Agent skills you can check before you install
Public SKILL.md skills from GitHub repositories with 50+ stars and from the vendors themselves, scanned and scored. Install any of them with one command in Claude Code, Cursor, Codex, GitHub Copilot or Gemini CLI.
46,700 skills from 2,243 repositories · updated 3 hours ago
2,394 skills
- writing-infrastructure-codeancoleman/ai-design-componentsManaging cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.DevOpsNo warnings★ 525↓ —
- model-servingancoleman/ai-design-componentsLLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.DevOpsNo warnings★ 525↓ —
- deploying-on-gcpancoleman/ai-design-componentsImplement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.DevOpsNo warnings★ 525↓ —
- optimizing-costsancoleman/ai-design-componentsOptimize cloud infrastructure costs through FinOps practices, commitment discounts, right-sizing, and automated cost management. Use when reducing cloud spend, implementing budget controls, or establishing cost visibility across AWS, Azure, GCP, and Kubernetes environments.DevOpsNo warnings★ 525↓ —
- aws-clichaterm/terminal-skillsAWS CLI 操作DevOpsNo warnings★ 59↓ —
- using-timeseries-databasesancoleman/ai-design-componentsTime-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.DevOpsNo warnings★ 525↓ —
- resource-taggingancoleman/ai-design-componentsApply and enforce cloud resource tagging strategies across AWS, Azure, GCP, and Kubernetes for cost allocation, ownership tracking, compliance, and automation. Use when implementing cloud governance, optimizing costs, or automating infrastructure management.DevOpsNo warnings★ 525↓ —
- dspy-debugging-observabilityOmidZamani/dspy-skillsUse for debugging DSPy programs, inspect_history, tracing LLM calls, custom callbacks, observability, monitoring, and cost tracking.DevOpsNo warnings★ 124↓ —
- managing-secretsancoleman/ai-design-componentsManaging secrets (API keys, database credentials, certificates) with Vault, cloud providers, and Kubernetes. Use when storing sensitive data, rotating credentials, syncing secrets to Kubernetes, implementing dynamic secrets, or scanning code for leaked secrets.DevOpsNo warnings★ 525↓ —
- dspy-production-deploymentOmidZamani/dspy-skillsUse for deploying DSPy with save/load, configure_cache, restrict_pickle, track_usage, async execution, streaming, and production runtime controls.DevOpsNo warnings★ 124↓ —
- hunt-cloudEncod3d-Sec/TORCHCloud attack hunting for AWS / Azure / GCP - credential discovery, metadata SSRF, IAM privesc, service enumeration, persistence. Scope + billing aware. Wiki-first, FIND schema output.DevOpsReview first★ 335↓ —
- deploying-on-azureancoleman/ai-design-componentsDesign and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.DevOpsNo warnings★ 525↓ —
- aws-sst-developmentzxkane/aws-skillsSST v4 (Ion) expert for managing AWS resources as code with the Pulumi-backed framework. Use when writing or editing sst.config.ts, building infra/ modules (sst.aws.Function/Bucket/Dynamo/Cron/Service/Router, sst.Secret, sst.Linkable, raw aws.* Pulumi resources), wiring resource links, scoping IAM, or running sst deploy/dev/diff/remove. Essential when the user mentions SST, sst.config.ts, $config, $transform, $interpolate, sst.aws.*, sst.Secret, Pulumi/Ion, "sst deploy", a failed SST deploy (ConflictException on a resource-type change, "Identifier '__filename' has already been declared", MalformedPolicyDocument on an Output<T>), or wants to scaffold/troubleshoot AWS infrastructure with SST. Also use when a request to "deploy my AWS stack" or "add a Lambda/bucket/table" is made in a repo that already contains an sst.config.ts (using $config) or an sst dependency. Do NOT use when the task is primarily AWS CDK, Terraform, raw CloudFormation, or SAM with no SST present — those have their own tooling.DevOpsReview first★ 371↓ —
- implementing-mlopsancoleman/ai-design-componentsStrategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.DevOpsNo warnings★ 525↓ —
- implementing-observabilityancoleman/ai-design-componentsMonitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.DevOpsNo warnings★ 525↓ —
- implementing-gitopsancoleman/ai-design-componentsImplement GitOps continuous delivery for Kubernetes using ArgoCD or Flux. Use for automated deployments with Git as single source of truth, pull-based delivery, drift detection, multi-cluster management, and progressive rollouts.DevOpsNo warnings★ 525↓ —
- operating-kubernetesancoleman/ai-design-componentsOperating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Use when deploying workloads to Kubernetes, configuring cluster resources, implementing security policies, or troubleshooting operational issues.DevOpsNo warnings★ 525↓ —
- deploying-applicationsancoleman/ai-design-componentsDeployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.DevOpsNo warnings★ 525↓ —
- writing-github-actionsancoleman/ai-design-componentsWrite GitHub Actions workflows with proper syntax, reusable workflows, composite actions, matrix builds, caching, and security best practices. Use when creating CI/CD workflows for GitHub-hosted projects or automating GitHub repository tasks.DevOpsNo warnings★ 525↓ —
- ci-runner-auditapache/magpieRead-only audit of GitHub Actions runner compatibility for one repository, a repository set, one Apache project, or the full Apache org. Finds obsolete GitHub-hosted runner labels and macOS runner/tool architecture mismatches. Produces TSV evidence; never edits workflows, opens PRs, or posts comments.DevOpsReview first★ 127↓ —
- dspy-finetune-bootstrapOmidZamani/dspy-skillsUse for BootstrapFinetune, fine-tuning DSPy models, teacher-student distillation, weight optimization, and lower-cost deployment.DevOpsNo warnings★ 124↓ —
- ansiblechaterm/terminal-skillsAnsible 自动化运维DevOpsNo warnings★ 59↓ —
- diagnoseavibebuilder/claude-primeInvestigate unexpected behavior and mysterious bugs. Use when the cause of a problem is unknown and the user needs to understand WHY something is happening — symptoms like: sudden unexplained changes in metrics or behavior, works locally but not in staging/production, inconsistent or intermittent failures, correct code producing wrong results, operations succeeding but having no effect, environment-specific failures, duplicate executions, stale data, or any "why did this change?" or "why is this happening?" situation. Covers infrastructure anomalies (cache hit rates dropping, latency spikes, queue behavior shifts) as well as code bugs. The key signal is confusion about root cause, not a request to implement a known fix. Do NOT use for feature requests, known fixes, planning, or documentation tasks.DevOpsNo warnings★ 119↓ —
- configmap-secretchaterm/terminal-skillsKubernetes ConfigMap 与 SecretDevOpsNo warnings★ 59↓ —