AI Analysis
The package has moderate risks due to potential shell command execution and low maintainer activity, which raises concerns about its reliability and security.
- High shell risk indicating potential for unauthorized command execution
- Low maintainer activity suggesting poor package upkeep
Per-check LLM notes
- Network: The network call to GitHub appears legitimate for fetching resources or updates.
- Shell: Executing shell commands can be risky if not properly sanitized or controlled, potentially allowing unauthorized command execution.
- Obfuscation: The use of base64 decoding might indicate an attempt to obfuscate code, but it could also be used for legitimate purposes such as encoding signatures.
- Credentials: No clear patterns indicative of credential harvesting have been detected.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, but there's no clear evidence of malicious intent.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (10174 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
221 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Found 1 network call pattern(s)
keeps base CLI lean with httpx.Client(timeout=10.0) as client: response = client.get(GITHU
Found 1 obfuscation pattern(s)
try: signature = base64.b64decode(signature_b64) except Exception as exc: # pragma: no co
Found 1 shell execution pattern(s)
".join(cmd), cwd) return subprocess.run( # noqa: S603 cmd, cwd=cwd, capture
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based mini-application that leverages the 'agentops-accelerator' package to manage release readiness for Microsoft Foundry agents. Your application should include the following key functionalities: 1. **Agent Registration**: Users should be able to register new agents by providing necessary details such as agent type, environment (e.g., staging, production), and specific configurations. 2. **Release Gates Management**: Implement a feature where users can define different release gates for their agents. These gates could include checks for code quality, security scans, performance benchmarks, etc. 3. **Evidence Submission and Verification**: After passing the defined gates, users should submit evidence (like logs, screenshots, reports) to prove compliance. The app should verify these submissions against predefined criteria. 4. **Dashboard for Monitoring**: Develop a simple dashboard where users can monitor the status of their agents across different environments. This includes visual indicators for each gate's status (passed, failed, pending). 5. **Notification System**: Integrate a notification system that alerts users via email or SMS when a gate fails or when evidence submission is required. The 'agentops-accelerator' package will be utilized primarily for handling the registration and verification processes, ensuring that all operations align with Microsoft Foundry standards. Additionally, explore how you can use this package to streamline the evidence submission process, making it easier for developers to maintain compliance without manual intervention.
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