🤖 AI Analysis

Final verdict: SAFE

The package primarily appears to be legitimate, aimed at diagnosing issues with AI agents. While there are some potential risks identified, they do not strongly suggest a supply-chain attack.

Moderate shell risk due to git command usage
Potential credential risk as it retrieves GITHUB_TOKEN from environment variables

Per-check LLM notes

Network: The network calls appear to be making HTTP POST requests and GET requests to GitHub API endpoints, which could be for legitimate purposes like reporting or syncing.
Shell: The shell execution patterns indicate running git commands, possibly to fetch repository URLs or other metadata. However, the use of subprocess.run without proper validation or sanitization could pose a risk for command injection attacks.
Obfuscation: No obfuscation patterns detected in the provided code snippet.
Credentials: The code attempts to retrieve a GITHUB_TOKEN from environment variables and raises an error if it is not found, which could be a legitimate use for interacting with GitHub's API but also indicates a potential risk for credential harvesting.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

encode("utf-8") request = urllib.request.Request( webhook_url, data=data, hea
method="POST", ) with urllib.request.urlopen(request, timeout=10) as response: if respons
: str) -> str: response = requests.get( f"https://api.github.com/repos/{owner}/{repo}",
: str) -> str: response = requests.get( f"https://api.github.com/repos/{owner}/{repo}/git/r
str ) -> None: response = requests.post( f"https://api.github.com/repos/{owner}/{repo}/git/r
-> str | None: response = requests.get( f"https://api.github.com/repos/{owner}/{repo}/conte

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

"] try: result = subprocess.run( cmd, cwd=Path.cwd(), ca
tuple[str, str]: result = subprocess.run( ["git", "remote", "get-url", "origin"], cap

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

pull request.""" token = os.environ.get("GITHUB_TOKEN") if not token: raise ValueError("GITHUB_TOKEN

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Abhisekhpatel/AgentAutopsy appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Abhishek Patel" 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 agentautopsy

Create a Python-based application named 'AgentWatcher' which serves as a robust monitoring tool for debugging and troubleshooting issues in complex software agents. The application should be able to receive logs and performance data from these agents, analyze them using the 'agentautopsy' package, and provide detailed insights into why an agent might have failed or underperformed.

Step-by-Step Instructions:
1. Set up a basic Flask web server to handle incoming agent data. This includes setting up routes for receiving POST requests containing log files and performance metrics.
2. Implement a data parsing module that can process the incoming data, separating it into structured formats like JSON or CSV for easier analysis.
3. Integrate the 'agentautopsy' package into your application. Use its core functionalities to diagnose common failure points based on the parsed data. For example, if the logs indicate frequent timeouts or memory leaks, 'agentautopsy' should be able to pinpoint these issues and suggest possible causes.
4. Develop a user-friendly dashboard using HTML/CSS/JavaScript (with Flask integration) where users can upload their agent data, view diagnostic results, and access detailed explanations about potential failures.
5. Add a feature that allows users to save their diagnostic reports in PDF format for future reference.
6. Include an API documentation section within the application that explains how developers can integrate 'AgentWatcher' into their own systems for automated diagnostics.
7. Ensure the application is scalable and can handle large volumes of data efficiently.

Suggested Features:
- Real-time monitoring and alerts for critical issues.
- Historical data analysis to identify recurring problems.
- Integration with popular logging frameworks like Logstash or Splunk.
- Support for multiple types of agent data inputs.
- Customizable report generation options.

The 'agentautopsy' package will be crucial in automating the analysis phase, providing quick and accurate insights that would otherwise require extensive manual investigation. By leveraging its capabilities, 'AgentWatcher' aims to streamline the troubleshooting process for developers managing complex agent-based systems.