AI Analysis
Final verdict: SUSPICIOUS
The package shows moderate risks in network and shell execution areas, with no clear evidence of malicious activities. However, the combination of these signals and the metadata risk due to low activity and a new maintainer warrant further investigation.
- moderate network risk
- potential shell misuse
- low activity and new maintainer
Per-check LLM notes
- Network: The network call pattern suggests the package may be fetching external resources or updates, which is not inherently suspicious but requires verification of intent and destination.
- Shell: The shell execution patterns indicate the package might be invoking external commands or tools, which could be legitimate if it's designed to integrate with system utilities, but raises concern about potential misuse or unintended behavior.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
- Metadata: The low activity and new maintainer suggest potential risks, but no clear indicators of malicious intent.
Package Quality Overall: Medium (6.2/10)
✦ High
Test Suite
9.0
Test suite present — 2 test file(s) found
Test runner config found: pyproject.toml2 test file(s) detected (e.g. test_minimal_app.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/GrinRus/ai_driven_dev_v2/tree/main/docsDetailed PyPI description (16683 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Type checker (mypy / pyright / pytype) referenced in project438 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 100 commits in GrinRus/ai_driven_dev_v2Two distinct contributors found
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
}/json" try: with urllib.request.urlopen(url, timeout=10) as response: # noqa: S310
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 10.0
Found 6 shell execution pattern(s)
ommandResult: completed = subprocess.run( tuple(argv), cwd=cwd, check=False,tr: try: result = subprocess.run( command, capture_output=True,ne: try: result = subprocess.run( [command_path, "--version"], capturne: try: result = subprocess.run( [command_path, "--help"], capture_o) try: process = subprocess.Popen( spec.command, cwd=spec.cwd,SCRIPT_FILENAME process = subprocess.Popen( (tokens[0], "app-server", "--listen", "stdio://"),
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 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "GrinRus" 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-driven-dev-v2
Your task is to develop a sophisticated mini-application called 'AI-Driven Bug Tracker' using the 'ai-driven-dev-v2' Python package. This application aims to streamline the process of managing bugs and issues within software development projects by leveraging AI-driven automation. The app will support multiple development environments and integrate seamlessly with existing workflows thanks to its runtime-agnostic nature. ### Core Functionality: 1. **Issue Tracking:** Users can log new issues or bugs, categorize them based on severity, type (e.g., bug, enhancement), and assign them to specific team members. 2. **AI-Powered Analysis:** Utilize 'ai-driven-dev-v2' to analyze issue descriptions for potential root causes and suggest solutions or related fixes. 3. **Automated Prioritization:** Implement an AI-driven system to prioritize issues based on impact, frequency, and other relevant metrics. 4. **Integration Capabilities:** Ensure the application can integrate with popular version control systems like Git and project management tools such as Jira or Trello. 5. **Document-First Orchestration:** Use 'ai-driven-dev-v2' to ensure that all interactions and processes within the application are well-documented and orchestrated efficiently, ensuring a smooth user experience and easy maintenance. ### Suggested Features: - **User Authentication:** Allow users to sign up and log in securely. - **Real-Time Notifications:** Notify users about updates or changes in issue status via email or in-app notifications. - **Customizable Dashboards:** Provide customizable dashboards where users can view their assigned tasks, recent activity, and more. - **Analytics & Reporting:** Offer detailed analytics and reporting features to help teams track progress and identify trends over time. ### How to Utilize 'ai-driven-dev-v2': - **Documentation Generation:** Use 'ai-driven-dev-v2' to automatically generate comprehensive documentation for your application, including API documentation, user guides, and FAQs. - **Orchestration:** Leverage the package's orchestration capabilities to manage the flow of data and operations between different components of the application, ensuring seamless integration and efficient processing. - **AI Integration:** Integrate 'ai-driven-dev-v2' to enhance the AI-powered analysis feature, enabling it to provide more accurate and insightful suggestions for resolving issues. By following these guidelines and utilizing 'ai-driven-dev-v2', you will create a powerful and user-friendly tool that significantly enhances the efficiency and effectiveness of software development teams.