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Disaster Recovery

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BagelHole
disaster-recovery

Implement disaster recovery strategies and runbooks. Configure RPO/RTO targets and failover procedures. Use when planning for business continuity.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill namedisaster-recovery
Stars
1.1K
Forks
161
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BagelHole on GitHub. Read the source before you install it.

Installation

Install the Disaster Recovery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/compliance/continuity/disaster-recovery
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/compliance/continuity/disaster-recovery .claude/skills/disaster-recovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Disaster Recovery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Disaster Recovery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Disaster Recovery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Disaster Recovery

Implement disaster recovery strategies including RTO/RPO planning, AWS cross-region failover patterns, DR testing procedures, and automated failover scripts.

When to Use

  • Defining RTO and RPO targets for critical systems
  • Designing multi-region or multi-cloud disaster recovery architectures
  • Implementing automated failover and failback procedures
  • Conducting DR tests (tabletop, component, full failover)
  • Meeting compliance requirements for contingency planning (SOC 2, HIPAA, FedRAMP, ISO 27001)

RTO/RPO Planning

yaml
recovery_metrics:
  RTO:
    definition: "Recovery Time Objective - maximum acceptable downtime"
    measurement: "From incident declaration to service restoration"
    factors:
      - Failover automation maturity
      - Data replication lag
      - DNS propagation time
      - Application warm-up time
      - Verification procedures

  RPO:
    definition: "Recovery Point Objective - maximum acceptable data loss"
    measurement: "Time gap between last good backup and the incident"
    factors:
      - Backup frequency
      - Replication method (sync vs. async)
      - Transaction log shipping interval
      - Cross-region replication lag

service_tier_targets:
  tier_1_critical:
    examples: "Authentication, payment processing, core API"
    rto: "< 15 minutes"
    rpo: "< 1 minute (near-zero)"
    strategy: "Multi-site active-active or warm standby"
    replication: "Synchronous or near-synchronous"
    testing: "Quarterly failover test"

  tier_2_essential:
    examples: "Customer dashboards, reporting, notifications"
    rto: "< 1 hour"
    rpo: "< 15 minutes"
    strategy: "Warm standby or pilot light"
    replication: "Asynchronous with short interval"
    testing: "Semi-annual failover test"

  tier_3_standard:
    examples: "Internal tools, analytics, batch processing"
    rto: "< 4 hours"
    rpo: "< 1 hour"
    strategy: "Pilot light or backup and restore"
    replication: "Periodic snapshots"
    testing: "Annual failover test"

  tier_4_non_essential:
    examples: "Development environments, documentation sites"
    rto: "< 24 hours"
    rpo: "< 24 hours"
    strategy: "Backup and restore"
    replication: "Daily backups"
    testing: "Annual backup restore verification"

DR Strategies Comparison

yaml
strategies:
  backup_and_restore:
    rto: "Hours"
    rpo: "Hours (depends on backup frequency)"
    cost: "$"
    description: "Regular backups stored in DR region. Restore from backup when needed."
    aws_services:
      - "S3 cross-region replication for backups"
      - "RDS automated snapshots copied to DR region"
      - "AMI copies in DR region"
      - "Terraform/CloudFormation for infrastructure rebuild"
    pros: "Lowest cost, simplest to maintain"
    cons: "Longest recovery time, highest data loss potential"

  pilot_light:
    rto: "Minutes to hours"
    rpo: "Minutes"
    cost: "$$"
    description: "Core infrastructure running in DR region (databases replicated). Scale up compute on failover."
    aws_services:
      - "RDS cross-region read replica (always running)"
      - "S3 cross-region replication"
      - "AMIs pre-built in DR region"
      - "Auto Scaling groups at zero/minimal capacity"
    pros: "Fast database recovery, moderate cost"
    cons: "Compute scale-up adds to recovery time"

  warm_standby:
    rto: "Minutes"
    rpo: "Seconds to minutes"
    cost: "$$$"
    description: "Scaled-down but functional environment in DR region. Scale up on failover."
    aws_services:
      - "RDS cross-region read replica"
      - "ECS/EKS running at reduced capacity"
      - "Route53 health checks for automated DNS failover"
      - "Global Accelerator for traffic management"
    pros: "Fast failover, reduced risk"
    cons: "Higher baseline cost for idle resources"

  multi_site_active:
    rto: "Near-zero"
    rpo: "Near-zero"
    cost: "$$$$"
    description: "Active-active across regions. Traffic served from both regions simultaneously."
    aws_services:
      - "DynamoDB Global Tables or Aurora Global Database"
      - "Route53 latency/weighted routing"
      - "CloudFront with multi-origin"
      - "Global Accelerator"
      - "ECS/EKS in both regions"
    pros: "Minimal downtime and data loss"
    cons: "Highest cost, most complex to operate"

