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
Assess and upgrade Azure workloads between plans, tiers, or SKUs, or modernize Azure SDK dependencies in source code. WHEN: upgrade Consumption to Flex Consumption, upgrade Azure Functions plan, change hosting plan, function app SKU, migrate App Service to Container Apps, modernize legacy Azure Java SDKs (com.microsoft.azure to com.azure), migrate Azure Cache for Redis (ACR/ACRE) to Azure Managed Redis (AMR).
Deploy, evaluate, fine-tune, and manage Foundry agents end-to-end with azd: hosted agent scaffold/run/deploy, prompt agent create, batch eval, continuous eval, prompt optimizer, Agent Optimizer scaffold, agent.yaml, dataset curation from traces, model fine-tuning (SFT/DPO/RFT). USE FOR: azd ai agent, azd provision/deploy, deploy agent, hosted agent, create agent, add tool to agent, invoke agent, evaluate agent, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, agent optimizer, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, AI Services, create Foundry resource, provision, knowledge index, customize deployment, onboard, availability, fine-tune, SFT, DPO, RFT, training-data, grader, distillation, fine-tuned model, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: \"Flask App Service\", \"Django App Service\", \"FastAPI App Service\", \"deploy Python to App Service\". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.
Access and test Vercel deployments protected by Vercel Authentication, SSO, or Deployment Protection. Use when curl, agent-browser, Playwright, or another automated request reaches a Vercel login or protection page; when a protected preview or production URL returns 401 or 403; when TRUSTED_SOURCES_ENVIRONMENT_MISMATCH appears; or when choosing between `vercel curl` and the `x-vercel-trusted-oidc-idp-token` header.
Vercel deployment and CI/CD expert guidance. Use when deploying, promoting, rolling back, inspecting deployments, building with --prebuilt, or configuring CI workflow files for Vercel.
Vercel Marketplace expert guidance — discovering, installing, and managing third-party integrations via the `vercel integration` CLI. Use when building any app that needs an external capability without a dedicated skill — commerce (stores, storefronts, selling products), payments (checkout, subscriptions, billing), observability/monitoring, messaging/email, search, or CMS — or when discovering, installing, or managing integrations.
Guide for building, configuring, and deploying microfrontends on Vercel. Use this skill when the user mentions microfrontends, multi-zones, splitting an app across teams, independent deployments, cross-app routing, incremental migration, composing multiple frontends under one domain, microfrontends.json, @vercel/microfrontends, the microfrontends local proxy, or path-based routing between Vercel projects. Also use when the user asks about shared layouts across projects, navigation between microfrontends, fallback environments, asset prefixes, or feature flag controlled routing.
Deploy, manage, and develop projects on Vercel from the command line
Vercel CLI expert guidance. Use when deploying, managing environment variables, linking projects, viewing logs, querying metrics, managing domains, or interacting with the Vercel platform from the command line.
Query and manage Salesforce CRM data via the Salesforce CLI (`sf`). Run SOQL/SOSL queries, inspect object schemas, create/update/delete records, bulk import/export, execute Apex, deploy metadata, and make raw REST API calls.
Autonomous multi-agent task orchestration with dependency analysis, parallel tmux/Codex execution, and self-healing heartbeat monitoring. Use for large projects with multiple issues/tasks that need coordinated parallel execution.
DevOps knowledge reference covering Git workflows, testing strategies, DevSecOps, release pipeline orchestration (release.yml, multi-arch images, cosign integration), CI/CD pipelines, database management, observability, and performance optimization. Use when working with Git, CI/CD, release pipelines, ghcr image publishing, testing, monitoring, or infrastructure automation.
Architecture knowledge reference covering API design, security architecture, cloud-native patterns, caching strategies, message queues, and data security. Use when designing system architecture, APIs, or cloud-native infrastructure.
Data engineering knowledge reference covering Airflow, Dagster, Kafka Streams, Flink, dbt, and data quality patterns. Use when building data pipelines, ETL workflows, stream processing, or data quality checks.
云原生与软件供应链安全防御。容器/K8s 加固、Service Mesh、CI/CD 安全、SLSA/SBOM/Sigstore、云 IAM、Secrets 管理、IaC 安全。Use when hardening Kubernetes clusters, auditing CI/CD pipelines, implementing supply chain security, managing cloud IAM, or reviewing IaC code.
Cloud-native infrastructure knowledge reference covering Kubernetes, Helm, Kustomize, Operators, CRDs, GitOps (ArgoCD, Flux), and IaC (Terraform, Pulumi, CDK). Use when provisioning infrastructure, managing clusters, or working with GitOps workflows.
Run TestDriver tests in CI/CD with parallel execution and cross-platform support
Deploy TestDriver on your AWS infrastructure using CloudFormation
Self-hosted enterprise deployments with assisted setup and dedicated support
Per-test JSON result files with metadata, versions, and infrastructure details
Run TestDriver tests in CI/CD with parallel execution and cross-platform support
Databricks AI Runtime (`air`) CLI — the command-line tool for submitting and managing GPU training workloads on Databricks serverless compute. Use for: running `air` workloads, custom Docker image setup, environment configuration, and troubleshooting `air` jobs.
