analog-sdk

v0.1.1 suspicious
5.0
Medium Risk

Python SDK for Analog — instantly distill webpages into structured data.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential external service communication and low maintainer activity, raising concerns about its legitimacy and ongoing support.

  • network risk due to external service communication
  • low maintainer activity and poor metadata quality
Per-check LLM notes
  • Network: The observed network call patterns indicate the package is using httpx to communicate with external services, which could be legitimate if the package is designed for API interactions.
  • Shell: No shell execution patterns were detected, indicating no immediate risk of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which may indicate a lack of transparency and accountability.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • Test runner config found: pyproject.toml
  • 4 test file(s) detected (e.g. test_analog.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (349 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

  • 80 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 7.5

Found 5 network call pattern(s)

  • ey}" self._http = httpx.Client( base_url=self._base_url, ti
  • e: self._client = httpx.Client( timeout=self._timeout, foll
  • Transport(handler) http = httpx.Client( base_url="https://test.example.com", transp
  • kwargs["http_client"] = httpx.Client( base_url=kwargs.get("base_url") or "https://tes
  • tcher() fetcher._client = httpx.Client( transport=httpx.MockTransport(handler), hea
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: getanalog.io>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 analog-sdk 
Create a Python-based web scraping utility named 'WebDataDistiller' using the 'analog-sdk' package. This utility should allow users to input a URL of a webpage and extract structured data from it. Here’s a detailed breakdown of what the application should achieve:

1. **User Interface**: Develop a simple command-line interface where users can input a URL.
2. **Data Extraction**: Utilize the 'analog-sdk' to scrape the webpage and convert the raw HTML content into structured data. Ensure that the extracted data includes key elements such as titles, descriptions, images, and links.
3. **Data Output**: Provide options for the user to output the structured data in various formats like JSON, CSV, or plain text.
4. **Error Handling**: Implement robust error handling to manage cases where the URL might be invalid or the webpage cannot be accessed.
5. **Customization Options**: Allow users to specify which types of data they want to extract (e.g., just images, or just text).
6. **Performance Optimization**: Optimize the code to handle large webpages efficiently without causing memory issues.
7. **Documentation**: Include comprehensive documentation explaining how to install and use the tool, along with examples of different configurations and outputs.

The 'analog-sdk' package will be the backbone of this utility, enabling the transformation of unstructured web data into structured formats that are easier to work with. Your task is to write clean, efficient, and well-documented Python code that leverages the 'analog-sdk' capabilities to deliver a useful and user-friendly tool.

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