Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Cloud & DevOps
Catalyst Zia Services — OCR, Face Analytics, Text Analytics, Object Detection, Barcode Reader, and Content Moderation via pre-trained AI APIs. Trigger on 'Zia', 'OCR', 'face detection', 'text analytics', 'object detection', 'barcode', 'content moderation', or 'image recognition'. DC restrictions: Identity Scanner Document Processing is IN DC only (API included); Facial Comparison works via API from any DC (console testing is IN DC only). For QuickML (AutoML, ML pipelines, LLM Serving, RAG) load catalyst-quickml instead.
Catalyst pricing — free tier limits, pay-as-you-go rates, GB-seconds calculation, and cost estimation for functions, storage, and other services. Trigger on 'pricing', 'cost', 'free tier', 'how much does Catalyst cost', 'GB-seconds', 'billing', or 'will this be free'.
Guides the agent through installing, authenticating, configuring, and using the Capawesome CLI (@capawesome/cli). Covers installation, interactive and token-based authentication, project linking via capawesome.config.json, the full command reference (app management, native builds, live updates, certificates, environments, channels, deployments, destinations, devices), CI/CD integration with token auth and JSON output, and diagnostics via the doctor command. Do not use for Capawesome Cloud feature setup (native builds workflow, live updates workflow, app store publishing) — use the capawesome-cloud skill instead.
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes
This skill should be used when the user wants to manage Railway deployments, view logs, or debug issues. Covers deployment lifecycle (remove, stop, redeploy, restart), deployment visibility (list, status, history), and troubleshooting (logs, errors, failures, crashes, why deploy failed). NOT for deleting services - use environment skill with isDeleted for that.
Diagnoses dbt Cloud/platform job failures by analyzing run logs, querying the Admin API, reviewing git history, and investigating data issues. Use when a dbt Cloud/platform job fails and you need to diagnose the root cause, especially when error messages are unclear or when intermittent failures occur. Do not use for local dbt development errors.
Work with the upstash-box Python SDK for sandboxed cloud containers with AI agents, shell, filesystem, git, cron schedules, snapshots, and a headless browser. Use when building with Upstash Box in Python, creating a sandbox or isolated environment to run untrusted or agent-generated code, running AI coding agents in containers, giving an agent a cloud dev environment with a shell and repository, browser automation from a box, scheduling recurring jobs inside a box, saving and restoring snapshots, or orchestrating parallel boxes.
CloudBase auth provider configuration and login-readiness guide. This skill should be used when users need to inspect, enable, disable, or configure auth providers, publishable-key prerequisites, login methods, SMS/email sender setup, or other provider-side readiness before implementing a client or backend auth flow.
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL, including Auto CDC from event streams or periodic complete snapshots. Invoke BEFORE starting implementation.
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
Single entry point for all AEM as a Cloud Service Workflow skills. Covers workflow model design, custom process step and participant chooser development, launcher configuration, workflow triggering, and production support including debugging stuck or failed workflows, triaging incidents with Cloud Manager logs, thread pool analysis, and Sling Job diagnostics for the Granite Workflow Engine.
Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Provisioned uses aws mwaa invoke-rest-api, CloudWatch log groups, and get-environment; Serverless uses aws mwaa-serverless API (GetWorkflowRun, ListWorkflowRuns, GetTaskInstance) and CloudWatch logs. Covers failed runs and tasks, DAGs not appearing, import errors, worker OOM, IAM denials, and dependency drift. Triggers on: DAG failed, task failed, workflow run failed, MWAA error, debug my DAG, why did my workflow fail, DAG not showing up, MWAA import error, requirements failing, worker crashed, serverless run failed. Not applicable to authoring workflows (handled by authoring-mwaa-workflow), running or smoke-testing a workflow (handled by testing-mwaa-workflow), or CI-CD deploy failures.
Single entry point for all AEM as a Cloud Service Dispatcher skills. Covers config authoring, technical advisory, incident response, performance tuning, security hardening, and full lifecycle orchestration for the Adobe Dispatcher Apache HTTP Server module and its HTTPD/dispatcher configuration.
