DevOps Claude Skills
4 344 DevOps skills from 392 authors. They ship releases, run infrastructure and keep watch over what is deployed. Half of them fit into 2 095 tokens or less — that is what one costs your context window when the agent loads it. 513 ship runnable scripts rather than instructions alone. 28 of them cannot work without an MCP server, most often rube. We also found 633 copies of these same skills sitting in other people's repositories — counted once here, not 633 times.
4 344 unique 392 authors 2 063 updated this month 579 from vendors
> Migrate a .NET application from the classic Elastic APM .NET agent to the EDOT .NET SDK. Use when switching from Elastic.Apm.* packages to Elastic.OpenTelemetry.
> Instrument a Java application with the Elastic Distribution of OpenTelemetry (EDOT) Java agent for automatic tracing, metrics, and logs. Use when adding observability to a Java service that has no existing APM agent.
> Migrate a Java application from the classic Elastic APM Java agent to the EDOT Java agent. Use when switching from elastic-apm-agent.jar to elastic-otel-javaagent.jar.
> Instrument a Python application with the Elastic Distribution of OpenTelemetry (EDOT) Python agent for automatic tracing, metrics, and logs. Use when adding observability to a Python service that has no existing APM agent.
> Investigate Kubernetes workload, node, and control-plane issues using OTel telemetry (EDOT). Use when diagnosing pod failures (CrashLoopBackOff, OOMKilled, Error), node pressure, resource exhaustion, image pull failures, admission rejections, autoscaling anomalies, or correlating K8s state with application signals. OTel ingest path only — the legacy ECS Kubernetes integration shape is out of scope.
> orchestration. Use when the user asks about LLM monitoring, GenAI observability, or AI cost/quality.
> Search and filter Observability logs using ES|QL. Use when investigating log spikes, errors, or anomalies; getting volume and trends; or drilling into services or containers during incidents.
> Create and manage SLOs in Elastic Observability using the Kibana API. Use when defining SLIs, setting error budgets, or managing SLO lifecycle.
> Assess APM service health using SLOs, alerts, ML, throughput, latency, error rate, and dependencies. Use when checking service status, performance, or when the user asks about service health.
Prepare and verify a staged or production deployment with rollback and smoke checks.
Verify feature, bug, UI, API, mobile, security, or deployment work against acceptance criteria.
Configure release signing, R8 obfuscation, and App Bundle publishing for Android. Use when setting up signing configs, enabling minification, adding ProGuard keep rules, or preparing for Play Store submission.
Enforce structured JSON logging, OpenTelemetry distributed tracing, and RED metrics across backend services. Use when adding request correlation, setting up tracing spans, defining SLO burn-rate alerts, or instrumenting middleware.
>- Turn a complete beginner's app idea into a buildable plan, then keep them oriented while they build. Use it whenever someone who has never coded wants to build or "vibe code" an app, has an idea but no idea where to start, or wants it turned into a full journey, a validate-only pass ("is my idea worth building", "reality-check this idea"), and a plan-only path for someone arriving with validation done ("I already validated my idea", "I have the research, just plan my MVP", "skip the research"). ALSO use it when a non-coder local vs. staging vs. production, putting an app online (deploy/ship), or keeping API keys safe. AND use it in Checkup Mode when someone who built with AI says it became a mess, the AI keeps breaking things or going in circles, they're scared to touch their code, or they ask "is my code organized" or "can you clean it up." Built for people who don't know what an API, database, or GitHub is, so reach for it when they never say "plan" or "architecture." Not for an experienced dev debugging, refactoring, or setting up CI/CD.
Use when building a new Butterbase app from scratch, creating a full-stack application, or when the user asks to set up a complete backend with database, auth, and deployment
Use when deploying a frontend (React, Next.js, or static HTML) to a live URL on Butterbase, or when troubleshooting deployment issues like MIME type errors or blank pages
Use when developing, deploying, or debugging Butterbase serverless functions, or when the user needs to add backend logic like webhooks, scheduled jobs, or custom API endpoints
Use as the deploy-verification stage of the Butterbase journey, after journey-frontend (or after any build stage if there is no frontend). Smoke-tests the deployed app — hits the live URL, invokes any deployed functions, checks auth round-trip. Writes results to docs/butterbase/04-build-log.md. Blocks journey-submit if smoke fails.
