Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 79 566 files from 1 758 authors, of which 61 913 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Set up metrics collection and visualization with Prometheus and Grafana. Configure scrape targets, create PromQL queries, build dashboards, and implement alerting. Use when implementing monitoring, metrics collection, or visualization for applications and infrastructure.
Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs. Use when implementing distributed tracing, service-level troubleshooting, or vendor-neutral observability.
Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.
Implement GitOps with ArgoCD for declarative Kubernetes deployments. Configure applications, manage sync policies, implement progressive delivery, and automate deployments from Git repositories. Use when implementing GitOps workflows or continuous deployment to Kubernetes.
Create, manage, and deploy Helm charts for Kubernetes package management. Build reusable chart templates, manage releases, configure values, and use Helm repositories. Use when packaging Kubernetes applications or managing K8s deployments with Helm.
Customize Kubernetes manifests without templating using Kustomize. Create base configurations with environment overlays, manage configuration variants, and patch resources declaratively. Use when managing Kubernetes configurations across multiple environments without Helm.
Deploy, scale, and manage Kubernetes workloads. Create deployments, services, and configurations, manage cluster resources, troubleshoot pods, and implement production-ready Kubernetes patterns. Use when working with Kubernetes clusters, K8s deployments, or container orchestration.
Build internal developer platforms (IDPs) with self-service infrastructure, golden paths, and developer portals using Backstage, Crossplane, and score.
Manage Red Hat OpenShift clusters and deployments. Configure projects, routes, builds, and deploy applications using OpenShift-specific features. Use when working with OpenShift Container Platform or OKD for enterprise Kubernetes.
Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.
Configure zero-downtime deployment strategies including blue-green, canary, and rolling deployments. Implement traffic shifting, health checks, and rollback procedures. Use when implementing production deployment strategies or zero-downtime releases.
Implement feature flags for progressive feature rollout using LaunchDarkly, Unleash, or custom solutions. Control feature visibility, perform A/B testing, and enable trunk-based development. Use when implementing gradual rollouts, feature toggles, or experimentation platforms.
Implement Git branching strategies, PR workflows, and release management patterns. Configure GitFlow, trunk-based development, or GitHub Flow for team collaboration. Use when establishing version control workflows or improving development team collaboration.
Automate versioning and changelog generation using semantic versioning principles. Configure release automation, version bumping, and changelog tools. Use when implementing version management or automating release processes.
Deploy containers on ECS and Fargate. Configure task definitions, services, and load balancing. Use when running containerized workloads on AWS.
Manage EC2 instances, AMIs, and auto-scaling groups. Configure security groups, key pairs, and instance types. Use when deploying compute resources on AWS.
Reduce AWS spend with rightsizing, autoscaling, commitment planning, and storage lifecycle policies. Use when running FinOps reviews, lowering cloud bills, or improving cost-per-request metrics.
Manage IAM users, roles, and policies. Implement least-privilege access and security best practices. Use when configuring AWS identity and access management.
Build and deploy serverless functions on AWS Lambda. Configure triggers, manage permissions, and optimize performance. Use when implementing serverless applications.
Provision and manage RDS databases. Configure backups, replication, and security. Use when deploying managed relational databases on AWS.
Configure S3 buckets, policies, and lifecycle rules. Implement versioning, replication, and security. Use when managing object storage on AWS.
Design and implement VPCs and networking. Configure subnets, route tables, and security groups. Use when setting up AWS network infrastructure.
Deploy and manage Azure Kubernetes Service clusters. Configure node pools, networking, and integrations. Use when running Kubernetes workloads on Azure.
Deploy AWS resources with CloudFormation templates. Create stacks, use nested stacks, and implement drift detection. Use when deploying AWS-native IaC.
Provision AWS infrastructure with Terraform. Create modules, manage state, and implement IaC best practices. Use when deploying AWS resources declaratively.
Configure Azure VNets, NSGs, and Azure Firewall. Implement hub-spoke topology and private endpoints. Use when designing Azure network infrastructure.
Build serverless applications on Azure Functions. Configure triggers, bindings, and deployment. Use when implementing serverless workloads on Azure.
Provision Azure SQL Database and Cosmos DB. Configure security, backups, and replication. Use when deploying managed databases on Azure.
