Skill-uri pentru agenți pe care le poți verifica înainte să le instalezi
Skill-uri SKILL.md publice din depozite GitHub cu peste 50 de stele și de la producători, scanate și evaluate. Instalează oricare dintre ele cu o singură comandă în Claude Code, Cursor, Codex, GitHub Copilot sau Gemini CLI.
19.942 de skill-uri din 718 depozite · actualizat acum 18 minute
1.273 de skill-uri
- testdriver:enterprisetestdriverai/testdriveraiSelf-hosted enterprise deployments with assisted setup and dedicated supportDevOpsFără avertismente★ 465↓ —
- testdriver:aws-setuptestdriverai/testdriveraiDeploy TestDriver on your AWS infrastructure using CloudFormationDevOpsFără avertismente★ 465↓ —
- testdriver:aws-setuptestdriverai/testdriveraiDeploy TestDriver on your AWS infrastructure using CloudFormationDevOpsFără avertismente★ 465↓ —
- 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.DevOpsFără avertismente★ 370↓ —
- 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.DevOpsFără avertismente★ 370↓ —
- 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-codeDevOpsFără avertismente★ 893↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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).DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsVerifică întâi★ 370↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- 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.DevOpsFără avertismente★ 524↓ —
- terraform-azureBagelHole/DevOps-Security-Agent-SkillsProvision Azure infrastructure with Terraform. Configure providers, manage state, and deploy resources. Use when implementing IaC for Azure.DevOpsVerifică întâi★ 1,2 K↓ —