incident-severity-levels

id: SKL-incident-INCIDENTSEVERITYLEVELS name: Incident Severity Levels description: 'Incident Severity Levels provide a standardized framework for classifying incidents based on their impact on users, business operations, and SLA compliance. Consistent severity classification ensures ' version: 1.0.0 status: active owner: '@cerebra-team' last_updated: '2026-02-22' category: Backend tags:

• api
• backend
• server
• database stack:
• Python
• Node.js
• REST API
• GraphQL difficulty: Intermediate

Incident Severity Levels

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

Incident Severity Levels provide a standardized framework for classifying incidents based on their impact on users, business operations, and SLA compliance. Consistent severity classification ensures appropriate response times and prioritization.

Core Principle: "Severity should reflect business impact, not engineering complexity."

Why This Matters

• Consistent Response: All teams use same severity definitions
• Prioritization: Critical incidents get immediate attention
• SLA Compliance: Severity determines response time requirements
• Clear Communication: Everyone understands incident impact quickly
• Resource Allocation: Right resources matched to severity
• Reduced MTTR: Clear classification speeds up initial response

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:
  • Business impact thresholds (user count, revenue impact)
  • SLA definitions and requirements
  • Service inventory and criticality levels
• Entry Conditions:
  • Severity framework is defined and documented
  • Team is trained on severity assessment
  • Monitoring provides impact data
• Outputs:
  • Severity classification documentation
  • SLA compliance reports
  • Severity decision matrix
• Artifacts Required (Deliverables):
  • Severity definition document
  • SLA calculation spreadsheets
  • Incident classification guidelines
• Acceptance Evidence:
  • Severity classification accuracy reports
  • SLA compliance audit results
  • Incident response time metrics
• Success Criteria:
  • All incidents classified consistently
  • Severity accuracy > 95%
  • SLAs met for each severity level

Skill Composition

• Depends on: Incident Triage
• Compatible with: Escalation and Ownership, Communication Templates
• Conflicts with: Systems without clear severity definitions
• Related Skills:
  • 41-incident-management/incident-triage - Initial assessment
  • 41-incident-management/escalation-and-ownership - Ownership for escalation
  • 41-incident-management/communication-templates - Communication during incidents

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