🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate suspicion due to potential credential misuse and shell execution risks, despite having low risks in other categories.

Potential misuse of GITHUB_TOKEN
Unvalidated shell execution

Per-check LLM notes

Network: No network calls detected, which is normal and not indicative of malicious behavior.
Shell: Shell execution detected may be legitimate for command-line tool interaction but could pose risks if commands are executed without proper validation or user consent.
Obfuscation: No obfuscation patterns detected in the provided code snippet.
Credentials: The function b_token() retrieves a GITHUB_TOKEN environment variable and logs an audit event upon retrieval, which could be for legitimate authentication purposes but also indicates potential misuse if not properly managed.
Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other suspicious flags are present.

🔬 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)

try: completed = subprocess.run( list(command), cwd=str(cwd),
st[str]) -> str: result = subprocess.run(args, check=False, capture_output=True, text=True) if re
return token result = subprocess.run( ["gh", "auth", "token"], check=False,
"--title", title] return subprocess.run(cmd, capture_output=True, text=True) def run_validate(
["--db", str(db)] return subprocess.run(cmd, capture_output=True, text=True) def run_calibrate(db:
["--db", str(db)] return subprocess.run(cmd, capture_output=True, text=True) """Shared fixtures for

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

b_token() -> str: token = os.getenv("GITHUB_TOKEN") if token: emit_audit_event( "auth

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository kiloloop/agent-estimate appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Kiloloop" 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-estimate

Create a user-friendly web application using Flask that allows users to estimate the cost of running various AI tasks before they execute them. The application should integrate the 'agent-estimate' package to provide accurate cost estimations based on the specifics of the AI task, such as the model type, data size, and computational resources required. Here are the key steps and features for your project:

1. **Setup Project Environment**: Begin by setting up a virtual environment for your project and installing necessary packages including Flask and 'agent-estimate'.
2. **Design User Interface**: Develop a simple yet intuitive interface where users can input details about their AI task (e.g., model name, dataset size).
3. **Cost Estimation Functionality**: Utilize the 'agent-estimate' package to create a backend function that calculates the estimated cost based on user inputs. This function should consider factors like model complexity, training duration, and resource usage.
4. **Integration with UI**: Implement functionality to pass user inputs from the frontend to the backend cost estimation function and display the results back to the user.
5. **Error Handling and Validation**: Ensure robust error handling and input validation to manage incorrect or missing information gracefully.
6. **Documentation and Testing**: Write comprehensive documentation explaining how the application works and its integration with 'agent-estimate'. Also, conduct thorough testing to ensure accuracy and reliability of cost estimations.
7. **Deployment**: Finally, deploy your application on a platform like Heroku or AWS so it can be accessed publicly.

This project aims to help developers make informed decisions about running AI tasks by providing upfront cost estimates, thereby promoting efficient resource management.