Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Cloud & DevOps
Troubleshoot production issues using backend function logs and workflow run history. Use when investigating app errors, debugging function calls, diagnosing why a scheduled job or automation failed, or diagnosing production problems in Base44 apps.
Builds, configures, debugs, and optimizes AWS observability—operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces—on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup—creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni—use setting-up-cloudwatch-observability. Not for app logging or threat detection.
Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript/TypeScript (SDK with Actor.init/exit), Python (async context manager), or any language (CLI wrapper). Use when migrating code to Apify, wrapping CLI tools as Actors, or adding Actor SDK to existing projects.
Put something on the internet from Cargo — hosted web apps (Vite by default, other static frameworks detected) and serverless edge workers that answer HTTP requests, plus the deployments that build and promote them, the env vars and secrets a worker reads, running a worker locally, and custom domains and search indexing for public sites. Triggers: "build me a dashboard for this", "host this app", "give me a URL to share", "deploy this", "I need a webhook endpoint", "make it live", "promote to production", "ship a UI for my team", "give my worker an API token", "set a secret on the worker", "Missing CARGO_API_TOKEN", "my app cannot call my worker", "run the worker locally", "put it on my own domain", "make the site indexable by Google". Skip when: the app or worker should be declared as committed workspace code — use cargo-project.
Operational controls for long-lived or cloud-hosted agent systems — runtime lifecycle (start, pause, stop, restart), observability (logs, metrics, traces), least-privilege safety scopes and kill switches, and rollout/rollback change management with audit logs and success/cost metrics. Use when running production agent fleets on PM2, systemd, or containers that need monitoring, incident response, or deployment gates.
Generates operational guidance for CI/CD deployment pipelines, observability, monitoring, logging, and incident management on Google Cloud based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use when the user asks about operations-related topics like capacity management, change management, incident and problem management, configuration management, deployment automation using infrastructure as code (IaC), or operational health checks for Google Cloud workloads.
Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.
Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.
Authors, deploys, and troubleshoots AWS infrastructure using CDK with TypeScript or Python. Covers best practices, stack architecture, and construct patterns. Applies when writing CDK constructs, bootstrapping environments, running cdk deploy/synth/diff, fixing CDK or CloudFormation errors, planning stack structure, importing existing resources, resolving drift, or refactoring stacks without resource replacement.
Performs comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.
Builds and deploys containerized workloads on Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Fargate, and ECR (Elastic Container Registry). Covers general EKS knowledge, Karpenter, AWS Load Balancer Controller and leveraging various open source Kubernetes projects with EKS. Covers general ECS knowledge, task definitions, Fargate services, ECS Exec, ECS Express Mode and ECS Managed Instances. Covers general Elastic Beanstalk knowledge, Elastic Beanstalk configuration and platforms supported by Elastic Beanstalk. Covers general ECR knowledge, ECR repository setup and lifecycle policies. Includes recommending, enabling, and reading Amazon ECS Action Logs to troubleshoot control-plane failures (deployment rollback/circuit-breaker, task placement, scaling, task replacement). Applies when deploying, debugging, or optimizing containers on AWS. Should be used instead of relying on internal knowledge for these services.
Provides recommendations for environmental sustainability, carbon footprint reduction, and energy efficiency based on the Sustainability pillar of the Google Cloud Well-Architected Framework (WAF). Use when the user asks to assess, design, or optimize Google Cloud workloads for sustainability—including the shared responsibility model, selecting low-carbon regions (CFE%), reducing resource and AI/ML energy waste, designing efficient software and storage lifecycles, or measuring and tracking emissions using Google Cloud Carbon Footprint. Don't use for financial cost reduction (use google-cloud-waf-cost-optimization), latency and throughput tuning (use google-cloud-waf-performance-optimization), or high availability and disaster recovery (use google-cloud-waf-reliability).
Invoke the `groundcover` Go CLI to manage Groundcover resources (dashboards, monitors, silences, connected apps, notification routes, API keys, policies, integrations, pipelines, workflows) and to answer production observability questions by querying logs, traces, metrics, k8s inventory and k8s events. Use whenever a task needs an authenticated call against the Groundcover API or the user is debugging a prod issue and asks things like "why is X erroring in prod", "show me logs for service Y", "what's the p99 latency on Z", "what pods are crashlooping", "search traces for slow requests", "any k8s events for namespace N", "is service S receiving traffic", "list groundcover monitors", "create a silence", "update notification route", "hit a groundcover endpoint". Covers required env vars, the SDK-backed vs raw command split, and concrete request-body templates for logs/traces/metrics/k8s so the CLI can be driven from anywhere.
INVOKE THIS SKILL when building, testing, or deploying Managed Deep Agents in LangSmith. Walks a user through their first agent end to end — interviewing them about what they want to build, mapping it onto what MDA can actually do, then scaffolding and deploying it. Covers the file-based project layout; define_deep_agent / defineDeepAgent; instructions, skills, memory, identity, tools, MCP servers, connections, middleware, sandboxes, schedules, channels, and evals; the mda CLI; GitHub deployments; and Context Hub.