Use as the durable-objects build stage of the Butterbase journey. Implements the Durable section of 02-plan.md by delegating to durable-objects. Calls manage_durable_objects (deploy). Skipped if the plan has no per-key stateful actors (chat rooms, multiplayer, rate limiters).
Use as the frontend build stage of the Butterbase journey. Implements the Frontend section of 02-plan.md by delegating to deploy-frontend. Scaffolds (if needed) and deploys via create_frontend_deployment + manage_frontend (start_deployment). Sets VITE_API_URL and VITE_APP_ID env. Skipped if the plan is API-only.
Use as the functions build stage of the Butterbase journey. Implements the Functions section of 02-plan.md by delegating to function-dev for each function. Calls deploy_function per function; smokes each with invoke_function. Skipped if the plan has no functions.
Use as stage 1 of the Butterbase journey, when the user has only a rough idea ("I want to build something that..."). Conducts a concrete, one-question-at-a-time brainstorm that surfaces who the user is, what they do first, what the must-haves are, and inline-tags Butterbase capabilities (→ manage_schema, → deploy_function, etc.). Produces docs/butterbase/01-idea.md.
Use as the final stage of the Butterbase journey when hackathon_mode is true and journey-deploy has passed. Resolves which hackathon to submit to (asking the user when multiple are open), walks every field in the hackathon's returned field_schema with the user one at a time, then calls prep_and_submit_hackathon_entry. Writes the receipt to docs/butterbase/05-submission.md.
Use as the optional substrate-linking stage of the Butterbase journey, after deploy and before submit. Asks whether to connect the deployed app to the owner's substrate (so functions get ctx.substrate). Skipped by default in hackathon mode.
Use when the user says "build an app", "let's start", "help me build", "I have an idea for", "ship it", or otherwise signals they want to go from idea to deployed Butterbase app. Orchestrates the full guided journey (idea → plan → preflight → build → deploy → optional hackathon submit) by reading docs/butterbase/00-state.md and dispatching to the next stage skill.
Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).
Bootstrap new 33GOD ecosystem agents with standardized configuration. Use when creating new agents, spawning workers, deploying Yi nodes, or when the user says "spin up an agent", "create a new agent", "deploy a new worker", or needs a new agent for a specific pipeline role. Handles workspace creation, config injection, skill installation, provider mirroring, channel binding, and 33GOD ecosystem onboarding (GOD Docs, Plane, Bloodbank, memory).
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
Expert IoT engineer specializing in connected device architectures, edge computing, and IoT platform development. Masters IoT protocols, device management, and data pipelines with focus on building scalable, secure, and reliable IoT solutions.
Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
Audit a proposed assessment for construct validity, reliability, and alignment to learning objectives. Use when reviewing or quality-assuring assessments before deployment.
Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.
Reference guide for Agentica multi-agent infrastructure APIs
Lambda best practices, S3 event patterns, SQS/SNS fanout, and DynamoDB access patterns for serverless AWS architectures.
Azure Functions, Cosmos DB modeling, Service Bus patterns, Bicep templates
Traffic splitting, health checks, automated rollback, progressive delivery, and canary analysis for safe deployments.
GitHub Actions workflow templates, matrix builds, caching, and monorepo CI strategies
GitHub Actions workflow patterns, matrix builds, caching strategies, deployment pipelines, artifact management, and rollback procedures.
Completion Check: Verify Infrastructure Is Wired
TOCTOU prevention, distributed locking, idempotency keys, race condition detection for Node.js and serverless environments.
ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration
Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks before release or PR.
Dockerfile best practices, multi-stage builds, docker-compose, container networking, volume management, and image optimization.
Email delivery infrastructure - DNS authentication (SPF/DKIM/DMARC), subdomain isolation, provider abstraction, template systems, bounce handling, warmup strategy, and deliverability monitoring.
Cloud Run deployment, BigQuery optimization, Pub/Sub patterns, IAM best practices
Web change monitoring - track changes on pages, detect updates, changelog diffs
Pod design patterns, sidecar containers, init containers, config management, rolling updates, and resource limits.
n8n otomasyon workflow'lari. Webhook, cron trigger, API entegrasyon, CI/CD otomasyon.