| name | real-time-monitoring |
| description | Real-time monitoring provides live visibility into IoT device status Use when this capability is needed. |
| metadata | {"author":"amnadtaowsoam"} |
Real Time Monitoring
Skill Profile
(Select at least one profile to enable specific modules)
Overview
Real-time monitoring provides live visibility into IoT device status and sensor data. This guide covers dashboards, alerts, and WebSocket implementation for building monitoring systems that provide instant insights into IoT device health and performance.
Why This Matters
- Live Visibility: Instant insights into device status and sensor data
- Proactive Alerts: Immediate notification of issues
- Data Visualization: Real-time charts and graphs
- Scalability: Monitor thousands of devices simultaneously
- User Experience: Interactive dashboards with live updates
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:
- Device status data
- Sensor readings
- Alert thresholds
- Time-series data
- Entry Conditions:
- MQTT broker available
- WebSocket server running
- TimescaleDB configured
- Frontend application ready
- Outputs:
- Real-time dashboard
- Alert notifications
- Live charts
- Device status updates
- Artifacts Required (Deliverables):
- Real-time stream service
- Dashboard components
- Alert service
- Chart components
- WebSocket server
- Acceptance Evidence:
- Dashboard displays live data
- Alerts trigger correctly
- Charts update in real-time
- Device status is accurate
- Success Criteria:
- Dashboard latency < 500ms
- Alert delivery time < 1s
- Support 10K+ concurrent connections
- Chart update rate 60fps
Skill Composition
- Depends on: IoT Protocols (
36-iot-integration/iot-protocols/), Device Management (36-iot-integration/device-management/)
- Compatible with: Sensor Data Processing (
36-iot-integration/sensor-data-processing/), IoT Security (36-iot-integration/iot-security/)
- Conflicts with: None
- Related Skills: iot-protocols, device-management
Quick Start / Implementation Example
- Review requirements and constraints
- Set up development environment
- Implement core functionality following patterns
- Write tests for critical paths
- Run tests and fix issues
- Document any deviations or decisions
def example_function():
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)
| 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
2. Performance & Resources
3. Architecture & Scalability
4. Observability & Reliability
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
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
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