Devops Automation logo

Devops Automation

Organization
claude-office-skills
devops-automation

DevOps and IT Ops automation - CI/CD, monitoring, incident management, and infrastructure workflows

Overview

Publisherclaude-office-skills
Repositoryskills
Skill namedevops-automation
Stars
514
Forks
97
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 claude-office-skills on GitHub. Read the source before you install it.

Installation

Install the Devops Automation 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/claude-office-skills/skills/tree/main/devops-automation
  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/claude-office-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/devops-automation .claude/skills/devops-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Devops Automation 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 Devops Automation 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 Devops Automation 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.

DevOps Automation

Automate DevOps workflows including CI/CD pipelines, monitoring, incident management, and infrastructure operations. Based on n8n's IT Ops workflow templates.

Overview

This skill covers:

  • CI/CD pipeline automation
  • Monitoring and alerting
  • Incident management
  • Infrastructure automation
  • Deployment workflows

CI/CD Automation

GitHub Actions Integration

yaml
workflow: "GitHub CI/CD Notifications"

triggers:
  - github_push
  - github_pull_request
  - github_workflow_run
  
on_push:
  action:
    - trigger_ci: if_main_branch
    - notify_slack:
        channel: "#deployments"
        message: |
          📦 *New Push to {branch}*
          
          Commit: `{commit_sha_short}`
          Author: {author}
          Message: {commit_message}
          
          [View Diff]({compare_url})

on_pr_opened:
  action:
    - notify_slack:
        channel: "#code-review"
        message: |
          🔀 *New Pull Request*
          
          Title: {pr_title}
          Author: {author}
          Branch: {head} → {base}
          
          [Review PR]({pr_url})
    - assign_reviewers: based_on_codeowners
    - run_ci_checks

on_workflow_complete:
  action:
    - notify_slack:
        message: |
          {status_emoji} *Build {status}*
          
          Workflow: {workflow_name}
          Branch: {branch}
          Duration: {duration}
          
          {if_failed: [View Logs]({logs_url})}

Deployment Pipeline

yaml
deployment_pipeline:
  stages:
    build:
      trigger: push_to_main
      steps:
        - checkout_code
        - install_dependencies
        - run_tests
        - build_artifact
        - push_to_registry
        
    staging:
      trigger: build_success
      steps:
        - deploy_to_staging
        - run_integration_tests
        - notify_qa
        
    production:
      trigger: manual_approval
      steps:
        - create_backup
        - deploy_to_production
        - run_smoke_tests
        - notify_team
        
  rollback:
    trigger: deployment_failed OR manual
    steps:
      - revert_to_previous
      - notify_team
      - create_incident

Monitoring & Alerting

Alert Routing

yaml
alert_routing:
  sources:
    - prometheus
    - datadog
    - cloudwatch
    - new_relic
    
  severity_levels:
    critical:
      response_time: 5_minutes
      channels: [pagerduty, slack_urgent, sms]
      escalation: immediate
      
    high:
      response_time: 15_minutes
      channels: [slack_alerts, email]
      escalation: after_15_minutes
      
    medium:
      response_time: 1_hour
      channels: [slack_alerts]
      
    low:
      response_time: 24_hours
      channels: [slack_logging]
      
  routing_rules:
    - if: service == "payments"
      team: payments_oncall
      severity_boost: +1
      
    - if: service == "auth"
      team: security_oncall
      
    - default:
      team: platform_oncall

Alert Templates

yaml
alert_templates:
  infrastructure:
    cpu_high:
      title: "🔥 High CPU Usage"
      body: |
        Server: {host}
        CPU: {cpu_percent}%
        Duration: {duration}
        
        Threshold: {threshold}%
        
        [View Dashboard]({grafana_url})
        
    memory_critical:
      title: "💾 Critical Memory"
      body: |
        Server: {host}
        Memory: {memory_percent}%
        Available: {available_mb}MB
        
        [SSH to Server]({ssh_link})
        
    disk_full:
      title: "💿 Disk Space Critical"
      body: |
        Server: {host}
        Disk: {disk_percent}%
        Available: {available_gb}GB
        
        Suggestion: Clean logs or expand volume
        
  application:
    error_spike:
      title: "📈 Error Rate Spike"
      body: |
        Service: {service}
        Error Rate: {error_rate}%
        Normal: {baseline}%
        
        Top Errors:
        {top_errors}
        
    latency_high:
      title: "🐢 High Latency"
      body: |
        Service: {service}
        P99 Latency: {p99_ms}ms
        Threshold: {threshold_ms}ms

