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 2 068 updated this month 579 from vendors
Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications.
Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks before release or PR.
Docker and Docker Compose patterns for local development, container security, networking, volume strategies, and multi-service orchestration.
Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.
Use when deploying to production environment
Manage Alibaba Cloud Container Registry (ACR) Enterprise Edition using the @alicloud/cr20181201 TypeScript SDK. Use when working with container image registries on Alibaba Cloud, including instance management, namespaces, image repositories, image tags, build rules, image synchronization, security scanning, delivery chains, Helm charts, artifact lifecycle, and event notifications. Covers all 115 APIs of the CR 20181201 version.
Docker and Kubernetes patterns. Triggers on: Dockerfile, docker-compose, kubernetes, k8s, helm, pod, deployment, service, ingress, container, image.
Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s manifests, GitHub Actions workflows, package.json, filter data.
| Automatically navigates to Lovable.dev and submits deployment prompts. Runs verification tests based on configuration. Auto-deploys after git push when enabled.
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Network architecture, troubleshooting, and infrastructure patterns. Use when designing network topologies, debugging connectivity issues, configuring load balancers, DNS, or implementing network security.
This skill should be used when deploying a Docusaurus site to GitHub Pages. It automates the configuration, building, and deployment process, handling GitHub Pages setup, environment configuration, and CI/CD automation. Includes local validation before GitHub Actions triggering.
| Design production-grade Helm charts through architectural reasoning rather than pattern retrieval. Activate when designing new Helm charts for Kubernetes deployments, evaluating chart architecture, making decisions about component packaging, or reviewing charts for extensibility and maintainability. Guides decision-making about dependencies, lifecycle hooks, configuration surface, and multi-environment deployment through context-specific reasoning rather than generic best practices.
Guide for creating new skills in Kai's personal AI infrastructure. Use when user wants to create, update, or structure a new skill that extends capabilities with specialized knowledge, workflows, or tool integrations. Follows both Anthropic skill standards and PAI-specific patterns.
Infrastructure as Code patterns for deploying Guts nodes using Terraform, Docker, and Kubernetes
Comprehensive FastAPI development skill covering REST API creation, routing, request/response handling, validation, authentication, database integration, middleware, and deployment. Use when working with FastAPI projects, building APIs, implementing CRUD operations, setting up authentication/authorization, integrating databases (SQL/NoSQL), adding middleware, handling WebSockets, or deploying FastAPI applications. Triggered by requests involving .py files with FastAPI code, API endpoint creation, Pydantic models, or FastAPI-specific features.
Knowledge of the Vercel deployment pipeline, hybrid build scripts, and environment configuration.
Code quality standards, linting rules, and CI/CD principles.
Operates and debugs the local stack (API/worker/frontend); focuses on observability, logs, and safe automation.
Expert guidance for Google Kubernetes Engine (GKE) operations including cluster management, workload deployment, scaling, monitoring, troubleshooting, and optimization. Use when working with GKE clusters, Kubernetes deployments on GCP, container orchestration, or when users need help with kubectl commands, GKE networking, autoscaling, workload identity, or GKE-specific features like Autopilot, Binary Authorization, or Config Sync.
> API specification linting and security validation using Stoplight's Spectral with support for OpenAPI, AsyncAPI, and Arazzo specifications. Validates API definitions against security best OpenAPI/AsyncAPI specifications for security issues and design flaws, (2) Enforcing API design standards and governance policies across API portfolios, (3) Creating custom security rules for API specifications in CI/CD pipelines, (4) Detecting authentication, authorization, and data exposure issues in API definitions, (5) Ensuring API specifications comply with organizational security standards and regulatory requirements.
> Container vulnerability scanning and dependency risk assessment using Grype with CVSS severity images and filesystems for known vulnerabilities, (2) Integrating vulnerability scanning into CI/CD pipelines with severity thresholds, (3) Analyzing SBOMs (Syft, SPDX, CycloneDX) for security risks, (4) Prioritizing remediation based on threat metrics (CVSS, EPSS, KEV), (5) Generating vulnerability reports in multiple formats (JSON, SARIF, CycloneDX) for security toolchain integration.
> Dockerfile security linting and best practice validation using Hadolint with 100+ built-in misconfigurations and anti-patterns, (2) Enforcing container image security best practices in CI/CD pipelines, (3) Detecting hardcoded secrets and credentials in container builds, (4) Validating compliance with CIS Docker Benchmark requirements, (5) Integrating shift-left container security into developer workflows, (6) Providing remediation guidance for insecure Dockerfile instructions.
> Dynamic application security testing (DAST) using OWASP ZAP (Zed Attack Proxy) with passive and active scanning, applications and APIs, (2) Detecting vulnerabilities like XSS, SQL injection, and authentication flaws in deployed applications, (3) Automating security scans in CI/CD pipelines with Docker containers, (4) Conducting authenticated testing with session management, (5) Generating security reports with OWASP and CWE mappings for compliance.
> Generic detection rule creation and management using Sigma, the universal SIEM rule format. Sigma provides vendor-agnostic detection logic for log analysis across multiple SIEM platforms. SIEM platforms (Splunk, Elastic, QRadar, Sentinel), (3) Threat hunting with standardized detection patterns, (4) Building detection-as-code pipelines, (5) Mapping detections to MITRE ATT&CK tactics, (6) Implementing compliance-based monitoring rules.
> Infrastructure as Code (IaC) security scanning using Checkov with 750+ built-in policies for Terraform, misconfigurations and compliance violations, (2) Validating cloud infrastructure against CIS, PCI-DSS, HIPAA, and SOC2 benchmarks, (3) Detecting secrets and hardcoded credentials in IaC, (4) Implementing policy-as-code in CI/CD pipelines, (5) Generating compliance reports with remediation guidance for cloud security posture management.
> Endpoint visibility, digital forensics, and incident response using Velociraptor (1) Conducting forensic investigations across multiple endpoints, (2) Hunting for indicators of compromise or suspicious activities, (3) Collecting endpoint telemetry and artifacts for incident analysis, (4) Performing live response and evidence preservation, (5) Monitoring endpoints for security events, (6) Creating custom forensic artifacts for specific threat scenarios.
> Policy-as-code enforcement and compliance validation using Open Policy Agent (OPA). (2) Validating Kubernetes admission control policies, (3) Implementing policy-as-code for compliance frameworks (SOC2, PCI-DSS, GDPR, HIPAA), (4) Testing and evaluating OPA Rego policies, (5) Integrating policy checks into CI/CD pipelines, (6) Auditing configuration drift against organizational security standards, (7) Implementing least-privilege access controls.
> Python-based threat modeling using pytm library for programmatic STRIDE analysis, (1) Creating threat models programmatically using Python code, (2) Generating data flow diagrams (DFDs) with automatic STRIDE threat identification, (3) Integrating threat modeling into CI/CD pipelines and shift-left security practices, (4) Analyzing system architecture for security threats across trust boundaries, (5) Producing threat reports with STRIDE categories and mitigation recommendations, (6) Maintaining threat models as code for version control and automation.