Create, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.
Execute code and manage compute on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete clusters and SQL warehouses.
Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage AI Gateway rate limits; discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concrete fixes for the serverless Spark Connect architecture and guides the full migration. Not for classic DBR version upgrades or cluster configuration changes within classic compute.
Unity Catalog governance, access control, and observability. Use to grant or revoke access (GRANT/REVOKE), reason about the privilege model and ownership, set up row-level security and column masks, create external locations and storage credentials, define catalogs/schemas/tables/volumes, answer \"who can read this table\", and query system tables (audit, lineage, billing) or work with volume files in /Volumes/.
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Debug Docker containers, fix Dockerfile issues, optimize images, and troubleshoot docker-compose. Use when having Docker problems, container issues, or optimizing Docker builds.
Production incident response automation. Reads logs, checks recent deploys, identifies root cause, suggests fixes, drafts incident comms, creates post-mortem templates. Severity classification (SEV1-4), escalation paths, status page updates. Generates incident-report.md with timeline, root cause, impact assessment, remediation steps, and prevention measures.
Analyze pipeline health, identify stalled deals, predict close probability, and suggest actions to move deals forward. Improves forecast accuracy and prevents revenue leakage. Use when deals get stuck or forecast accuracy is poor.
Generates comprehensive operational runbooks for any system or process. Reads codebase, infrastructure config, and deployment scripts to produce structured runbook.md files formatted for on-call engineers. Use when you need operations documentation, incident response guides, deployment procedures, or disaster recovery plans.
>- This skill should be used when the user asks to "manage UniFi devices", "configure UniFi networks", "create a VLAN", "provision an SSID", "create firewall rules", "reorder firewall policies", "create a NAT rule", "set up port forwarding", "configure masquerade NAT", "add DNS records", "manage traffic matching lists", "create DHCP reservations", "list DHCP reservations", "block a client", "kick a client", "find a client by IP or name", "adopt a device", "restart a UniFi device", "cycle a PoE port", "upgrade device firmware", "run a speed test", "stream UniFi events", "watch real-time events", "query UniFi stats", "analyze DPI traffic", "enable DPI", "generate hotspot vouchers", "show network topology", "audit firewall policies", "create a backup", "call the raw UniFi API", "check network health", or any task involving UniFi network infrastructure management via the unifly CLI. Also triggers on mentions of unifly, UniFi, Ubiquiti, UDM, UCG, USG, USW, UAP, UXG, UNVR, U6, U7, or UniFi controller operations.
> Design or audit the uncertainty-quantification, out-of-distribution (OOD) detection, and selective-prediction layer of a medical-imaging model framed for deployment — so a clinical-use claim carries calibrated per-case uncertainty (MC-dropout / deep ensemble / conformal / Bayesian), an OOD guard validated on a held-out OOD set, an abstention rule at a pre-specified operating point, and uncertainty checked under distribution shift. Emits an uncertainty manifest and a deterministic gate that flags a deployment claim built on point predictions, conformal intervals with unmeasured coverage, and an OOD claim with no held-out OOD data. Integrates MAPIE / captum / pretrained OOD scorers; it does not reimplement them and never runs a model on real patient data.
Use when the user asks about fees, slippage, maker vs taker, post-only orders, fee tiers, fee optimization, why my strategy is losing more than backtest, builder code fee, effective spread, order pricing, or wants to lower trading costs on a Hyperliquid Freqtrade deployment. Also use proactively when the user designs a high-turnover strategy (5m or faster, tight ROI < 0.5%) — fees often dominate edge there.
Use when the user asks "what's hot", "what's moving", "any alpha", "show me squeeze setups", "what's the setup on ETH", "is SOL coiled", "should I deploy NEAR" or any market-scan / single-pair-drilldown question. Surfaces Superior Trade's live multi-bucket scoring across Hyperliquid alts + HIP-3 (stocks/indices/commodities/FX) — Squeeze fuel, Stealth accumulation, Coiled spring, Basis flipping. The engine picks the strongest timeframe (15m/1h/4h/24h) per pair per bucket; you don't pick one. Pairs in to the existing strategy → backtest → deployment workflow at api.superior.trade.
Use when creating, validating, backtesting, deploying, sizing, or troubleshooting Aerodrome/Base spot trading strategies through the Superior Trade API, especially Freqtrade configs using exchange.name "aerodrome", AERO/USDC or CHECK/USDC pairs, AMM market swaps, wallet/gas balance checks, no-orderbook pricing, or Aerodrome live deployment safety.
Structured pre-trade thesis builder — bull/bear cases, invalidation criteria, and sizing rationale before any live deployment. Read this page when a user proposes a trade idea, says 'should I trade X', asks for a bull/bear case, wants a pre-trade analysis, or before the agent deploys a new strategy live for the first time.
Onboard, fund, place immediate orders, withdraw, proxy signed Lighter transactions, and deploy Lighter Nautilus strategies through Superior Trade.
Discover Polymarket markets, place single immediate market orders, run v3 filled-data backtests, and plan/start live Nautilus deployments through Superior Trade's managed cloud.
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.
Handles containerization, CI/CD pipelines, and deployment setup.
> Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.