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.
28,750 skills from 1,176 repositories · updated 12 minutes ago
1,640 skills
- go-lintingcxuu/golang-skillsUse when setting up linting for a Go project, configuring golangci-lint, or adding Go checks to a CI/CD pipeline. Also use when starting a new Go project and deciding which linters to enable, even if the user only asks about "code quality" or "static analysis" without mentioning specific linter names. Does not cover code review process (see go-code-review).DevOpsNo warnings★ 181↓ —
- aws-solution-architectalirezarezvani/claude-code-skill-factoryExpert AWS solution architecture for startups focusing on serverless, scalable, and cost-effective cloud infrastructure with modern DevOps practices and infrastructure-as-codeDevOpsNo warnings★ 893↓ —
- iac-reviewerproflead/codex-skills-libraryReview infrastructure-as-code changes for safety and correctness. Use when a mid-level developer needs a second look on IaC.DevOpsNo warnings★ 161↓ —
- observability-setupproflead/codex-skills-librarySet up metrics, logs, and traces for a service. Use when a mid-level developer needs basic observability coverage.DevOpsNo warnings★ 161↓ —
- lamindbBioTender-max/awesome-bio-agent-skillsThis skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.DevOpsNo warnings★ 206↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- building-clisancoleman/ai-design-componentsBuild professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.DevOpsNo warnings★ 525↓ —
- 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★ 334↓ —
- managing-dnsancoleman/ai-design-componentsManage DNS records, TTL strategies, and DNS-as-code automation for infrastructure. Use when configuring domain resolution, automating DNS from Kubernetes with external-dns, setting up DNS-based load balancing, or troubleshooting propagation issues across cloud providers (Route53, Cloud DNS, Azure DNS, Cloudflare).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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- institutional-flow-trackernicepkg/ai-workflowUse this skill to track institutional investor ownership changes and portfolio flows using 13F filings data. Analyzes hedge funds, mutual funds, and other institutional holders to identify stocks with significant smart money accumulation or distribution. Helps discover stocks before major moves by following where sophisticated investors are deploying capital.DevOpsNo warnings★ 288↓ —