AI Analysis
Final verdict: SUSPICIOUS
The package exhibits a moderate level of risk due to its shell execution patterns and potential issues with metadata, although no direct evidence of malicious activity was found.
- High shell risk due to Docker and database operations
- Suspicious metadata indicating a possibly new or inactive account
Per-check LLM notes
- Network: Network calls appear to be for making HTTP POST requests and checking network availability, which may be legitimate depending on the package's functionality.
- Shell: Shell execution patterns include running Docker commands and database readiness checks, which could indicate the package performs operations requiring shell access, but also raises concern for potential unauthorized command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of potentially being a new or inactive account with minimal engagement, raising suspicion.
Package Quality Overall: Low (4.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (7591 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
5.0
Partial type annotation coverage
260 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 100 commits in james20140802/argosTwo distinct contributors found
Heuristic Checks
Outbound Network Calls
score 9.0
Found 6 network call pattern(s)
""" try: with socket.create_connection((host, port), timeout=timeout): return Trueeep_alive": 0} async with httpx.AsyncClient(timeout=60) as client: resp = await client.post(f"{_hink"] = think async with httpx.AsyncClient(timeout=httpx.Timeout(timeout, connect=10)) as client:eep_alive": 0} async with httpx.AsyncClient(timeout=30) as client: resp = await client.post(f"{_eep_alive": 0} async with httpx.AsyncClient(timeout=httpx.Timeout(120, connect=10)) as client: r" try: async with httpx.AsyncClient( timeout=_ROBOTS_FETCH_TIMEOUT, foll
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 10.0
Found 5 shell execution pattern(s)
ma") try: proc = subprocess.run( ["docker", "info"], capture_output=cwd) try: proc = subprocess.run( cmd, cwd=str(cwd) if cwd else None,try: proc = subprocess.run( ["pg_isready", "-h", host, "-p", str(port)]cmd) try: proc = subprocess.run( cmd, capture_output=False,s", " ".join(cmd)) return subprocess.run( cmd, capture_output=True, text=True
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 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 argos-scout
Create a fully-functional mini-application called 'TechTrendBot' using the Python package 'argos-scout'. This bot will serve as an automated tracker of AI technology trends and will notify users via Slack about the latest developments. Here’s a detailed plan on how to build it: 1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python installed and then install the 'argos-scout' package along with other necessary packages such as Slack API client for Python. 2. **Configuration**: Configure your application to connect with your Slack workspace. You will need to obtain a Slack API token which allows your application to interact with the Slack API. Store this token securely and use it to authenticate your application with Slack. 3. **Integration with Argos-Scout**: Use the 'argos-scout' package to set up tracking for specific keywords related to AI technologies. Define these keywords based on the most recent trends in AI, such as 'Machine Learning', 'Deep Learning', 'Neural Networks', etc. The package should automatically gather data from relevant sources and update you on new developments. 4. **Notification System**: Implement a system where every time there is a significant update or new trend detected by 'argos-scout', the bot sends a notification to a specified Slack channel. These notifications should include brief summaries of the updates, links to articles or studies, and any other pertinent information. 5. **User Interface**: While the primary function of the bot will be through Slack, consider adding a simple web interface where users can manage their subscriptions to different technology trends, view recent updates, and provide feedback. 6. **Testing and Deployment**: Test your application thoroughly to ensure all functionalities work as expected. Once tested, deploy your application to a cloud service like AWS or Heroku so it runs continuously. 7. **Maintenance and Updates**: Regularly update the keywords being tracked and the sources of information to keep the bot relevant and useful. Also, maintain the application by fixing bugs and improving performance. **Suggested Features**: - Customizable keyword tracking based on user preferences. - Historical data visualization of tracked trends. - Integration with more social media platforms for broader reach. - User feedback mechanism to improve the relevance of tracked trends.