AWS Cross-Region DR Implementation

bash
# === Database Replication ===

# Create cross-region RDS read replica
aws rds create-db-instance-read-replica \
  --db-instance-identifier prod-db-dr-replica \
  --source-db-instance-identifier arn:aws:rds:us-east-1:123456789012:db:prod-db \
  --db-instance-class db.r6g.large \
  --region us-west-2 \
  --kms-key-id arn:aws:kms:us-west-2:123456789012:alias/rds-dr-key \
  --multi-az \
  --tags Key=Purpose,Value=DR Key=Environment,Value=production

# Create Aurora Global Database for near-zero RPO
aws rds create-global-cluster \
  --global-cluster-identifier prod-global-db \
  --source-db-cluster-identifier arn:aws:rds:us-east-1:123456789012:cluster:prod-aurora-cluster \
  --region us-east-1

# Add secondary region to Aurora Global Database
aws rds create-db-cluster \
  --db-cluster-identifier prod-aurora-dr \
  --global-cluster-identifier prod-global-db \
  --engine aurora-postgresql \
  --region us-west-2 \
  --kms-key-id arn:aws:kms:us-west-2:123456789012:alias/aurora-dr-key

# === Storage Replication ===

# S3 cross-region replication
cat > /tmp/replication-config.json << 'EOF'
{
  "Role": "arn:aws:iam::123456789012:role/s3-replication-role",
  "Rules": [
    {
      "ID": "ReplicateAll",
      "Status": "Enabled",
      "Filter": {"Prefix": ""},
      "Destination": {
        "Bucket": "arn:aws:s3:::prod-data-dr-usw2",
        "StorageClass": "STANDARD",
        "EncryptionConfiguration": {
          "ReplicaKmsKeyID": "arn:aws:kms:us-west-2:123456789012:alias/s3-dr-key"
        }
      },
      "DeleteMarkerReplication": {"Status": "Enabled"}
    }
  ]
}
EOF

aws s3api put-bucket-replication \
  --bucket prod-data-use1 \
  --replication-configuration file:///tmp/replication-config.json

# === DNS Failover ===

# Route53 health check for primary region
aws route53 create-health-check --caller-reference "prod-health-$(date +%s)" \
  --health-check-config '{
    "Type": "HTTPS",
    "FullyQualifiedDomainName": "api.example.com",
    "Port": 443,
    "ResourcePath": "/health",
    "RequestInterval": 10,
    "FailureThreshold": 3,
    "EnableSNI": true
  }'

# Configure failover routing
aws route53 change-resource-record-sets --hosted-zone-id Z123456 \
  --change-batch '{
    "Changes": [
      {
        "Action": "UPSERT",
        "ResourceRecordSet": {
          "Name": "api.example.com",
          "Type": "A",
          "SetIdentifier": "primary",
          "Failover": "PRIMARY",
          "AliasTarget": {
            "HostedZoneId": "Z1234PRIMARY",
            "DNSName": "primary-alb.us-east-1.elb.amazonaws.com",
            "EvaluateTargetHealth": true
          },
          "HealthCheckId": "health-check-id-primary"
        }
      },
      {
        "Action": "UPSERT",
        "ResourceRecordSet": {
          "Name": "api.example.com",
          "Type": "A",
          "SetIdentifier": "secondary",
          "Failover": "SECONDARY",
          "AliasTarget": {
            "HostedZoneId": "Z5678SECONDARY",
            "DNSName": "dr-alb.us-west-2.elb.amazonaws.com",
            "EvaluateTargetHealth": true
          }
        }
      }
    ]
  }'

Failover Script

bash
#!/usr/bin/env bash
# dr-failover.sh - Execute disaster recovery failover to DR region
set -euo pipefail

DR_REGION="us-west-2"
PRIMARY_REGION="us-east-1"
SLACK_WEBHOOK="${DR_SLACK_WEBHOOK}"
LOG_FILE="/var/log/dr-failover-$(date +%Y%m%d-%H%M%S).log"

log() {
  echo "[$(date -u +%Y-%m-%dT%H:%M:%SZ)] $1" | tee -a "$LOG_FILE"
}

notify() {
  curl -s -X POST "$SLACK_WEBHOOK" \
    -H "Content-Type: application/json" \
    -d "{\"text\":\"DR FAILOVER: $1\"}" > /dev/null
}

log "=== DR Failover Initiated ==="
notify "DR failover initiated to $DR_REGION"

# Step 1: Promote RDS read replica
log "Step 1: Promoting RDS read replica in $DR_REGION"
aws rds promote-read-replica \
  --db-instance-identifier prod-db-dr-replica \
  --region "$DR_REGION"
log "Waiting for RDS promotion to complete..."
aws rds wait db-instance-available \
  --db-instance-identifier prod-db-dr-replica \
  --region "$DR_REGION"
log "RDS promotion complete"
notify "RDS read replica promoted to primary in $DR_REGION"

