Skills
98,639 packaged capabilities and playbooks for agents. Install any of them with one command.
All
Install the Datadog Agent on a macOS machine with the macOS installer, with infrastructure metrics, logs, and APM trace intake on by default, plus optional live processes, APM instrumentation, custom tags, and service management. Use when the user wants to install the Datadog Agent on their Mac, set up Datadog on macOS, or monitor a macOS developer or CI machine with Datadog. Does not install the Agent on Linux, Windows, Docker, or Kubernetes (use dd-agent-install-linux, dd-agent-install-windows, dd-agent-install-docker, or dd-agent-install-kubernetes) and does not target managed cloud fleets. Requires a Datadog account with a valid API key and application key; run `dd-account-setup` first if they are missing.
Install the Datadog Agent on Windows Server hosts—a single machine or a fleet—with support for cloud rollouts (AWS Systems Manager, GCP VM Manager, Azure Run Command), MSI properties, FIPS, custom tags, and advanced configuration. Infrastructure metrics, logs, and APM trace intake are enabled by default. Use when the user wants to install the Datadog Agent on a Windows server, deploy Datadog to Windows VMs, or set up the Datadog Agent on Windows Server 2022 or another Windows Server version. Does not install the Agent on Linux, macOS, Docker, or Kubernetes (use dd-agent-install-linux, dd-agent-install-macos, dd-agent-install-docker, or dd-agent-install-kubernetes) and does not connect a cloud provider integration. Requires a Datadog account with a valid API key and application key; if they are missing, run `dd-account-setup` first on commercial sites, or supply them directly on government sites.
Set up Datadog Storage Management for AWS S3 by configuring S3 Inventory on the buckets selected by the user and provisioning destination buckets, a destination bucket policy, a Datadog read policy, and Storage Management sync with the DataDog/storage-management-datadog/aws Terraform module. Supports multi-Region and SSE-KMS destinations and optional S3 access logs. Use when the user wants to set up Storage Management for S3 buckets, monitor S3 storage in Datadog, or enable S3 storage management. Does not connect a new AWS account to Datadog or deploy the Datadog Forwarder Lambda (that is `dd-aws-log-forwarding`). Requires an existing role-based Datadog AWS integration; run `dd-aws-integration` first if it is missing.
Set up Datadog Database Monitoring (DBM) for PostgreSQL on AWS RDS - enable pg_stat_statements on the instance's parameter group, then deploy a managed Agent with CloudFormation, provision one with Terraform, or configure an existing Agent, plus the read-only datadog monitoring user. Use when the user wants to set up Database Monitoring for an RDS Postgres database, monitor query performance on a Postgres RDS instance, enable DBM for Postgres on RDS, or use Terraform to set up DBM for an RDS Postgres database. Does not handle Aurora, RDS Multi-AZ DB clusters, or read replicas (point the user at the source writer or the DBM docs instead), and does not set up DBM for other engines. Requires the Datadog AWS integration for the AWS account that holds the instance; run `dd-aws-integration` first if it is missing.
Set up Datadog Cloud Cost Management for GCP - enables the BigQuery and BigQuery Data Transfer APIs, walks the user through the Cloud Billing detailed-usage export to BigQuery, then uses Terraform to provision the GCS bucket and the predefined IAM role bindings Datadog documents for the Datadog service account, and registers the cost config through datadog_gcp_uc_config. Use when the user wants to track their GCP cloud costs, asks to set up a GCP billing export for Datadog, or asks to set up Cloud Cost Management for GCP. Does not create the GCP integration or the Datadog service account itself. Requires an existing Datadog GCP integration; run `dd-gcp-integration` first if it is missing.
Set up GCP log forwarding to Datadog with Terraform. Creates a Dataflow pipeline running Google's Cloud_PubSub_to_Datadog template, fed by log sinks on the selected projects and/or folders through a Pub/Sub topic and subscription, with a dead-letter queue, a GCS staging bucket, and Secret Manager holding a dedicated Datadog API key. Use when the user wants to forward GCP logs to Datadog, set up GCP log forwarding, or send Cloud Logging to Datadog. Does not create the GCP integration itself or set up metrics collection. Requires the Datadog GCP integration; run `dd-gcp-integration` first if it is missing.
Debugs issues users encounter with Tuist-generated projects by reproducing the scenario locally, building Tuist from source when needed, and triaging whether it is a bug, misconfiguration, or something that needs team input. Use when users report generation failures, build errors after generation, or unexpected project behavior.
