🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low direct risks such as network calls, shell execution, obfuscation, and credential harvesting. However, its metadata suggests low maintenance and potential poor intent, raising suspicion.

Low maintenance indicators
Lack of author information and GitHub repository

Per-check LLM notes

Network: No network calls detected, indicating low risk.
Shell: Shell execution is present but seems to be for running OPA eval, possibly for policy evaluation which could be legitimate depending on the package's purpose.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintenance and potentially poor intent due to the lack of author information and a GitHub repository.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

str]: try: return subprocess.run(args, check=True, capture_output=True, text=True, timeout=OP
ding="utf-8") completed = subprocess.run( [ "opa", "eval",

✓ 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 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 acs-generator

Create a command-line tool using Python that allows users to generate customized Agent Control Specifications (ACS) policies for their agents based on the 'acs-generator' package. This tool should provide a simple yet powerful interface for specifying various control parameters such as agent capabilities, permissions, and constraints. The application should support the following key features:

1. **Policy Template Creation**: Users should be able to define custom templates for ACS policies, including placeholders for specific details like agent names, actions, and conditions.
2. **Parameterization**: Implement functionality to fill in template placeholders with user-defined values dynamically. For example, a user might specify that an agent named 'DataProcessor' should have permission to read data but not write it under certain conditions.
3. **Output Generation**: Once all parameters are set, the tool should generate a complete ACS policy document in a specified format (JSON, YAML, etc.).
4. **Validation**: Integrate validation checks to ensure that the generated ACS policies adhere to the specified standards and requirements.
5. **Interactive Mode**: Offer an interactive mode where users can input values directly through prompts, making it easier to generate policies without manually editing templates.
6. **Help Documentation**: Provide comprehensive help documentation and examples within the tool to assist users in understanding how to use each feature effectively.

The 'acs-generator' package will be utilized throughout the development process to handle the core functionalities of generating, validating, and formatting ACS policies. Ensure that your implementation leverages the package's capabilities efficiently while providing a seamless user experience.