# Step 2: Scale up application in DR region
log "Step 2: Scaling up application in $DR_REGION"
aws ecs update-service \
  --cluster prod-cluster-dr \
  --service api-service \
  --desired-count 4 \
  --region "$DR_REGION"
log "Waiting for ECS service to stabilize..."
aws ecs wait services-stable \
  --cluster prod-cluster-dr \
  --services api-service \
  --region "$DR_REGION"
log "ECS service scaled up and stable"
notify "Application scaled up in $DR_REGION"

# Step 3: Verify health
log "Step 3: Verifying health in $DR_REGION"
for i in $(seq 1 10); do
  STATUS=$(curl -s -o /dev/null -w "%{http_code}" "https://dr-alb.us-west-2.elb.amazonaws.com/health")
  if [ "$STATUS" = "200" ]; then
    log "Health check passed (attempt $i)"
    break
  fi
  log "Health check failed (attempt $i, status $STATUS), retrying..."
  sleep 10
done

if [ "$STATUS" != "200" ]; then
  log "ERROR: Health check failed after 10 attempts"
  notify "ALERT: DR health check failing - manual intervention required"
  exit 1
fi

# Step 4: Update DNS (if not using automatic Route53 failover)
log "Step 4: DNS failover (Route53 automatic failover should handle this)"
log "Verifying DNS resolution..."
DR_IP=$(dig +short api.example.com)
log "api.example.com resolves to: $DR_IP"

# Step 5: Verify end-to-end
log "Step 5: End-to-end verification"
RESPONSE=$(curl -s "https://api.example.com/health")
log "Health response: $RESPONSE"

log "=== DR Failover Complete ==="
notify "DR failover to $DR_REGION complete. Service restored."

# Generate failover report
cat > "/var/log/dr-failover-report-$(date +%Y%m%d).md" << EOF
# DR Failover Report
- **Date:** $(date -u +%Y-%m-%dT%H:%M:%SZ)
- **Primary Region:** $PRIMARY_REGION
- **DR Region:** $DR_REGION
- **RTO Actual:** Calculate from incident declaration
- **RPO Actual:** Check replication lag at time of incident
- **Status:** Operational in DR region
- **Actions Required:**
  - [ ] Monitor error rates and latency
  - [ ] Plan failback when primary region is restored
  - [ ] Conduct post-incident review
EOF

DR Testing Procedures

yaml
dr_test_types:
  tabletop_exercise:
    frequency: Quarterly
    duration: "1-2 hours"
    participants: "Engineering, SRE, management, communications"
    process:
      - Present a disaster scenario (region outage, data corruption, etc.)
      - Walk through the response step by step
      - Identify gaps in runbooks and communication plans
      - Document action items
    output: "Tabletop exercise report with findings and action items"

  component_failover:
    frequency: Monthly
    duration: "1-4 hours"
    scope: "Individual component failover (database, single service)"
    process:
      - Select component for testing
      - Execute failover procedure from runbook
      - Measure actual RTO and RPO
      - Execute failback procedure
      - Document results
    output: "Component test report with measured RTO/RPO"

  full_failover:
    frequency: Annually
    duration: "4-8 hours (scheduled maintenance window)"
    scope: "Complete regional failover of all tier 1 and tier 2 services"
    process:
      1_preparation:
        - Schedule maintenance window and notify stakeholders
        - Verify DR environment is healthy
        - Brief all participating teams
        - Set up war room communication channel
      2_execute:
        - Simulate primary region failure
        - Execute failover runbooks for all services
        - Record timestamps at each milestone
      3_verify:
        - Run end-to-end test suite against DR environment
        - Verify data consistency
        - Check monitoring and alerting in DR region
        - Confirm external integrations work
      4_failback:
        - Restore primary region
        - Re-establish replication
        - Execute failback to primary
        - Verify data consistency post-failback
      5_report:
        - Document actual RTO and RPO for each service
        - Compare against targets
        - List all issues encountered
        - Create action items for improvements
    output: "Full DR test report with measured vs. target metrics"

dr_test_checklist:
  before_test:
    - [ ] Test plan documented and approved
    - [ ] Maintenance window scheduled and communicated
    - [ ] All DR runbooks reviewed and updated
    - [ ] DR environment health verified
    - [ ] Monitoring configured in DR region
    - [ ] Communication channel established
    - [ ] Rollback plan confirmed

  during_test:
    - [ ] Timestamps recorded for each step
    - [ ] Screenshots captured for evidence
    - [ ] Issues logged in real-time
    - [ ] Data consistency verified
    - [ ] External integrations tested
    - [ ] Health checks passing in DR

  after_test:
    - [ ] Failback completed successfully
    - [ ] Primary region replication re-established
    - [ ] Data consistency verified post-failback
    - [ ] Test report written with metrics
    - [ ] Action items created and assigned
    - [ ] Runbooks updated based on findings
    - [ ] Results presented to management

