AI Analysis
The package shows low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the low maintainer activity and poor metadata quality raise some suspicion.
- Low maintainer activity
- Poor metadata quality
Per-check LLM notes
- Network: No network calls detected, which is normal and not indicative of malicious activity.
- Shell: Shell execution is present but seems to be used for Git operations, likely for interacting with GitHub pull requests, which aligns with the package's probable functionality.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows low maintainer activity and poor metadata quality, raising suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (3.6/10)
Test suite present — 9 test file(s) found
Test runner config found: conftest.py9 test file(s) detected (e.g. conftest.py)
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
60 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
Found 4 shell execution pattern(s)
.CompletedProcess: return subprocess.run(args, cwd=cwd, capture_output=True, text=True, check=check).CompletedProcess: return subprocess.run(args, capture_output=True, text=True, check=check) def is_""" try: result = subprocess.run( ["gh", "pr", "edit", str(pr_number), "--base",result = _subprocess.run(cmd, shell=True, cwd=root) if result.returncode != 0: e
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based mini-application called 'PRStacker' that leverages the 'arc-prs' package to manage stacked pull requests in a GitHub repository. PRStacker should be designed to streamline the process of organizing and merging multiple pull requests into a single stack, making it easier for developers to handle complex changes in a more manageable way. ### Features: 1. **Pull Request Collection:** Users should be able to specify a list of pull request numbers or a range of numbers, and PRStacker will gather these pull requests from the specified GitHub repository. 2. **Dependency Checking:** Before stacking pull requests, PRStacker should check if there are any dependencies between the selected pull requests, ensuring that they can be merged in the correct order without conflicts. 3. **Stack Creation:** Once dependency checks are passed, PRStacker will create a new pull request that stacks all the selected pull requests together. 4. **Interactive Interface:** Provide an interactive command-line interface where users can select options, input necessary details, and view progress updates. 5. **Configuration Management:** Allow users to save their preferred settings such as default repository, personal access token, etc., in a configuration file to avoid repetitive data entry. 6. **Logging and Notifications:** Implement logging for actions performed by PRStacker and send notifications via email or Slack when certain events occur, like successful stacking or encountering errors. ### Utilizing 'arc-prs': - Use 'arc-prs' to interact with the local environment for managing the pull requests before pushing them to GitHub. This includes fetching details about pull requests, checking out branches, and creating new pull requests. - Integrate 'arc-prs' commands into your application flow to automate the process of stacking pull requests locally before they are pushed to the remote repository. - Ensure that your application handles exceptions gracefully and provides meaningful error messages when issues arise during the use of 'arc-prs'. ### Deliverables: - A fully functional Python application named 'PRStacker'. - Documentation on how to install and run PRStacker, including setup instructions for 'arc-prs'. - Example use cases demonstrating the application's capabilities.
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