Manage Azure Virtual Machines and scale sets. Configure availability sets and managed disks. Use when deploying compute resources on Azure.
Provision Azure infrastructure with Terraform. Configure providers, manage state, and deploy resources. Use when implementing IaC for Azure.
Deploy and manage Google Kubernetes Engine clusters. Configure node pools, networking, and workload identity. Use when running Kubernetes on GCP.
Provision Cloud SQL and Spanner databases. Configure high availability, backups, and security. Use when deploying managed databases on GCP.
Deploy serverless functions on Google Cloud Functions. Configure triggers and manage deployments. Use when implementing serverless workloads on GCP.
Manage Compute Engine instances and instance templates. Configure managed instance groups and preemptible VMs. Use when deploying compute resources on GCP.
Configure VPCs, firewall rules, and Cloud NAT. Implement shared VPC and private service connect. Use when designing GCP network infrastructure.
Provision GCP infrastructure with Terraform. Configure providers and deploy Google Cloud resources. Use when implementing IaC for GCP.
Deploy static sites and full-stack apps on Cloudflare Pages with previews, functions, and custom domains.
Build and deploy edge functions with Cloudflare Workers and Wrangler. Use for APIs, cron jobs, and edge middleware.
Manage Cloudflare R2 buckets, lifecycle, and signed URLs. Use for low-egress object storage and media delivery.
Protect internal apps with Cloudflare Access, device posture, and Zero Trust policies.
Administer MongoDB databases. Configure replica sets, sharding, and backups. Use when managing MongoDB deployments.
Implement database backup strategies. Configure automated backups, retention, and recovery testing. Use when designing backup and recovery procedures.
Administer MySQL/MariaDB databases. Configure replication and optimize performance. Use when managing MySQL deployments.
Operate MySQL-compatible databases on PlanetScale with branching workflows, safe migrations, and production rollouts.
Administer PostgreSQL databases. Configure replication, backups, and performance tuning. Use when managing PostgreSQL deployments.
Configure Redis for caching and data storage. Set up clustering, persistence, and Sentinel. Use when implementing Redis caching or queues.
Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.
Migrate from Terraform to OpenTofu with state compatibility, provider registry setup, and CI/CD pipeline updates. Use when adopting the open-source Terraform fork or evaluating license-free IaC.
Set up and manage SSO, SCIM provisioning, and MFA for startup teams using Google Workspace, Okta, or Azure AD. Use when centralizing authentication, onboarding SSO, or meeting compliance requirements.
Manage and secure company devices with MDM solutions — enroll macOS, Windows, iOS, and Android devices, enforce security policies, and automate software deployment. Use when setting up device management for a growing team.
Practical IT troubleshooting playbooks for small teams without dedicated IT staff.
Audit and harden your SaaS tool stack — enforce SSO, review OAuth grants, manage shadow IT, and secure admin accounts across Slack, GitHub, Google Workspace, and AWS. Use when tightening security across company SaaS tools.
Operate GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.
Auto-scale LLM inference clusters on Kubernetes using KEDA, custom GPU metrics, and horizontal pod autoscaling. Handle traffic spikes, implement queue-based scaling, and optimize cost with spot instances for AI workloads.
Configure a Mac mini as a reliable local LLM server with remote access, observability, and power-safe operation. Use when building an always-on private AI inference server on Apple Silicon.
Design secure, multi-tenant LLM hosting platforms with tenant isolation, quotas, billing attribution, noisy-neighbor protection, and per-tenant policy controls.
Set up infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and distributed training with DeepSpeed or FSDP. Covers dataset prep, training runs, and model export.
Run local LLM workloads with Ollama, Open WebUI, and GPU-aware tuning for private development environments. Use when setting up private inference, local AI dev environments, or air-gapped LLM deployments.
Set up OpenClaw locally and run it reliably on a Mac mini for private, always-on local agent workflows.
Harden OpenClaw self-hosted environments with baseline host controls, auth tightening, secret handling, network segmentation, and safe update/rollback workflows. Use when deploying OpenClaw in home labs, startups, or production-like local AI infrastructure.
Claude Skills — questions
Answers built from the skills we actually parsed.