Terraform DR Infrastructure

hcl
# DR region infrastructure
provider "aws" {
  alias  = "dr"
  region = "us-west-2"
}

resource "aws_db_instance" "dr_replica" {
  provider               = aws.dr
  identifier             = "prod-db-dr-replica"
  replicate_source_db    = aws_db_instance.primary.arn
  instance_class         = "db.r6g.large"
  storage_encrypted      = true
  kms_key_id             = aws_kms_key.dr_rds.arn
  multi_az               = true
  deletion_protection    = true
  skip_final_snapshot    = false

  tags = {
    Purpose     = "DR"
    Environment = "production"
  }
}

resource "aws_route53_health_check" "primary" {
  fqdn              = "primary-alb.us-east-1.elb.amazonaws.com"
  port               = 443
  type               = "HTTPS"
  resource_path      = "/health"
  failure_threshold  = 3
  request_interval   = 10
  enable_sni         = true

  tags = {
    Name = "primary-health-check"
  }
}

resource "aws_route53_record" "failover_primary" {
  zone_id        = aws_route53_zone.main.zone_id
  name           = "api.example.com"
  type           = "A"
  set_identifier = "primary"

  failover_routing_policy {
    type = "PRIMARY"
  }

  alias {
    name                   = aws_lb.primary.dns_name
    zone_id                = aws_lb.primary.zone_id
    evaluate_target_health = true
  }

  health_check_id = aws_route53_health_check.primary.id
}

resource "aws_route53_record" "failover_secondary" {
  zone_id        = aws_route53_zone.main.zone_id
  name           = "api.example.com"
  type           = "A"
  set_identifier = "secondary"

  failover_routing_policy {
    type = "SECONDARY"
  }

  alias {
    name                   = aws_lb.dr.dns_name
    zone_id                = aws_lb.dr.zone_id
    evaluate_target_health = true
  }
}

DR Compliance Checklist

yaml
dr_compliance_checklist:
  planning:
    - [ ] RTO and RPO targets defined per service tier
    - [ ] DR strategy selected based on targets and budget
    - [ ] DR architecture documented with diagrams
    - [ ] Failover and failback runbooks written
    - [ ] Communication plan for DR events documented
    - [ ] DR roles and responsibilities assigned

  implementation:
    - [ ] Cross-region database replication configured
    - [ ] Storage replication configured (S3, EBS snapshots)
    - [ ] DNS failover routing configured
    - [ ] DR region infrastructure provisioned (IaC)
    - [ ] Monitoring and alerting configured in DR region
    - [ ] Secrets and credentials available in DR region

  testing:
    - [ ] Tabletop exercises conducted quarterly
    - [ ] Component failover tests conducted monthly
    - [ ] Full failover test conducted annually
    - [ ] Actual RTO/RPO measured and compared to targets
    - [ ] Test results documented and reviewed
    - [ ] Runbooks updated based on test findings

  operational:
    - [ ] Replication lag monitored with alerting
    - [ ] DR environment health checked regularly
    - [ ] Backup integrity verified monthly
    - [ ] DR runbooks reviewed and updated quarterly
    - [ ] DR test evidence archived for compliance audits

Best Practices

  • Define RTO and RPO targets based on business impact analysis, not technical convenience
  • Choose the DR strategy that matches your targets and budget: do not over-engineer or under-invest
  • Automate failover as much as possible to reduce human error and recovery time
  • Test DR procedures regularly at increasing levels of complexity (tabletop, component, full)
  • Measure actual RTO and RPO during tests and compare against targets every time
  • Include failback procedures in your DR plan: getting back to normal is as important as failing over
  • Monitor replication lag continuously and alert when it exceeds RPO thresholds
  • Keep DR infrastructure managed by the same IaC as production to prevent configuration drift
  • Practice DR in non-emergency conditions so the team is prepared when a real disaster occurs
  • Archive DR test results as compliance evidence for SOC 2, HIPAA, and other frameworks

Frequently asked questions

What does the Disaster Recovery AI skill do?

Implement disaster recovery strategies and runbooks. Configure RPO/RTO targets and failover procedures. Use when planning for business continuity.

Why use Disaster Recovery on TypingMind?

Because you install it once and use it with any model. Disaster Recovery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Disaster Recovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/compliance/continuity/disaster-recovery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Disaster Recovery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Disaster Recovery?

As many as you like. As long as a model supports skills, you can use Disaster Recovery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Disaster Recovery AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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