analog-fetcher

v0.1.1 suspicious
4.0
Medium Risk

JS-aware fetcher for Analog — Playwright-based HTML fetching that satisfies the analog.fetcher.Fetcher protocol.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, its metadata quality and maintainer activity levels raise some concerns, suggesting potential instability or lack of support.

  • Low maintainer activity and poor metadata quality.
  • No significant risks detected in other categories.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute 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 could indicate potential issues but does not strongly suggest malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Brief PyPI description (724 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

  • 15 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

No suspicious network call patterns found

✓ 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-fetcher 
Create a web scraping tool called 'Analog Scraper' using Python that leverages the 'analog-fetcher' package to efficiently scrape complex JavaScript-driven websites. This tool will be designed to help users extract specific data from modern web pages that heavily rely on JavaScript for content loading and rendering. Here’s a detailed breakdown of the project steps and features:

1. **Project Setup**: Initialize your Python environment with the necessary packages including 'analog-fetcher', 'beautifulsoup4', and 'pandas'. Ensure you have a virtual environment set up for dependency management.

2. **Core Functionality**: Implement a core functionality that allows users to input a URL and specify elements they want to scrape. Utilize 'analog-fetcher' to fetch the page content accurately, even if it requires executing JavaScript.

3. **Data Extraction**: After fetching the content, use BeautifulSoup to parse the HTML and extract the desired data. Allow users to define which tags and attributes to target.

4. **Data Storage & Export**: Once the data is extracted, store it in a structured format like a pandas DataFrame. Provide options to export the data as CSV, JSON, or Excel files.

5. **Advanced Features**:
   - **Dynamic Content Handling**: Enhance the scraper to handle dynamically loaded content, such as infinite scroll pages or AJAX requests.
   - **Error Handling & Retry Mechanism**: Implement robust error handling to manage network issues and timeouts. Include a retry mechanism for failed requests.
   - **User Interface**: Develop a simple command-line interface (CLI) or a basic web UI to make the tool more user-friendly.

6. **Testing & Validation**: Write unit tests to ensure each part of the tool works as expected. Validate the scraped data against known good samples to ensure accuracy.

7. **Documentation & Deployment**: Document the setup process, usage instructions, and any limitations of the tool. Consider deploying the tool as a standalone application or a Docker container for easy distribution.

Throughout the development, utilize 'analog-fetcher' to its fullest potential by leveraging its ability to execute JavaScript on fetched pages, ensuring that all dynamic content is correctly rendered before parsing.

💬 Discussion Feed

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