🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of suspicion due to potential misuse of shell execution capabilities and the lack of detailed metadata about its maintainers.

Shell risk detected
Lack of maintainer metadata

Per-check LLM notes

Network: No network calls detected, which is low risk.
Shell: Detection of shell execution may indicate legitimate functionality but requires further investigation to ensure it's not being used maliciously.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The maintainer has a new or inactive account and lacks author information, raising some suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

pass try: return subprocess.run( ["ps", "-o", "comm=", "-p", str(ppid)],

✓ 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 forjd/agenthint 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 agenthint

Create a CLI tool named 'AIInspector' using Python that leverages the 'agenthint' package to detect whether it's being run within an AI agent environment (such as LangChain, Anthropic, etc.). This tool should intelligently adapt its output format based on the detected environment, providing more concise or tailored information for AI agents compared to human users.

Step 1: Set up your Python environment and install necessary packages including 'agenthint'.
Step 2: Design the core functionality of 'AIInspector', which includes detecting the runtime environment and adapting the output accordingly.
Step 3: Implement additional features such as logging the detection results, allowing customization of output formats through command-line arguments, and providing a help menu with usage instructions.
Step 4: Test 'AIInspector' thoroughly in various environments to ensure it correctly identifies different AI agent runtimes and adjusts its output appropriately.
Step 5: Document your code and create a README file explaining how to use 'AIInspector', what problem it solves, and how the 'agenthint' package is integrated into the solution.