augint-shell

v0.103.0 suspicious
4.0
Medium Risk

Launch AI coding tools and local LLMs in Docker containers

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate suspicion due to its interaction with Docker containers, which could be used for malicious purposes. However, it does not exhibit other high-risk behaviors like obfuscation or credential harvesting.

  • Shell risk due to interaction with Docker containers
  • Low activity from the maintainer
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution patterns suggest interaction with Docker containers, which could be legitimate if the package is intended for Docker management but may indicate potential risk if usage is not transparent.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a single package and lacks PyPI classifiers, indicating potential low effort or new account status.

📦 Package Quality Overall: Low (3.8/10)

◈ 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 (10689 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

  • 118 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ree + new branch result = subprocess.run( [ "docker", "exec",
  • stderr: result2 = subprocess.run( [ "docker",
  • er_path.rsplit("/", 1)[0] subprocess.run( ["docker", "exec", container_name, "mkdir", "-p", p
  • tput=True, ) result = subprocess.run( ["docker", "cp", host_path, f"{container_name}:{con
  • " ".join(args)) result = subprocess.run(args, capture_output=True, text=True, timeout=30) if re
  • ssion_name) has_session = subprocess.run(check_cmd, capture_output=True).returncode == 0 if has_
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "svange" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with augint-shell 
Your task is to create a Python-based utility called 'AI-DevHelper' that integrates the 'augint-shell' package to streamline the development process by launching AI coding tools and local LLMs within Docker containers. This utility will serve as a command-line interface (CLI) tool that simplifies the setup and management of these AI resources for developers.

Step 1: Define the Core Functionality
- The utility should allow users to launch various AI coding tools and local LLMs provided by 'augint-shell'.
- Users should be able to specify which tool or model they want to run via command-line arguments.
- The utility must handle the creation and management of Docker containers for these tools and models.

Step 2: Implement Additional Features
- Integrate a feature that allows users to pass custom configuration files to the launched AI tools or models.
- Add a logging mechanism that records the container IDs and logs from each launched instance.
- Provide a way for users to stop or remove running containers from the CLI.

Step 3: User Interface Design
- Develop a clean and intuitive command-line interface.
- Include help and usage instructions accessible via command-line flags.

Step 4: Testing and Documentation
- Write comprehensive tests to ensure the utility functions correctly under different scenarios.
- Create a user manual that explains how to install, configure, and use the utility effectively.

How 'augint-shell' is Utilized:
- Use 'augint-shell' to automate the Docker container setup for AI tools and local LLMs.
- Leverage its capabilities to streamline the integration of new AI tools and models into the utility.
- Ensure that the utility can dynamically interact with 'augint-shell' to manage the lifecycle of these Docker containers.

💬 Discussion Feed

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