🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks with notable concerns around shell execution and potential credential misuse. While not conclusive evidence of malicious intent, these factors warrant caution.

High shell risk due to unvalidated command execution
Potential credential risk from suspicious file paths

Per-check LLM notes

Network: The network calls seem to be making HTTP requests which could be part of normal package functionality but should be scrutinized for unexpected external communications.
Shell: The use of subprocess.run for git operations might be legitimate if the package is intended for Git project management, but it poses a risk if it executes commands without proper validation or sanitization.
Obfuscation: No obfuscation patterns detected in the code.
Credentials: Suspicious file paths referenced in error handling suggest potential misuse, but further investigation is needed to confirm if it's legitimate testing or actual credential harvesting.
Metadata: The maintainer seems new or inactive, and the repository lacks community engagement.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

+ self.path request = urllib.request.Request( url, data=body or None, headers=headers
try: response = urllib.request.urlopen(request) # noqa: S310 (trusted upstream URL)
) as proxy: request = urllib.request.Request( proxy.base_url + "/v1/messages",
POST", ) with urllib.request.urlopen(request) as response: assert response.st

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

return False result = subprocess.run( ["git", "-C", str(project_root), "ls-files", "--err
t: str) -> bool: result = subprocess.run( ["git", "-C", str(project_root), "ls-files", "--err
None try: return subprocess.run(argv, stdin=stdin).returncode except FileNotFoundError a
f _git_init_commit(root): subprocess.run(["git", "init", "-q", str(root)], check=True) subprocess
, str(root)], check=True) subprocess.run(["git", "-C", str(root), "add", "-A"], check=True) subpr
"add", "-A"], check=True) subprocess.run( [ "git", "-C",

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

or bad in ("../escape.json", "/etc/passwd", "a/../../b.json"): with pytest.raises(ProviderErr

✓ 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 2.0

1 maintainer concern(s) found

Author "Alice Voland" 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 agedum

Create a versatile command-line tool using the 'agedum' package that allows users to manage and execute various AI agents' commands in a unified manner. This tool will serve as a bridge between different AI agent interfaces, enabling seamless interaction without needing to understand each agent's specific command syntax. Here’s how your project will work:

1. **Project Overview**: Develop a CLI tool named 'AgentBridge'. This tool should support multiple AI agents, such as ChatGPT, Claude, and others, by abstracting their unique command structures into a standardized format.
2. **Core Features**:
- **Agent Configuration**: Users can add, remove, and configure supported agents through AGENTS.md and .agents/skills files.
- **Command Execution**: Execute commands across different agents using a common syntax, allowing for easy integration and testing of various AI services.
- **Output Standardization**: Ensure all outputs from different agents are presented in a consistent format for easier comparison and analysis.
3. **Utilizing 'agedum'**:
- Use 'agedum' to translate the standardized command inputs into the specific formats required by each agent during runtime.
4. **Development Steps**:
- **Setup Environment**: Install necessary packages including 'agedum', set up the project structure, and create the initial AGENTS.md file.
- **Define Agents**: Populate the .agents/skills directory with skill definitions for each supported agent.
- **CLI Development**: Build the CLI interface using Python, incorporating argparse or click for command parsing.
- **Command Handling**: Implement logic to handle commands based on the current configuration, utilizing 'agedum' for translation.
- **Testing**: Test the application thoroughly with various agents and commands to ensure reliability and consistency.
5. **Additional Enhancements**:
- Support for adding custom agents by users.
- Detailed logging and error handling for better user experience and debugging.
6. **Deliverables**: A fully functional CLI tool capable of managing and executing commands across multiple AI agents, along with comprehensive documentation explaining setup, usage, and customization options.