🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits unusual behavior with shell command execution and lacks critical metadata such as a destination URL for network calls and a linked GitHub repository, raising concerns about its legitimacy.

Shell risk due to unusual execution of shell commands
Missing destination URL for network calls

Per-check LLM notes

Network: The network call pattern is typical for making HTTP requests, but the destination URL is missing, which raises some concern.
Shell: Executing shell commands to interact with Kubernetes resources like CRDs is unusual and suggests potential misuse if the package is not intended to manage Kubernetes configurations.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The maintainer has only one package, and there's no linked GitHub repository, which raises some suspicion but not enough to conclusively determine malice.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1168 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

17 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

"true" try: response = requests.Session().send(req.prepare(), timeout=30) if not response.ok:

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

alled.") try: content = subprocess.check_output([kubectl_exe, "get", "-o", "yaml", "crd", kubectl_crd], time
rror as e: try: crds = subprocess.check_output([kubectl_exe, "get", "crd"], timeout=30) if crds: e

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://`

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Daniel Goldman" 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 alpacloud.crdvis

Create a Python-based command-line tool named 'CRDInspector' that leverages the 'alpacloud.crdvis' package to visualize and manage Custom Resource Definitions (CRDs) within a Kubernetes cluster. This tool should offer users a comprehensive way to inspect, modify, and understand their CRDs more effectively. Here are the key functionalities your application should include:

1. **CRD Discovery**: Automatically discover all available CRDs in a connected Kubernetes cluster.
2. **CRD Visualization**: Provide a graphical representation of each discovered CRD, highlighting its schema, fields, and relationships with other resources.
3. **CRD Modification**: Allow users to edit CRD definitions directly from the command line interface (CLI), ensuring changes are validated against Kubernetes API schema requirements before applying them.
4. **CRD Export/Import**: Enable the export of CRD definitions to local files and import CRD definitions from local files into the Kubernetes cluster.
5. **CRD Documentation**: Generate human-readable documentation for each CRD, summarizing its purpose, usage examples, and related Kubernetes resources.
6. **Interactive CLI**: Develop an intuitive and interactive CLI that guides users through the process of discovering, modifying, and managing CRDs.

To achieve these goals, you will need to utilize the 'alpacloud.crdvis' package extensively. Specifically, you'll use it to parse and visualize CRD schemas, handle CRD data transformations, and provide graphical representations of CRDs. Additionally, ensure your application integrates seamlessly with the Kubernetes Python client library for handling connections to the Kubernetes API server. Your final product should be well-documented, easy to install via pip, and open-source on GitHub.