Builds a one-screen executive report dashboard for teachers and lecturers. Presents all key class indicators (assignment submission rate, average CPMK, grade distribution, alerts for students needing attention) with a responsive Mobile-View First design and strict credential security.
Connect a JavaScript or TypeScript app to Fallow Cloud production runtime coverage. Use when asked to set up Fallow Cloud, add the Fallow beacon, send runtime coverage to Fallow Cloud, or find out why Fallow Cloud shows no data for a repository. Do not use for local code-quality tooling; use fallow-setup for that. Do not use to read runtime data of a repository that is already set up; use the fallow skill for that.
Use when the user asks to fix, patch or upgrade a Node.js vulnerability (e.g. 'Please fix my Node vulnerability'), the October 21, 2026 Node.js security release, CVE-2026-111497 (Brotli RCE), or end-of-life Node 20, across Docker images, .nvmrc/.tool-versions, setup-node CI, Lambda and IaC. Read-only inventory, approval gate, regression tests, focused PRs.
MANDATORY - use before planning, building, or redesigning an ArcGIS application, including requests that do not specify an ArcGIS technology. For existing applications, use when a change could affect architecture, product, technology, data access, or authentication. Review context, clarify consequential unknowns, and route to relevant authentication and build skills.
Guides day-to-day work in Tuist-generated Xcode workspaces, including generation, build and test commands, and buildable folders. Use when working in a Tuist-generated project or when users mention `tuist generate`, `xcodebuild test`, or generated workspaces.
Turn a product, a feature or an idea into a finished one-minute explainer video in any look: a script (hook, benefit, quick tutorial, try it), a voice whose words are the clock, HTML scenes drawn from that one clock and rendered frame by frame at 2×, and an edit in Fil's video editor over MCP with captions, music, sound effects and chapters. The look is an input, read from the person's site or a named style, so the same film can wear any type, colour, corners and motion. The shape is an input (4:3, 16:9, 1:1, 9:16). No camera by default: a voice-led film whose scenes fill the frame; ask for a camera and it adds record slots, layouts by keyframes and a teleprompter. Use when someone wants an explainer, a feature video, a product walkthrough, an onboarding video, a how-it-works video or a launch video, wants one in their own brand, or wants it without filming anything.
Design and build self-contained interactive HTML5+CSS3+JS presentation slides in 16:9 format, with keyboard navigation, full-screen mode, interactive QR codes for students, and print-ready layouts for instant PDF export without distortion.
Aggregate and read real user monitoring (RUM) data via KubeSense MCP — sessions, views, actions, errors, resources and long tasks from browser and mobile apps. Covers the rum_<type> signals, the per-event-type field catalog, which fields accept LIKE or group-by, nanosecond durations and their _ms/_s twins, feature flags and attributes, and the dashboard panel shape. Counts, rates and percentiles with analyze-telemetry, raw rows with search-rum. There is no alerting, SQL or SPL for RUM.
Diagnose why a Mac (especially one running Claude Code, other AI coding tools, or several Electron-based IDEs at once) is slow, hanging, or freezing due to memory pressure and swap thrashing. Covers reading real swap/memory numbers, finding MCP-server and plugin process bloat, spotting orphaned or duplicate background processes, safely clearing regenerable caches, and recognizing when a killed process keeps respawning because something else supervises it. Use this whenever the user says their Mac/MacBook is slow, laggy, hanging, freezing, "using too much memory," has "high memory pressure," or asks to find/stop/kill whatever is eating RAM or CPU. Every fix in this skill is reversible (stop, kill, disable, clear cache) — it never deletes or uninstalls anything, and it says so explicitly before running anything.
Builds a professional Excel spreadsheet workbook (.xlsx) for calculating Outcome-Based Education (OBE) assessment fulfillment, mapping CPL and CPMK attainment per student, grade distribution matrices, and visualizing accreditation achievement charts.
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", "orca artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only when a visible window needs GUI control that a CLI, filesystem, or API cannot do.
Read org state and change Setup through the Headless 360 (H360) MCP tools discover, describe, dispatch_readonly, and dispatch. Use when H360 tools are present and the answer is org state no skill or sf command reads: Setup settings, feature or license access, org-specific caps, health checks, release updates. Also H360 setup and errors (401, 403, 404, ROUTE_NOT_FOUND, INVALID_ARGUMENT). DO NOT TRIGGER for records, SOQL, schema, limits in sf org list limits, deploys, or which-plugin questions.