Incident Management

Incident Workflow

yaml
incident_workflow:
  detection:
    sources: [monitoring, user_report, automated_check]
    
  triage:
    auto_severity:
      - if: affects_payments
        severity: critical
      - if: affects_auth
        severity: critical
      - if: affects_api AND error_rate > 10%
        severity: high
        
  response:
    critical:
      - create_incident_channel: "#inc-{timestamp}"
      - page_oncall: immediately
      - notify_stakeholders: [engineering_lead, product]
      - start_war_room: zoom_link
      - create_status_page: incident
      
    high:
      - create_incident_channel
      - notify_oncall: slack
      - create_ticket: jira
      
  communication:
    internal:
      frequency: every_30_minutes
      channel: incident_channel
      template: |
        📊 *Incident Update*
        
        Status: {status}
        Impact: {impact}
        Next update: {next_update_time}
        
        Current actions:
        {action_items}
        
    external:
      channel: status_page
      template: customer_facing_update
      
  resolution:
    steps:
      - confirm_resolution
      - update_status_page: resolved
      - notify_stakeholders
      - schedule_postmortem
      - close_incident_channel: after_24h

Postmortem Template

yaml
postmortem_template:
  sections:
    summary:
      - incident_title
      - duration
      - severity
      - impact
      
    timeline:
      format: |
        | Time | Event |
        |------|-------|
        | {time} | {event} |
        
    root_cause:
      - what_happened
      - why_it_happened
      - contributing_factors
      
    impact:
      - users_affected
      - revenue_impact
      - sla_breach
      
    resolution:
      - how_it_was_fixed
      - time_to_detect
      - time_to_resolve
      
    action_items:
      format: |
        | Action | Owner | Due Date | Status |
        |--------|-------|----------|--------|
        
    lessons_learned:
      - what_went_well
      - what_went_poorly
      - lucky_breaks

Infrastructure Automation

Server Provisioning

yaml
provisioning_workflow:
  trigger: jira_ticket OR slack_request
  
  steps:
    1. validate_request:
        check: [budget_approval, security_review]
        
    2. create_infrastructure:
        terraform:
          - vpc
          - security_groups
          - ec2_instances
          - load_balancer
          
    3. configure_server:
        ansible:
          - base_configuration
          - security_hardening
          - monitoring_agent
          - application_setup
          
    4. validate:
        - health_check
        - security_scan
        - performance_baseline
        
    5. notify:
        slack: "✅ Server {hostname} is ready"
        include: [ssh_access, dashboard_link]

Scheduled Maintenance

yaml
maintenance_automation:
  tasks:
    certificate_renewal:
      schedule: "30 days before expiry"
      action:
        - request_new_cert: letsencrypt
        - deploy_cert
        - verify_ssl
        - notify: if_failure
        
    security_patching:
      schedule: "weekly"
      action:
        - check_updates
        - if_critical: immediate_patch
        - else: schedule_maintenance_window
        
    log_rotation:
      schedule: "daily"
      action:
        - rotate_logs
        - compress_old
        - upload_to_s3
        - delete_local: older_than_7_days
        
    backup_verification:
      schedule: "weekly"
      action:
        - restore_to_test_env
        - run_integrity_checks
        - report_status

Kubernetes Automation

K8s Workflows

yaml
kubernetes_automation:
  deployment:
    trigger: docker_image_pushed
    steps:
      - update_manifest: with_new_image_tag
      - apply_to_staging
      - run_tests
      - if_success: apply_to_production
      
  scaling:
    trigger: metric_threshold
    rules:
      - if: cpu > 80%
        action: scale_up
        max_replicas: 10
      - if: cpu < 20%
        action: scale_down
        min_replicas: 2
        
  rollback:
    trigger: health_check_failed
    action:
      - kubectl_rollout_undo
      - notify_team
      - create_incident

Output Example

Request: "Set up deployment notifications for GitHub Actions"

Output:

markdown
# GitHub Actions Deployment Notifications

## n8n Workflow

```yaml
trigger: GitHub Webhook
events: [workflow_run]

Notification Templates

Build Started:

🚀 *Deployment Started*

Branch: main
Commit: abc1234
Author: @developer
Triggered by: Push

[View Workflow](https://github.com/...)

Build Success:

✅ *Deployment Successful*

Environment: Production
Duration: 3m 42s
Version: v1.2.3

Changes:
• Feature X
• Bug fix Y

[View Deployment](https://app.example.com)

Build Failed:

❌ *Deployment Failed*

Stage: Test
Error: npm test failed

[View Logs](https://github.com/...)
[Retry](https://github.com/...)

Slack Integration

yaml
channel: "#deployments"
mention_on_failure: "@oncall"
thread_replies: true

---

*DevOps Automation Skill - Part of Claude Office Skills*

Frequently asked questions

What does the Devops Automation AI skill do?

DevOps and IT Ops automation - CI/CD, monitoring, incident management, and infrastructure workflows

Why use Devops Automation on TypingMind?

Because you install it once and use it with any model. Devops Automation 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 Devops Automation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/claude-office-skills/skills/tree/main/devops-automation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Devops Automation?

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 Devops Automation?

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

Is the Devops Automation AI skill free?

Yes. It is published on GitHub by claude-office-skills 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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