🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential network and shell execution vulnerabilities. The metadata risk adds uncertainty given the maintainer's account status.

moderate network risk
high shell risk
metadata concerns regarding maintainer's account

Per-check LLM notes

Network: Network calls could be legitimate for fetching updates or configuration, but need to verify URLs and purpose.
Shell: Execution of shell commands can pose significant risk if not properly sanitized or intended for unintended actions.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some concerns but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

entity) try: with urllib.request.urlopen(url, timeout=15) as response: if respons
try: with urllib.request.urlopen(url, timeout=15) as response: if res

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

n(command)}", flush=True) subprocess.run(command, cwd=cwd, env=release_env(), text=True, check=True,
nd)}", flush=True) return subprocess.check_output(command, cwd=cwd, env=release_env(), text=True).strip() de
env.update(extra_env) subprocess.run(command, cwd=cwd, env=env, text=True, check=True, timeout=ti
nd)}", flush=True) return subprocess.run(command, cwd=cwd, text=True, check=check) def capture(comm
nd)}", flush=True) return subprocess.check_output(command, cwd=cwd, text=True).strip() def update_version_fi

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: 163.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository hustyichi/agent-tune-kit appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 agent-tune-kit

Create a fully-functional mini-application named 'SkillMaster' that leverages the 'agent-tune-kit' Python package to streamline the process of training and validating AI agents. This application should allow users to define custom skills, run iterative tuning processes, and manage versions of their agents' performance data. Here’s a detailed step-by-step guide on what your application should achieve:

1. **Setup Environment**: Ensure that the 'agent-tune-kit' package is installed and properly configured within your Python environment.
2. **Define Custom Skills**: Users should be able to input their own skill definitions, which will be stored and managed within the application. These skills could range from simple tasks like arithmetic operations to more complex ones such as natural language processing tasks.
3. **Iterative Tuning Process**: Implement a feature where users can run iterative tuning sessions for their defined skills. Each session should be guided by the application, allowing users to adjust parameters and observe changes in real-time.
4. **Version Control**: Integrate version control into the application so that each iteration of a tuning session is saved and can be reviewed later. This will help users track improvements and regressions over time.
5. **Static Validation**: Use the static validation capabilities of 'agent-tune-kit' to ensure that all skills and tuning configurations meet predefined standards before execution.
6. **Results Management**: Provide a way to visualize and analyze the results of each tuning session. This could include graphs, tables, and other visual aids that make it easy to understand the performance of different iterations.
7. **User Interface**: Develop a user-friendly interface that allows users to interact with the application easily. This could be a command-line interface (CLI) or a graphical user interface (GUI), depending on the preference and technical expertise of the team.
8. **Documentation**: Create comprehensive documentation for both users and developers, explaining how to use the application effectively and how it integrates with 'agent-tune-kit'.

By following these steps, you will have built a powerful tool that not only demonstrates the capabilities of 'agent-tune-kit' but also provides significant value to anyone working with AI agent tuning.