Implement Terraform provider configuration and authentication with the Plugin Framework: provider schema for credentials (Optional + Sensitive attributes), environment variable fallbacks, credential provider chains (static config, then environment variables, shared credentials file, and platform identity), unknown-value guards in Configure(), secret redaction, configure-time credential validation, and diagnostics that name every source tried. Use when implementing or reviewing a provider's Configure method or provider schema, adding authentication options (API keys, tokens, profiles, credentials files, assume-role), deciding how a provider should resolve credentials, debugging "no valid credential sources" or missing-credentials errors, or unit testing credential resolution.
Authors and deploys MWAA workflow artifacts: Python Airflow DAGs for provisioned environments or YAML workflow files for Serverless. Covers operator selection, timeout design, retry strategy, scheduling, failure notifications, idempotency, and MWAA Serverless schema compliance. Deploys the artifact (S3 DAG upload or Serverless CreateWorkflow/UpdateWorkflow), creates an environment inline when approved, and redeploys fixes, then optionally hands off to testing-mwaa-workflow. Triggers on: create a DAG, write a pipeline, build a workflow, orchestrate tasks, Airflow DAG, data pipeline, schedule a job, deploy a DAG, deploy a workflow, YAML workflow. Not applicable to converting or migrating existing DAGs between provisioned and serverless (conversion is out of scope), running or smoke-testing a deployed workflow (handled by testing-mwaa-workflow) or diagnosing a failed run (handled by debugging-mwaa-workflow).
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
Tests Amazon MWAA workflow execution end to end: triggers a run and monitors it to completion for Provisioned (Python DAG via Airflow REST API) and Serverless (YAML workflow via StartWorkflowRun). Verifies the artifact is deployed and parse-ready, triggers with confirmation, polls to a terminal state, and on failure delegates diagnosis to debugging-mwaa-workflow and artifact or redeployment fixes to authoring-mwaa-workflow, then retests up to a capped number of attempts. Triggers on: test my DAG, test my workflow, run my DAG, trigger a test run, does my DAG work, smoke-test the pipeline, verify my workflow runs, execute my DAG to check it. Not applicable to writing or deploying a new workflow (handled by authoring-mwaa-workflow), or diagnosing why a run failed or root-causing an error (handled by debugging-mwaa-workflow).
Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an approach by whether run history and the same environment (URL/ARN) must be kept: a new-environment upgrade (blue-green), a rehearsed in-place upgrade validated on a test copy, or a direct in-place upgrade. Runs Ruff scanning and deprecation-warning log scans for 3.x moves, plus Docker validation, batched deployment, and switchover. Saves a resumable upgrade plan for multi-session work. Triggers on: upgrade MWAA, upgrade Airflow, migrate to Airflow 3, MWAA Airflow 3, Airflow 2 to 3, preserve Airflow history, keep same MWAA environment, blue-green cutover, validation environment before cutover. Not for authoring new DAGs (authoring-mwaa-workflow), debugging unrelated DAG failures (debugging-mwaa-workflow), or MWAA Serverless YAML workflows (provisioned Python DAGs only).
Operate Railway infrastructure: sign up for or sign in to a Railway account, create projects, provision services and databases, manage object storage buckets, deploy code, configure environments and variables, manage domains, troubleshoot failures, check status and metrics, set up Railway agent tooling, and query Railway docs. Use this skill whenever the user mentions Railway, signing up, creating an account, registering, logging in, deployments, services, environments, buckets, object storage, build failures, agent setup, MCP, or infrastructure operations, even if they don't say "Railway" explicitly. Also invoke this skill when the user asks to be signed up, registered, or onboarded to Railway: do not refuse — drive them through the unauthed `railway up` flow (deploys + signs up on the fly) or `railway login` (which creates new accounts on the fly).
Hit the Cloudflare REST API directly for operations that wrangler and MCP can't handle well. Bulk DNS, custom hostnames, email routing, cache purge, WAF rules, redirect rules, zone settings, Worker routes, D1 cross-database queries, R2 bulk operations, KV bulk read/write, Vectorize queries, Queues, and fleet-wide resource audits. Produces curl commands or scripts. Triggers: 'cloudflare api', 'bulk dns', 'custom hostname', 'email routing', 'cache purge', 'waf rule', 'd1 query', 'r2 bucket', 'kv bulk', 'vectorize query', 'audit resources', 'fleet operation'.
