🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators, with no evidence of network calls, shell executions, or credential harvesting. The low maintainer activity and poor metadata quality raise some concerns but do not indicate malicious intent.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls suggest the package does not engage in external communications, which is normal unless specific network interaction is expected.
Shell: No shell execution detected, indicating no direct system command execution, which is typical and safe unless the package's functionality requires it.
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, but there are no clear signs of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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

Repository scitix/agent-sandbox appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

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 agent-sandbox-harbor

Create a mini-application named 'HarborBenchRunner' using Python, which leverages the 'agent-sandbox-harbor' package to facilitate running benchmark tests on a pool of Agent Sandboxes connected to a Harbor instance. This application will serve as a user-friendly interface for developers and researchers to execute various benchmark suites such as Terminal-Bench, SWE-bench, and Harbor itself without needing deep knowledge of the underlying system configurations.

**Features to Implement:**
1. **User Interface**: Develop a simple command-line interface (CLI) where users can select from different benchmark suites (Terminal-Bench, SWE-bench, Harbor) and specify parameters like the number of iterations, sandbox pool size, etc.
2. **Configuration Management**: Allow users to configure their Harbor instance details, including URL, credentials, and sandbox pool specifications directly through the CLI.
3. **Benchmark Execution**: Automate the process of setting up the specified number of sandboxes in the pool, running the chosen benchmark suite(s), collecting results, and tearing down the environment after completion.
4. **Result Visualization**: Integrate a basic visualization tool within the CLI to display benchmark results graphically, highlighting performance metrics over time.
5. **Logging & Reporting**: Ensure all activities, including setup, execution, and teardown phases, are logged. Provide an option to generate comprehensive reports post-execution summarizing key findings.
6. **Customization Support**: Enable users to customize benchmark configurations, such as adding new benchmarks or modifying existing ones, through a configuration file.

**Utilization of 'agent-sandbox-harbor':**
- Use the package's functionalities to create and manage sandbox environments efficiently.
- Leverage its built-in support for benchmark suites to streamline the execution process.
- Employ its API or command-line tools to interact seamlessly with Harbor instances and handle sandbox lifecycle management.

Your task is to design and implement this application, ensuring it adheres to best coding practices, provides clear documentation, and showcases the capabilities of 'agent-sandbox-harbor'.