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.
29,591 skills from 1,220 repositories · updated 6 minutes ago
1,674 skills
- frappe-ops-website-deployImpertio-Studio/Frappe_Claude_Skill_PackageDeploy HTML/CSS websites to ERPNext/Frappe (v15/v16) as Web Pages via the REST API. Use this skill whenever a user wants to host a website on ERPNext, deploy HTML mockups to Frappe, create Web Pages programmatically, configure Website Settings, or integrate Frappe's Discussion system as a forum. Also use when the user mentions "website on ERPNext", "Web Page API", "Page Builder", "Web Template", or wants to serve custom HTML from Frappe. Covers: Web Pages with Page Builder, custom Web Templates, Website Settings (navbar, footer), CSS management, Frappe Discussion integration, and deployment scripting.DevOpsNo warnings★ 195↓ —
- azure-cloud-architectborghei/Claude-SkillsDesign, review, and validate Azure cloud architectures. Use when choosing Azure compute, storage, networking, or identity services, or applying the Azure Well-Architected Framework to a workload.DevOpsNo warnings★ 971↓ —
- aws-cdk-developmentzxkane/aws-skillsAWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment workflows.DevOpsNo warnings★ 371↓ —
- aws-agentic-aizxkane/aws-skillsAWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.DevOpsNo warnings★ 371↓ —
- astroericrisco/rsc-harnessUse when building a content-driven or marketing site with Astro 6: static-first pages, islands and partial hydration, content collections, server islands, per-route on-demand rendering, deploy adapters, and Astro 5→6 migration. NOT app-router React with server actions and heavy client interactivity (that is `nextjs`).DevOpsReview first★ 255↓ —
- config-hardeningproflead/codex-skills-libraryHarden configuration and defaults for safer deployment. Use when a mid-level developer needs to reduce misconfig risks.DevOpsNo warnings★ 161↓ —
- zero-downtime-migrationproflead/codex-skills-libraryPlan a zero-downtime migration for data or infrastructure. Use when a senior developer needs a safe migration strategy.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↓ —
- 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↓ —
- rowanBioTender-max/awesome-bio-agent-skillsRowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.DevOpsNo warnings★ 206↓ —
- dashmotioncsthink/dashmotionCreate dark-themed, animated technical diagrams as self-contained HTML+SVG files — flowcharts whose connectors visibly flow, and architecture diagrams where requests travel as light dots through the system (Diagrid/Temporal landing-page style). Use this skill whenever the user asks for a flowchart, workflow, pipeline, process diagram, state machine, system architecture, infrastructure, cloud, microservices, or network topology diagram — and especially when they mention "animated", "flowing", "dynamic", "alive", "GIF-like", or want a diagram for a landing page, README, docs, or product demo. Also use it to convert Mermaid source (a mermaid code block or .mmd file) into an animated diagram — "animate this mermaid", "make this flowchart move". Prefer this over static diagram output whenever the diagram represents anything that moves: requests, events, data, jobs, messages, or control flow.DevOpsNo warnings★ 180↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —
- 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↓ —