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.
41,606 skills from 1,950 repositories · updated 3 minutes ago
2,149 skills
- arize-migrate-langsmithArize-ai/arize-skillsMigrates LLM observability from LangSmith into Arize AX — historical trace import via OTLP (LS outputs→output.value, session/thread→session.id), datasets, and Prompt Hub prompts (latest or versions). Use when migrating from LangSmith to Arize, importing LangSmith runs into AX, or replacing LangSmith with Arize.DevOpsNo warnings★ 66↓ —
- docker-developmentborghei/Claude-SkillsThis skill should be used when the user asks to "analyze a Dockerfile", "optimize Docker layers", "validate docker-compose", "check container best practices", or "audit Docker configurations".DevOpsNo warnings★ 971↓ —
- arize-migrate-langfuseArize-ai/arize-skillsMigrates LLM observability from Langfuse into Arize AX — historical trace import via OTLP (LF output→output.value, sessionId→session.id), datasets, and prompts (latest or versions + labels). Use when migrating from Langfuse to Arize or importing Langfuse observations into AX.DevOpsNo warnings★ 66↓ —
- runbook-generatorborghei/Claude-SkillsGenerate operational runbooks from codebase analysis covering deployment, incident response, scaling, and monitoring, with copy-paste commands and rollback steps. Use when bootstrapping ops docs, preparing for on-call, or post-incident.DevOpsNo warnings★ 971↓ —
- helm-chart-builderborghei/Claude-SkillsThis skill should be used when the user asks to "analyze Helm charts", "validate Helm values", "review chart structure", "check Kubernetes Helm templates", or "audit chart dependencies and configuration".DevOpsNo warnings★ 971↓ —
- 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↓ —
- imagemagick-toolsdcc-mcp/dcc-mcp-coreInfrastructure skill — image processing and manipulation via ImageMagick: resize, composite, convert formats, add watermarks. Use when batch-processing textures, thumbnails, or rendered images at the file level. Not for in-DCC texture or material editing — use a domain skill bound to the specific DCC for that.DevOpsNo warnings★ 70↓ —
- senior-ml-engineerborghei/Claude-SkillsML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization.DevOpsNo warnings★ 971↓ —
- digital-pathologyawslabs/hcls-agent-skillsGenerate correct code for whole-slide image (WSI) analysis using TIAToolbox and foundation models (H-optimus-0, UNI, Prov-GigaPath). Triggers on requests involving whole-slide images, WSI, digital pathology, histopathology, SVS/NDPI/pyramidal TIFF, tissue segmentation, patch extraction, stain normalization, H-optimus-0, TIAToolbox, CAMELYON16/17, SlideGraph, MIL aggregation, HoVer-Net, PanNuke, or SageMaker deployment of pathology models. Produces deterministic commands and Python snippets for slide-info inspection, tissue masking, tile extraction at specified mpp, foundation-model feature embedding, slide-level aggregation, and regulated cloud inference.DevOpsNo warnings★ 66↓ —
- marketplace-create-extensiondcc-mcp/dcc-mcp-coreInfrastructure skill — scaffold a new marketplace extension package (SKILL.md + tools.yaml + scripts/) with MIT-0 licensing. Use when creating a publishable marketplace entry for any DCC host. Not for editing existing extensions or driving live DCC scenes — use domain skills for that.DevOpsNo warnings★ 70↓ —
- amazon-operating-model-masterswaylq/master-skill亚马逊管理之道 (亚马逊管理之道 (The Amazon Way — Amazon 自身的领导力与运营操作系统) — 这个 master skill 蒸馏的不是『在亚马逊平台上开店卖货』(那是跨境电商运营,已由 cross-border-ecommerce 另蒸),也不是 AWS 云技术架构;而是亚马逊这家公司『怎么运营自己、怎么做决策、怎么招人、怎么创新、怎么规划』的一整套可迁移的管理机制(mechanisms)与心智操作系统。面向想在自己团队/公司引入亚马逊式机制的创始人/CEO/高管/运营负责人(COO)、产品经理与技术产品经理(PM/PMT)、组织与流程设计者(chief of staff / ops)、以及在亚马逊工作或准备面试亚马逊(著名的 LP 行为面试 + Bar Raiser)的人。覆盖: (a) 第一性张力 — 亚马逊 OS 的核心世界观分歧: **机制 > 良好意图(Good intentions don't work, mechanisms do:问题不能靠『下次更努力』修复,要装一个带检查闭环的自我强化流程) ⇄ 可控输入指标 vs 滞后输出指标(管你能控制的 input 如选品/价格/在库/配送速度,而非 revenue/股价这类滞后又不可控的 output) ⇄ 长期主义 vs 季度业绩(Day 1、willing to be misunderstood、用现金流+飞轮长期投入,宁可牺牲短期利润) ⇄ 客户执念 vs 竞争对手执念(working backwards 从客户倒推,而非从竞品或『我们能造什么』正推) ⇄ 叙事备忘录 vs PPT(6-pager + 开会前静默阅读、动手前先写 PR/FAQ;写作逼出清晰思考,禁 PowerPoint) ⇄ 单线程负责人/两个披萨团队 vs 协调税(separable、autonomous、one owner per initiative,砍掉跨团队依赖) ⇄ 高标准可学习可传递 vs 与生俱来(insist on the highest standards + Bar Raiser 招聘门,标准是 domain-specific 且能教的) ⇄ 节俭催生创造力 vs 血汗文化(frugality/约束驱动发明 —— 但 frugality + 高压 bar + URA 强制淘汰 = 文化阴暗面批判) ⇄ 两类决策(Type 1 不可逆/DevOpsNo warnings★ 168↓ —
- speech-to-text-masterswaylq/master-skill语音转文字 (ASR) (语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力,尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成,方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。) Master OS — automated mastery of 语音转文字 (ASR / 语音识别 / SpeechDevOpsNo warnings★ 168↓ —