Transform mocked-up UI specs (specs-ui skill project) into a purely UI clickable prototype in HTML + Tailwind + Alpine.js: main flows are wired up (navigation, forms, states, simulated data stored in the browser, reset), with guided test scenarios for user testing and demos, published as a multi-page artifact. Use whenever someone wants a prototype, clickable mockup, interactive demo, user-testable flow, user test, or a version "you can click through" based on existing specs or mockups, even without saying "prototype".
Use when studying a SaaS web product from the outside in order to rebuild, clone or copy it (ClickUp, Pipedrive, Notion, a CRM, a project tool), when documenting its UX, screens, components, data model, business rules or API through a browser the agent drives, when assessing what would block a clone from launching (permissioned integrations, regulation, certifications), or when an agent must explore a live SaaS account without damaging production data or notifying real people.
Organizes and builds a per-session/class-meeting directory structure for courses or classes interactively and with full confirmation. Prepares subfolders for materials, presentations, exercises, games, practical work, and student assignment submissions.
Records the best scenes of a website or web app (WebGL hero, 3D, scroll) as 1080p MP4 for decks, uploads them to Figma Slides, and builds brand or product showreels in Remotion. Use on "record the site scenes", "site video for the deck", "showreel", "launch video", "gerar vídeo do site", "vídeo pro deck", "reel do produto", /site-reels.
Rules, Remotion helpers and audio scripts for stop-motion style video cut to music, proposing Remotion by default to make it. A low frame rate is kept on purpose, with one pose per eighth note of the song. Pages start on bar lines, clicks land on kicks and snares, and the skill teaches when to skip a beat. Use when making or reviewing stop-motion, low frame rate, beat-synced or music-synced video. Also use for cutting a video to a song, Remotion launch films cut to a track, compositions timed to a BPM, metronome or click tracks, kinetic type timed to a beat, or scenes played live on a web page in time with a metronome. It covers measuring a song's tempo, bars and drops, and deciding where a cut, a click, a hold or a transition should land.
Generates periodic learning progress report files and individual student learning portfolio books in Microsoft Word (.docx) or Excel (.xlsx) format, presenting work samples, grade history, and reflections on competency attainment.
Automatically assess student assignment files collected in the submissions folder against a grading rubric. Provide transparent analytical scores and constructive formative feedback, and automatically identify remedial and enrichment needs.
Use tdir-cli to query and directly generate the EMDR security operations threat daily report (including security incidents, endpoint and insight/ticket distribution, response times, and items requiring attention). Use when the user requests generating, recalculating, or reviewing an EMDR threat daily report or operations daily report. Does not handle TDIR global weekly reports, in-depth forensics of individual insights, or security incident response.
Verify web UI changes in headless Chrome with agent-browser, signed in as the project's Clerk test users, and run the project's relevant existing tests. Use after changing anything visible, or when asked to check, inspect, screenshot or exercise a browser flow.
Prevent, detect, and repair AI coding slop and dirty engineering in repositories. Use for planning features, writing code, changing tests, reviewing pull requests, refactoring, hardening CI, debugging regressions, or auditing agent-generated changes. Enforces behavioral contracts, focused diffs, test integrity, repo conventions, security boundaries, and evidence-based completion.
Create an evidence-backed DESIGN.md for any public website URL. Use when asked to extract a site's design system, colors, typography, spacing, radii, shadows, components, or to generate a design.md compatible with Google's design.md format. Composes the awwwards MCP inspection/capture tools; does not invent brand intent, source token names, or unobserved states.
Source-cloud-neutral intake and requirements step for migrating AI/LLM workloads to Amazon Bedrock. Use when the user already has a provider-neutral ai-workload-profile.json (schema_version 1.0 with ai_sources[] and gateways[]) that already matches this contract, and wants to validate it and answer the AI migration questions. It accepts OpenAI (direct or Azure OpenAI), Anthropic, Google/Gemini, and OpenRouter-gateway profiles and needs no infrastructure skill installed. This release stops after Clarify and does not scan code, design, estimate, or generate artifacts. For a full AI migration from source code use llm-to-bedrock; for infrastructure migrations use gcp-to-aws, azure-to-aws, or heroku-to-aws.
Generates interactive quizzes and assignments based on educational board games (snakes and ladders, crossword puzzles, concept Monopoly, academic bingo, question mazes, spin wheels/Jeopardy) as standalone interactive HTML5 and printable worksheets (PDF/A4).
