features are easily accessible and well-documented within the application itself.

8. **Documentation and Deployment**: Prepare comprehensive documentation for both developers and end-users. Document how to set up and use 'CodeInsight', including best practices for utilizing the 'agent-qa-console' features effectively. Finally, deploy the application to a hosting service like Heroku or AWS.

By following these steps and utilizing the 'agent-qa-console' package, you will create a powerful tool that significantly enhances the code review process, making it more efficient and effective.