Presence Detection

Skill Profile

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• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Presence detection tracks user online/offline status, typing indicators, and activity states. This guide covers implementation patterns using WebSocket and Redis to provide real-time presence information for collaborative features, chat applications, and live user status.

Why This Matters

• User Experience: Real-time presence improves collaboration awareness
• Engagement: Typing indicators encourage real-time communication
• Performance: Efficient presence tracking reduces unnecessary API calls
• Scalability: Redis-based presence handles millions of users

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • User ID and authentication token
  • Presence state (online, away, busy, offline)
  • Room or channel ID for context
  • Typing state and timeout duration
• Entry Conditions:
  • WebSocket server running
  • Redis configured for presence storage
  • Authentication system in place
  • Frontend presence components ready
• Outputs:
  • Real-time presence updates
  • Typing indicator broadcasts
  • User status changes
  • Last seen timestamps
• Artifacts Required (Deliverables):
  • Presence service implementation
  • Typing indicator service
  • User status service
  • Presence UI components
• Acceptance Evidence:
  • Presence updates in real-time
  • Typing indicators working
  • Status changes broadcast correctly
  • Heartbeat mechanism functional
• Success Criteria:
  • Presence update latency < 100ms
  • Typing indicator accuracy 100%
  • Support 10,000+ concurrent users
  • Auto-away detection within 5 minutes

Skill Composition

• Depends on: WebSocket Patterns (34-real-time-features/websocket-patterns/), Redis Caching (04-database/redis-caching/)
• Compatible with: Live Notifications (34-real-time-features/live-notifications/), Collaborative Editing (34-real-time-features/collaborative-editing/)
• Conflicts with: None
• Related Skills: live-notifications, collaborative-editing

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 / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

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 & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

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