Awesome-omni-skill Disaster Recovery

'Disaster Recovery encompasses strategies and procedures for recovering

install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/backend/disaster-recovery" ~/.claude/skills/diegosouzapw-awesome-omni-skill-disaster-recovery && rm -rf "$T"
manifest: skills/backend/disaster-recovery/SKILL.md
safety · automated scan (low risk)
This is a pattern-based risk scan, not a security review. Our crawler flagged:
  • references .env files
  • references API keys
Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.
source content

Disaster Recovery

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

Disaster Recovery encompasses strategies and procedures for recovering from catastrophic failures and ensuring business continuity. This includes backup strategies, failover mechanisms, data recovery procedures, and business continuity planning.

Core Principle: "Plan for the worst, hope for the best. Test your recovery plan before you need it."

Why This Matters

  • Data Protection: Prevents permanent data loss from catastrophic failures
  • Business Continuity: Ensures operations can resume quickly after major incidents
  • Customer Trust: Demonstrates commitment to data protection and reliability
  • Compliance: Meets regulatory requirements for data backup and retention
  • Reduced Downtime: Minimizes RTO (Recovery Time Objective) and RPO (Recovery Point Objective)

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:
    • System architecture and component inventory
    • Data classification and criticality levels
    • RPO/RTO requirements per system
    • Regulatory compliance requirements
  • Entry Conditions:
    • Backup infrastructure is in place
    • Recovery procedures are documented
    • Team has been trained on recovery procedures
  • Outputs:
    • Backup and recovery documentation
    • Runbooks for disaster recovery
    • Monitoring and alerting configuration
  • Artifacts Required (Deliverables):
    • Backup schedules and retention policies
    • Recovery runbooks with step-by-step procedures
    • Contact information for recovery teams and vendors
  • Acceptance Evidence:
    • Successful backup restoration test (screenshot/log)
    • Recovery drill results (RTO/RPO met)
    • Compliance audit report
  • Success Criteria:
    • RPO and RTO targets met for all critical systems
    • Backups tested and verified regularly
    • Recovery procedures validated through drills

Skill Composition


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)

TypeFocus AreaRequired Scenarios / Mocks
UnitCore LogicMust cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage
IntegrationDB / APIAll external API calls or database connections must be mocked during unit tests
E2EUser JourneyCritical user flows to test
PerformanceLatency / LoadBenchmark requirements
SecurityVuln / AuthSAST/DAST or dependency audit
FrontendUX / A11yAccessibility 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 & 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