CloudBase Run backend development rules (Function mode/Container mode). Use this skill when deploying backend services that require long connections, multi-language support, custom environments, AI agent development, or migrating existing/GitHub apps that need VPC access to MySQL/PostgreSQL/Redis. Also use when diagnosing CloudRun container deploy failures (deploy_failed, readiness/probe failed, image won't start, docker.io pull loops) or a deploy stuck behind a running deploy task. For stateless HTTP services, prefer HTTP cloud functions.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Catalyst Signals — event-driven architecture platform for near-instantaneous communication between decoupled applications. Supports Zoho publishers, Catalyst publishers, custom publishers, webhooks, functions, circuits, event filtering, transformation, batch/scheduled dispatch, and retry policies. Trigger on 'Signals', 'event bus', 'publisher', 'event-driven', 'webhook target', 'dispatch policy', 'event transformation', 'rule filter', or 'event ordering'. Console-only service — no SDK or programmatic API.
Catalyst SmartBrowz — browser automation and document generation service. Includes Headless (connect to remote Chrome/Firefox with Puppeteer/Playwright/Selenium), Browser Logic (serverless functions for browser tasks in Java/Node.js), PDF & Screenshot (generate visual documents from HTML/URL/Template), Templates (design dynamic content templates), Browser Grid (parallel headless browsers with auto-scaling, Early Access), and Dataverse (data scraping APIs). Trigger on 'SmartBrowz', 'headless browser', 'Headless Browser', 'Puppeteer', 'Selenium', 'Playwright', 'Browser Logic', 'PDF generation', 'screenshot', 'PDF/Screenshot generation', 'PDF & Screenshot', 'browser automation', 'Browser Grid', or 'web scraping'. Console + SDK (Java/Node.js/Python for PDF/Screenshot + Browser Grid) + CLI (for Browser Logic functions).
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
Configures trace spans, defines custom metrics, sets up log exporters, and optimizes sampling strategies for OpenTelemetry instrumentation. Use when instrumenting applications with traces, metrics, or logs. Triggers on requests for observability, telemetry, tracing, metrics collection, logging integration, or OTel setup.
Use this skill when creating or configuring CI/CD pipeline files for automated testing, building, and deployment. Use it to generate GitHub Actions workflows, GitLab CI configs, CircleCI configs, or configurations for other CI/CD platforms. Ideal for setting up automated pipelines for Node.js/Next.js applications, including linting, testing, building, and deploying to platforms like Vercel, Netlify, or AWS.
Remediates Google Cloud Security Command Center findings, including IAM permission fixes, cloud resource misconfigurations, vulnerabilities, and Toxic Combinations. Use when asked to fix, remediate, or mitigate a Security Command Center finding or address attack paths. Don't use for general IAM policy querying without a Security Command Center finding. For runtime threat detections, this skill provides containment and investigation guidance rather than automated configuration fixes.
Review a development pipeline where AI coding agents write, commit and deploy — permission boundaries, approval gates on irreversible actions, credential scope, and what must never be delegated. Use when agents have write access to a repository or an environment.
Report in natural language the health, performance, resource status and actual cost of apps deployed by qianwenai-deploy to Alibaba Cloud's China site (aliyun.com), without opening the console. Reads the .qianwenai-deploy state file in the project directory to identify the app and its ECS (and optional RDS MySQL) resources, then calls read-only cloud APIs to summarize app availability, ECS/RDS performance and cost in RMB. Use when: the user asks how a deployed app is doing, wants a health/performance/cost overview, is concerned about CPU/memory/connections/slow SQL risks, or asks about the monthly cost of qianwenai-deployed apps. Don't use when: there is no .qianwenai-deploy state file; the target is Alibaba Cloud's international site (use qwencloud-observe); the user wants to change or restore resources (use qianwenai-operate).
Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation. For compute (serverless, performance mode, classic), also use databricks-serverless
Diagnose why an app deployed by qianwenai-deploy to Alibaba Cloud China (aliyun.com) is unavailable or noticeably slower, then perform safe recovery only after the user explicitly confirms, and verify the result. Reads the .qianwenai-deploy state file in the project directory to identify ECS (and optionally RDS MySQL) resources, runs a minimal read-only diagnosis first, then proposes only one recovery action at a time (start ECS, restart the app service, reload Nginx), each with a confirmation that explains the impact. Use when: the user says the app won't open, returns 502, or can't reach the database, or asks for a restart and confirmation that the app recovered. Don't use when: there is no .qianwenai-deploy state file; the target is Alibaba Cloud international (use qwencloud-operate); the user only wants a status/cost overview (use qianwenai-observe).
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.