恢复被 C3RB3R / Cerber 系勒索软件（文件后缀 .L0CK3D、勒索信 read-me3.txt）加密的群晖（Synology DSM）或其他 Btrfs NAS 上的数据，无需密钥：利用 Btrfs 写时复制留下的旧 extent 逐字节还原原文件，并用 Btrfs 校验和逐块证实。覆盖 CoW / NoCOW 判定、子卷快照代数（lastsnap）效应、extent 树精确重组、大文件跨 extent 推测、统计、导出、验证和报告。只要用户提到 NAS / 群晖 / 黑群晖 被勒索、.L0CK3D、read-me3.txt、C3RB3R、Btrfs 文件被加密想恢复、"没有备份没有快照怎么救"，即使没提 Btrfs 或 extent，也应使用本 skill。Use for ransomware recovery of .L0CK3D files on Synology/Btrfs volumes without the key.
Assemble Chinese running-script calligraphy from original glyphs, look up character forms and sources, inspect radicals, and synthesize missing glyphs. Use for calligraphy composition, character images, or copybook references. Read this skill for exact CLI syntax before running commands
Connects player, item, upgrade, and enemy data through one stat model and a chosen defense formula (flat, ratio, level-scaled ratio, or capped percent), computes hits and seconds to kill and to die for each stage and enemy, and flags results outside designer target ranges. Use when setting or reviewing combat stats, choosing a defense formula, or checking difficulty per zone, level, or wave in any genre and engine.
Turn the changes in a git working tree into clean Conventional Commits: read the diff, run the repository's own check, stage by path, split unrelated changes into separate commits, write the message, and verify what landed. Use when: (1) the user wants to commit, save progress, record WIP or wrap up a finished task, (2) a working tree mixes unrelated changes that should become separate commits, (3) a change breaks existing usage and the release tooling must see it, (4) the user mentions: commit, commit this, commit my changes, save progress, WIP commit, conventional commit, commit message, git add, staged, untracked, BREAKING CHANGE, feat!, Co-Authored-By, pre-commit hook, "split this into commits". Carries the staging rules that keep secrets and build output out, the breaking-change markers that `bumpv` reads to choose a MAJOR release, the hook and amend rules, and a post-commit check that no attribution trailer landed. Any git repository and any stack; the repository's own check and documented conventions are the seam. Not a release tool, not a changelog writer and not a push or pull request flow
Models item enhancement (cost, success rate, and keep/downgrade/destroy on failure per level) and synthesis (fixed recipes and random merges that combine matching items into one of a matching set), computes the expected cost and attempts to reach each level, the value ratio of each recipe against stat budgets, and the expected crafts to get one specific result, and finds item-multiplication loops and items no drop, recipe, or declared source can produce. Use when designing or reviewing upgrade systems, crafting, merging, salvage, or item acquisition paths in any genre and engine.
Install the Datadog Agent on a Docker host—standalone Docker (Linux or Windows) or Docker Compose—with infrastructure metrics, container logs, APM trace intake, and DogStatsD enabled by default, plus optional live processes, tags, hostname, proxy, and container filtering. Keep the API key in a restricted env file rather than on the command line. Use when the user has Docker containers on a single server, wants to install Datadog on their Docker host, or wants to set up Datadog with Docker Compose. Does not install the Agent on Kubernetes or directly on a Linux, macOS, or Windows host (use dd-agent-install-kubernetes, dd-agent-install-linux, dd-agent-install-macos, or dd-agent-install-windows). Requires a Datadog account with a valid API key and application key; run `dd-account-setup` first if they are missing.
Install the Datadog Agent on Linux hosts—a single machine or a fleet—with support for cloud rollouts (AWS Systems Manager, GCP VM Manager, Azure Run Command), remote configuration management, proxy, FIPS, custom tags, and advanced configuration. Infrastructure metrics, logs, and the APM trace receiver are enabled by default; host Single Step Instrumentation is enabled only when requested (for an SSI-first request, use `dd-apm/linux-ssi` `agent-install`). Use when the user wants to monitor Linux servers, install the Datadog Agent on an Ubuntu or other Linux host, or roll the Agent out to an EC2 or other cloud VM fleet without SSH. Does not install the Agent on Windows, macOS, Docker, or Kubernetes (use dd-agent-install-windows, dd-agent-install-macos, dd-agent-install-docker, or dd-agent-install-kubernetes) and does not connect a cloud provider integration. Requires a Datadog account with a valid API key and application key; run `dd-account-setup` first if they are missing.
