amplitude-ai

v1.8.0 suspicious
6.0
Medium Risk

Agent analytics for Amplitude

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is considered suspicious due to its use of dynamic importing and missing maintainer information. These factors raise concerns about its legitimacy.

  • Obfuscation risk due to dynamic importing
  • Missing maintainer information
Per-check LLM notes
  • Obfuscation: The code snippet uses dynamic importing which can be used for obfuscation but is also common in legitimate scenarios.
  • Credentials: No suspicious patterns related to credential harvesting were found.
  • Metadata: The package has some suspicious elements, notably the lack of maintainer information and a GitHub repository, which raises concerns about its legitimacy.

📦 Package Quality Overall: Medium (5.6/10)

✦ 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. generate_verify_test.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.amplitude.com
  • Detailed PyPI description (182731 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 476 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 3.0

Found 2 network call pattern(s)

  • ) if data else None req = urllib.request.Request(url, data=encoded, method=method) req.add_header
  • oded") try: with urllib.request.urlopen(req) as resp: body: dict[str, Any] = jso
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • pkg) try: __import__(module_name) results.append({"name": pkg, "installed": True}
✓ 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: amplitude.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 amplitude-ai 
Create a user engagement analytics dashboard using Python's 'amplitude-ai' package. This application will serve as a real-time monitoring tool for tracking user interactions across various digital platforms. The dashboard should allow users to input their Amplitude API credentials and select specific events or user properties they wish to track. Key functionalities include real-time event streaming, customizable visualization of user engagement metrics, and historical data analysis.

Step 1: Set up the project environment by installing necessary packages including 'amplitude-ai', 'streamlit' for web app development, and 'matplotlib' or 'seaborn' for data visualization.

Step 2: Design a user-friendly interface where users can enter their Amplitude API key and secret, and specify which events or user properties they want to analyze.

Step 3: Utilize the 'amplitude-ai' package to connect to the Amplitude API and fetch real-time data based on user inputs. Implement functionality to stream this data into the dashboard.

Step 4: Develop visualizations such as line graphs, bar charts, and pie charts to display user engagement metrics like session duration, event frequency, and user retention rates.

Step 5: Implement a feature that allows users to filter and segment data based on time periods, user demographics, or other relevant criteria.

Step 6: Add historical data analysis capabilities, allowing users to compare current trends with past performance and identify patterns over time.

Throughout the development process, focus on integrating 'amplitude-ai' effectively to ensure accurate and efficient data retrieval and processing. Ensure the final application is intuitive and provides actionable insights for improving user engagement strategies.