Video Analytics

Skill Profile

(Select at least one profile to enable specific modules)

• [ ] DevOps
• [x] Backend
• [ ] Frontend
• [ ] AI-RAG
• [ ] Security Critical

Overview

Video analytics tracks viewer behavior, engagement, and quality metrics. This guide covers player events, QoE metrics, and analytics dashboards for understanding video performance and viewer engagement.

Why This Matters

Video analytics is critical for video platforms as it directly impacts:

• Content Strategy: Understanding what viewers watch
• User Experience: Identifying and fixing quality issues
• Monetization: Optimizing ad placement and engagement
• Technical Decisions: Data-driven infrastructure improvements

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Core Concepts

1. Player Events: Play, pause, seek, end events
2. Watch Time: Total time viewers spend watching
3. Engagement Metrics: Completion rate, interaction rate
4. QoE (Quality of Experience): Startup time, buffering, bitrate
5. Heatmaps: Visual representation of viewer retention
6. A/B Testing: Comparing different video variants
7. Sampling: Reducing data volume for high-traffic scenarios

Inputs / Outputs / Contracts

Skill Composition

• Depends on: None
• Compatible with: None
• Conflicts with: None
• Related Skills: None

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions

• Player SDK supports event tracking
• Network connectivity for sending events
• User consent for analytics tracking
• Sufficient storage for event data

Compatibility

| Player | Event Support | Heatmaps | QoE Tracking | |--------|--------------|-----------|---------------| | Video.js | Full | Yes | Yes | | HLS.js | Full | Yes | Yes | | Shaka | Full | Yes | Yes | | Native HTML5 | Partial | Limited | Limited |

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Test Scenario Matrix (QA Strategy)

| Type | Focus Area | Required Scenarios / Mocks | | :--- | :--- | :--- | | Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage | | Integration | DB / API | All external API calls or database connections must be mocked during unit tests | | E2E | User Journey | Critical user flows to test | | Performance | Latency / Load | Benchmark requirements | | Security | Vuln / Auth | SAST/DAST or dependency audit | | Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• [ ] Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• [ ] Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• [ ] Authorization: Validate user permissions before state changes

2. Performance & Resources

• [ ] Execution Efficiency: Consider time complexity for algorithms
• [ ] Memory Management: Use streams/pagination for large data
• [ ] Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• [ ] Design Pattern: Follow SOLID principles, use Dependency Injection
• [ ] Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• [ ] Logging Standards: Structured JSON, include trace IDs request_id
• [ ] Metrics: Track error_rate, latency, queue_depth
• [ ] Error Handling: Standardized error codes, no bare except
• [ ] Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives

1. Always track key events without impacting performance
2. Anonymize user data for privacy compliance
3. Use sampling for high-traffic scenarios
4. Pre-aggregate metrics for efficient querying
5. Set data retention policies for compliance
6. Provide real-time dashboards for monitoring

Definition of Done (DoD) Checklist

• [ ] Tests passed + coverage met
• [ ] Lint/Typecheck passed
• [ ] Logging/Metrics/Trace implemented
• [ ] Security checks passed
• [ ] Documentation/Changelog updated
• [ ] Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure