attenlabs-saa

v0.3.1 safe
3.0
Low Risk

Python SDK for AttentionLabs real-time attention detection.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate and serves its intended purpose without any signs of malicious activity. The low scores across all risks except obfuscation suggest benign use of the package features.

  • Low risk scores for network, shell, credential, and metadata risks.
  • Obfuscation risk is moderate but likely due to normal operational needs rather than malicious intent.
Per-check LLM notes
  • Network: The observed network calls are likely part of the package's intended functionality, possibly for token-based authentication and resource allocation.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The observed pattern is likely used for data decoding and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other suspicious activities were flagged.

📦 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 (10814 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

  • 28 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)

  • f.token else {} req = urllib.request.Request( allocate_url, method="POST", headers=he
  • try: with urllib.request.urlopen(req, timeout=BROKER_ALLOCATE_TIMEOUT_S) as resp:
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • (0, dtype=np.int16) raw = base64.b64decode(b64) return np.frombuffer(raw, dtype=np.int16).copy()
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Attention Labs" 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 attenlabs-saa 
Develop a real-time attention monitoring application using the 'attenlabs-saa' Python package. This application will serve as a tool for educators or business trainers to understand the engagement levels of their audience during live sessions or webinars. The app should include the following features:

1. **User Interface**: Design a simple yet intuitive UI where the trainer can input a session ID and start/stop the monitoring process.
2. **Real-Time Data Collection**: Utilize the 'attenlabs-saa' package to capture real-time attention data from participants. This involves setting up the SDK, initializing it with necessary credentials, and configuring it to listen for attention signals.
3. **Data Visualization**: Implement a dashboard within the application that displays the collected attention data in real-time. Use charts or graphs to show trends and spikes in participant engagement.
4. **Session Analysis**: After the session ends, provide a summary report that includes key metrics such as average attention level, peak times of engagement, and overall session engagement score.
5. **Custom Alerts**: Allow users to set custom thresholds for low attention levels. When these thresholds are crossed, send notifications to the trainer.
6. **Integration Capabilities**: Explore integrating the application with popular webinar platforms like Zoom or Microsoft Teams, allowing trainers to monitor attention directly within these tools.
7. **Security and Privacy**: Ensure all user data is handled securely, complying with relevant privacy laws and regulations.
8. **Feedback Loop**: Include functionality for participants to provide feedback on the content and format of the session, linking their experience directly to their attention levels.

The 'attenlabs-saa' package will be crucial in handling the real-time data collection aspect of the application. It will need to be properly initialized and configured to ensure accurate and reliable attention data is captured throughout the session.

💬 Discussion Feed

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