- devops-sre-masterswaylq/master-skillDevOps 与站点可靠性工程 (SRE) — 平台 / 基础设施 / 可靠性工程师的认知操作系统, 覆盖软件交付 + 运维全生命周期 (CI/CD 与发布工程 trunk-based + 渐进式发布 canary/blue-green/feature flag + GitOps Argo CD/Flux / 基础设施即代码 Terraform/OpenTofu/Pulumi/Ansible + policy-as-code OPA / 容器与编排 Docker/Kubernetes + Helm/Kustomize + service mesh Istio/Linkerd / 可观测性 Prometheus + Loki + OpenTelemetry + Honeycomb + eBPF + RED/USE / SLO-SLI-error budget 与可靠性工程 Google SRE 学科 + 容量规划 + 优雅降级 / 事件管理与 on-call 事件指挥 + PagerDuty + runbook + 无指责复盘 + MTTR / 云平台与 FinOps AWS/GCP/Azure + 成本优化 + 弹性伸缩 / 平台工程与开发者体验 IDP + Backstage + golden path + Team Topologies / DevSecOps 与供应链安全 shift-left + SBOM + SLSA + sigstore + Vault / 韧性与混沌工程 fault injection + game day + 安全科学 / DORA 指标与工程效能 部署频率 + 变更前置时间 + 变更失败率 + Accelerate 研究 / 数据库与有状态运维 schema 迁移 + 备份容灾) — 不含 通用应用开发 / 纯云销售认证速成 / 'DevOps = 跑 Jenkins 的岗位' 窄化误解 / ITIL 工单文化传统运维 (旧范式仅做边界) / 把手工运维 ClickOps 当稳态 (是 toil, 本 skill 核心反模式) (DevOps & Site Reliability Engineering — the cognitive operating system of platform / infrastructure / reliability practitionersDevOpsNo warnings★ 168↓ —
- marketplace-publish-extensiondcc-mcp/dcc-mcp-coreInfrastructure skill — publish (register/update) an extension package to a marketplace catalog. Reads the extension's SKILL.md frontmatter, constructs a CatalogEntry, and upserts it into the target marketplace.json. Optionally commits and pushes when the catalog source is a git repository. Use after scaffolding an extension with marketplace-create-extension. Not for installing or searching extensions — use marketplace-install or marketplace-search for that.DevOpsNo warnings★ 70↓ —
- deploy-previewxvirobotics/metaskillBuild Docker images and launch a local preview environment with docker-compose. Use to test the full stack locally before merging.DevOpsNo warnings★ 73↓ —
- setup-luminarixNewafu-it/malaysia-payment-gatewaySet up, build, debug, review, and explain Luminarix payment integrations using official Luminarix sources (the official Luminarix WooCommerce plugin published by Luminarix Sdn. Bhd. on WordPress.org, since no public developer docs exist), the v1 payments API (deposit, transaction-status, refund), hosted checkoutUrl redirect, channel payment instructions, X-Signature HMAC-SHA256 Base64 request checksums, signed callbacks with X-Timestamp and X-Signature, replay protection, per-payment-method Merchant ID, Sub-Merchant ID, Client Secret and Payment System Code, sandbox/live separation, status polling, full and partial refunds, and Cloudflare allowlisting. Use when working with Luminarix (lmnrx.com) Malaysia, FPX, DuitNow QR, cards, Touch 'n Go, GrabPay, Boost, ShopeePay, MAE, WooCommerce Luminarix setup, callback settlement, refunds, or production readiness.DevOpsNo warnings★ 73↓ —
- astro-ops0xDarkMatter/claude-modsAstro framework patterns, islands architecture, content collections, rendering strategies, and deployment. Use for: astro, islands architecture, content collections, astro cloudflare, view transitions, partial hydration, astrojs, SSG, SSR, hybrid rendering, astro adapter.DevOpsReview first★ 74↓ —
- mind-stingerlegioncodeinc/vibe-coding-toolsReviews, audits, and extends the AI cognitive layer: coach routing, prompt cascade, RAG, three-tier memory, observability, evaluation. Use when reviewing AI code or tuning retrieval.DevOpsNo warnings★ 94↓ —
- specsfy-specialist-docker-swarmpromovaweb/specsfyProjetar, implantar e operar stacks Docker Swarm com serviços, overlay networks, secrets, configs, placement, rollout, rollback e recuperação do quorum. Use para swarm init, docker stack, services, managers, workers e arquivos de stack; não use Compose local como evidência de comportamento do Swarm.DevOpsNo warnings★ 87↓ —
- 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↓ —
- 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↓ —
- specsfy-specialist-observabilitypromovaweb/specsfyProjetar e revisar logs, métricas, traces, correlação, SLOs, alertas e dashboards para explicar comportamento de sistemas. Use quando a tarefa envolve instrumentação, incidentes, telemetry ou operação; use também para definir o sinal que decide um rollout ou dispara um alerta; não confunda volume de logs com observabilidade, e para o diagnóstico de causa de latência específica use `$specsfy-specialist-performance-engineering`.DevOpsNo warnings★ 87↓ —
- canarynota-america/forgecat-agent-profilesPost-deploy canary monitoring. (gstack)DevOpsNo warnings★ 93↓ —
- model-traineraisa-group/skill-injectThis skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.DevOpsNo warnings★ 100↓ —Also in 1 other repo