apple-appstore-metadata-extractor

v0.3.1 suspicious
5.0
Medium Risk

Extract and monitor metadata from Apple App Store applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential misuse of shell commands and concerns over the repository's low activity and new author status.

  • shell risk due to execution of external commands
  • low repository activity and new author
Per-check LLM notes
  • Network: The network calls appear to be part of fetching metadata from the Apple App Store, which is expected behavior.
  • Shell: The shell execution patterns may indicate the package is attempting to run external commands or scripts, which could potentially be used for unintended purposes if not properly controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious intent.
  • Metadata: The repository has low activity and the author seems new or inactive, raising some suspicion.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 22 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 22 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Bickster-LLC/appstore-metadata-extractor-
  • Detailed PyPI description (26098 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 161 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 57 commits in Bickster-LLC/appstore-metadata-extractor-python
  • Single author but highly active (57 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • try: response = requests.get(url, timeout=30) response.raise_for_status()
  • =self.timeout) return aiohttp.ClientSession(timeout=timeout) class ITunesAPIExtractor(BaseExtractor):
  • ) self._session = aiohttp.ClientSession(timeout=timeout) return self._session async def
  • try: async with aiohttp.ClientSession() as session: html = await self._fetch_page(
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • ") try: result = subprocess.run(cmd, cwd=project_root) sys.exit(result.returncode)
  • "json", ] result = subprocess.run(cmd, capture_output=True, text=True) if result.returnco
  • validate", url] result = subprocess.run(cmd, capture_output=True, text=True) if result.returnco
  • ng CLI help...") result = subprocess.run( [sys.executable, "-m", "appstore_metadata_extractor
  • ommand help...") result = subprocess.run( [sys.executable, "-m", "appstore_metadata_extractor
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: bickster.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 apple-appstore-metadata-extractor 
Create a Python-based mini-application named 'AppStoreAnalyzer' which leverages the 'apple-appstore-metadata-extractor' package to provide detailed insights into Apple App Store applications. Your task is to develop a tool that not only extracts metadata but also allows users to track changes over time for specific apps. Here's a step-by-step guide on what your application should do:

1. **User Interface**: Design a simple command-line interface where users can input the app ID of the Apple App they wish to analyze.
2. **Metadata Extraction**: Use 'apple-appstore-metadata-extractor' to fetch the latest metadata for the specified app, including details like app name, description, screenshots, ratings, and reviews.
3. **Change Tracking**: Implement functionality to periodically (e.g., daily) check for updates in the app's metadata and notify users if any significant changes occur, such as version updates, price changes, or new reviews.
4. **Data Storage**: Store historical metadata in a local SQLite database for comparison purposes. This will enable your application to detect changes over time accurately.
5. **Report Generation**: Allow users to generate reports summarizing the app's performance over a selected period, highlighting key metrics like download trends, rating fluctuations, and review sentiment analysis.
6. **Custom Alerts**: Users should have the option to set up custom alerts based on specific criteria, such as when a competitor's app receives a significant update or when their own app reaches certain milestones.
7. **Security Measures**: Ensure all interactions with the App Store API are secure and compliant with Apple's guidelines, handling any rate limits or errors gracefully.

Your application should utilize 'apple-appstore-metadata-extractor' effectively by integrating its core functionalities into each step above, making it a robust tool for developers and marketers interested in analyzing and tracking Apple App Store applications.