Set up Datadog Cloud Cost Management for AWS by configuring a Cost and Usage Report - either CUR 2.0 (recommended) or a Legacy CUR - and provisioning the S3 bucket, bucket policy, IAM read policy, role attachment, and Datadog registration with Terraform. Use when the user wants to track their AWS cloud costs, set up cost and usage reports, set up Cloud Cost Management (CCM) for AWS, set up CCM with CUR 2.0, or connect an AWS Data Export to Datadog. Does not connect a new AWS account to Datadog and does not set up cost management for other clouds. Requires an existing role-based Datadog AWS integration; run `dd-aws-integration` first if it is missing.
Set up AWS log forwarding to Datadog with Terraform: deploys the Datadog Forwarder Lambda in the user's AWS account, seeds the Datadog API key into a Secrets Manager secret out-of-band so it never lands in Terraform state, and registers the forwarder ARN on the existing AWS integration so Datadog automatically subscribes CloudWatch log groups and S3 buckets. Use when the user wants to forward AWS logs to Datadog, send CloudWatch logs to Datadog, or set up the Datadog Forwarder Lambda. Does not create the AWS integration itself or set up metrics collection. Requires the Datadog AWS integration (datadog_integration_aws_account); run `dd-aws-integration` first if it is missing.
Set up Datadog Cloud Cost Management for Azure with Terraform. Provisions (or reuses) a Storage Account and Container, creates actual-cost and amortized-cost Cost Management exports, grants the existing Datadog app registration read access through role assignments, and registers the exports with Datadog through datadog_azure_uc_config in a two-phase apply around the manual file-partitioning step. Use when the user wants to track Azure cloud costs, set up Azure cost exports for Datadog, or set up Cloud Cost Management for Azure. Does not create the Azure integration or its app registration/service principal. Requires an existing Datadog Azure integration; run `dd-azure-integration` first if it is missing.
Set up automated Azure log forwarding to Datadog by running Datadog's official Log Forwarding Orchestrator (LFO) install script through the Azure CLI - deploys the managed control plane that auto-discovers resources across the monitored subscriptions and provisions and scales the log forwarders itself, with no per-resource diagnostic settings. Use when the user wants to forward Azure logs to Datadog, send Azure resource logs to Datadog, or set up Azure log forwarding. Does not generate Terraform and does not create the Azure integration or set up metrics collection. Requires the Datadog Azure integration; run `dd-azure-integration` first if it is missing.
Set up the Datadog Confluent Cloud integration: create a Cloud resource management API key in Confluent Cloud (with the Confluent CLI or the console), register the account with Datadog, discover and register its Kafka clusters, connectors, ksqlDB and Schema Registry resources, and optionally add Schema Registry tags and Cloud Cost Management. Use when the user wants to monitor Confluent Cloud Kafka clusters, set up the Datadog Confluent Cloud integration, connect Confluent Cloud to Datadog, see Confluent metrics in Datadog, or monitor Kafka clusters on Confluent. Does not set up self-managed Kafka or the Datadog Agent. Requires a Datadog API key and application key; run `dd-account-setup` first if they are missing.
Set up Datadog Cloud Cost Management for Oracle Cloud Infrastructure (OCI) by enabling cost collection on an integrated parent tenancy—an API toggle on the existing tenancy integration, with no Terraform, storage buckets, exports, or extra IAM policies. Use when the user wants to track their OCI cloud costs or asks to set up Cloud Cost Management for OCI. Does not create the OCI tenancy integration itself or provision any infrastructure. Requires an existing Datadog OCI integration; run `dd-oci-integration` first if it is missing.
Scaffold a directions lab that shows shipped UI (Today) beside 2-3 design directions, with surface tabs, theme and animation toggles and the V1 MVP hover motion, registered in design-proposals Storybook. Use for a directions page or lab, or to explore design directions next to production.
Models currency income per stage (kills times enemy rewards, expected sell value of drops, other income) and an ordered purchase plan whose prices come from item prices, expected enhancement costs, crafting costs, permanent upgrade costs, or fixed amounts, then computes when each purchase becomes affordable within a run, which run a carried meta currency affords it, and how much currency is left over. Use when tuning rewards, prices, shop or upgrade pacing, or checking for currency piling up or running short, in any genre and engine.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.