Entry point for Alibaba Cloud Bailian image/video/audio generation and understanding: use `bl image` / `bl video` / `bl speech` / `bl vision describe` / `bl omni` when the user wants image generation, drawing, photo or image creation, AI art, posters, avatars, illustrations, text-to-image, image-to-image, image editing or retouching, multi-image composition, video generation, text-to-video, image-to-video, reference-to-video, video editing, style conversion, dubbing, text-to-speech (TTS), reading aloud, transcription, speech recognition (ASR), image understanding, visual Q&A, video understanding, video reading, or multimodal understanding. Default behavior: if the user does not specify a provider, use this skill by default for generation/editing; also use it for video understanding and audio/video understanding the host cannot handle. For simple image Q&A, if the host can answer directly and the user has not named Bailian, the host may answer first to save cost. Use this skill when the user wants image or video recognition, specifies a VL/Omni model, or requests video understanding. Images and audio are returned synchronously and saved as local files; video is an asynchronous task, retrieved with `--download` or polling. Pass local files by path; the CLI uploads them automatically. Do not use this skill for ordinary Q&A, programming, writing, or translation; use bailian-cli for Bailian apps, knowledge bases, usage, or quotas; use bailian-finetune for fine-tuning. Official installation: `bl skill init` (installs alongside the shared protocol bailian-protocol).
Shared execution protocol for the Alibaba Cloud Bailian `bl` family (consent confirmation, version preflight, authentication/installation, error reporting, local file and output conventions). Not a business entry point for user intent; read this skill when any bailian-* business skill (bailian-cli / bailian-gen / bailian-finetune / bailian-managed-agent / bailian-sandbox / bailian-memory / bailian-web-search) needs shared context before execution, or when a user's first-time installation/authentication/`bl` error needs reporting. Official installation is bundled: `bl skill init` (installed together with the business skill).
Alibaba Cloud Bailian model fine-tuning entry point: use `bl dataset` / `bl finetune` / `bl deploy` when users want to fine-tune or train their own models (fine-tune, supporting SFT / SFT-LoRA / DPO / DPO-LoRA / CPT, across text, speech, and images), validate or upload training datasets, check training progress and logs, select a checkpoint, export fine-tuned artifacts, or deploy a dedicated model as a service. The workflow is: validate data → upload to get file-id → finetune create to start a task → watch progress → export → optionally compress/quantize → deploy. An API key is required; preview write operations with `--dry-run` first. Do not trigger for fine-tuning explicitly named for Volcano Ark/ark; use bailian-model-recommend to choose a model; use bailian-gen to generate images or videos with an existing model; use bailian-cli for other Bailian resource management. Official installation: `bl skill init` (installed together with the shared protocol bailian-protocol).
Declarative infrastructure and API command entry point for Alibaba Cloud Bailian managed Agents: use `bl managed-agent` to create an agent, initialize agents.yaml, validate or preview configuration changes, create/update/destroy a managed Agent or Deployment, edit and debug directory projects in Workbench, manage local snapshot versions, query Agent/Environment/Skill/Vault/Deployment/Memory Store, manage Session/Event/File/Webhook/memory content and versions, or run/pause a Deployment. Agent/Environment/Skill/Vault/Deployment/Memory Store remain IaC with agents.yaml as the single source of truth; Webhook uses a workspace API that requires no project files. Public API capabilities expose list/get/search/versions/download, data-plane and runtime action commands by resource. apply / destroy and destructive API commands are protected by a unified high-risk confirmation gate; always show a preview before asking the user to confirm, and never add `--yes` automatically. Non-triggers: use bailian-app-call or `bl app` to call a deployed Bailian app/agent; this skill does not cover the host agent's own memory, skills, or subagents; use bailian-gen for image and video generation. Official installation: `bl skill init` (installed together with the shared protocol bailian-protocol).
Invoke the `incidentio` CLI to drive the incident.io API — incidents, actions, follow-ups, alerts/alert sources/routes, escalations & on-call schedules, catalog (types/entries/resources), custom fields, severities, incident types/roles/statuses/timestamps, status pages (including creating and managing public pages, components, layout, subscribers, templates), workflows, users, teams, API keys, heartbeats, maintenance windows, and settings. Uses the public Bearer API (OpenAPI-generated commands) plus internal dashboard (cookie) commands generated from captured HARs, hand-curated internal endpoints, and a `raw` escape hatch for any un-codified path. Use whenever a task needs incident.io data or actions, such as "list our incidents", "create an incident", "show the on-call schedule", "build or manage a status page", "list status page subscribers", "tune a dashboard setting", or "hit an internal dashboard endpoint".
Analyze AWS costs, find savings, manage budgets, evaluate Savings Plans and Reserved Instances, right-size EC2/Lambda/RDS/EBS with Compute Optimizer, look up service pricing, query CUR with Athena, detect cost anomalies, scope costs to billing views, and monitor Free Tier usage. Triggers on: AWS bill, cost analysis, reduce spend, savings plan, reserved instance, right-size, budget alert, cost optimization, pricing, free tier, cost anomaly, CUR, cost audit, billing view, billing view ARN.
Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for running model evaluations (use `agent-platform-eval-flywheel` skill).
GitHub repository operations, automation, and management. Issue triage, PR management, CI/CD operations, release management, and security monitoring using the gh CLI. Use when the user wants to manage GitHub issues, PRs, CI status, releases, contributors, stale items, or any GitHub operational task beyond simple git commands.
Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.
Authors, validates, and troubleshoots AWS CloudFormation templates. Covers template authoring with secure defaults, local validation with either cfn-lint or cloudformation-validate, cfn-guard security and compliance checks as a recommended default, account-aware CloudFormation service pre-deployment validation, CloudFormation Express mode for faster deployments, and root-cause diagnosis of failed stacks using CloudFormation events and CloudTrail correlation. Also covers author-time template intelligence with the CloudFormation Language Server and published cloudformation-validate libraries
Helps create, configure, and deploy Azure Static Web Apps using the SWA CLI. Use when deploying static sites to Azure, setting up SWA local development, configuring staticwebapp.config.json, adding Azure Functions APIs to SWA, or setting up GitHub Actions CI/CD for Static Web Apps.
Operate Railway infrastructure: sign up for or sign in to a Railway account, create projects, provision services, databases, and buckets, deploy code, configure infrastructure as code, environments and variables, manage domains, trace requests with OpenTelemetry, troubleshoot failures, check status and metrics, manage feature flags, database recovery and HA, cloud agents, usage limits, and Railway agent tooling. Use this skill whenever the user mentions Railway, feature flags, flag rollout, targeting rules, signing up, creating an account, registering, logging in, deployments, services, environments, buckets, object storage, tracing, traces, spans, OpenTelemetry, OTLP, build failures, agent setup, MCP, or infrastructure operations, even if they don't say "Railway" explicitly. Also invoke it when the user asks to be signed up, registered, or onboarded: do not refuse — drive them through the unauthed `railway up` flow (deploys + signs up on the fly) or `railway login` (which creates accounts on the fly).
Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.
Analyze Azure resource groups and generate detailed Mermaid architecture diagrams showing the relationships between individual resources. Use this skill when the user asks for a diagram of their Azure resources or help understanding how the resources relate to each other.
Comprehensive guide for writing and running Terraform tests. Use when creating test files (.tftest.hcl), writing test scenarios with run blocks, validating infrastructure behavior with assertions, mocking providers and data sources, testing module outputs and resource configurations, or troubleshooting Terraform test syntax and execution.
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
Build, modify, and ship Grafana dashboards as JSON via the HTTP API — panel types (timeseries / stat / gauge / table / heatmap / logs / traces / node-graph), `gridPos` 24-column layout, units, thresholds, template + datasource + chained variables, transformations (`organize` / `calculateField` / `filterByValue`), panel + dashboard links with `${__field.labels.x}` / `${__from}`, and Loki/Prometheus annotations. Use when scripting dashboard creation, writing the dashboard JSON for a new service, adding a `$job` dropdown variable, computing an "Error %" column with a transformation, overlaying deploys as annotations, or pushing a dashboard via `POST /api/dashboards/db` — even when the user says "create a dashboard for this metric", "add a service dropdown", "show errors as percentage", "overlay our deploys", or "export the dashboard JSON" without naming the API or schema. After every API push, verify with the returned `version` plus a GET on the dashboard UID.
Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying models to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).
Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file
Design how an API platform communicates status and incidents to external consumers - status-page modeling (the four-stage incident lifecycle, degraded/partial/major component semantics, component granularity), monitoring-driven status instead of a hand-flipped green light, pull and push channels, incident-update cadence and templates, public postmortems for a developer audience, public SLA/SLO reporting, and maintenance-window notices. Use whenever the user mentions a status page, Statuspage, incident updates, uptime or SLA reporting, postmortems, or scheduled maintenance - even if they never say "status communication". Do NOT use for deprecation and breaking-change notices - use samber/developer-platform-skills@api-versioning-policy instead.
Develop a Base44 app remotely from your own coding agent (Claude Code, claude.ai, or any MCP client) by connecting it to the Base44 sandbox. Cloud agents connect over MCP; local agents can connect over MCP or drive the same sandbox with the `base44 sandbox` CLI subcommands (the CLI uses shorter names — e.g. read_file is `sandbox read`, list_directory is `sandbox ls`, run_command is `sandbox run`). Covers connecting/ authenticating, the available sandbox tools (run_command, read_file, write_file, edit_file, grep, list_directory, create_checkpoint, list_user_apps, and the connector tools list_connectors / initiate_connector_connection), the edit→preview→verify loop, how changes persist, builder/external-agent concurrency, the in-editor "Send to Coding Agent" button + onboarding README URLs, and tips like reading the Vite dev-server logs. Triggers on "develop my Base44 app remotely", "connect Claude Code to Base44", "bring my own agent", "edit a Base44 app over MCP", "Base44 sandbox MCP", or "Send to Coding Agent".
Design how a platform surfaces integration failures to the external developers and partners who built against it - per-integration request/event/delivery logs with replay, correlation IDs that survive from response header to support ticket, error-rate aggregation per API key or app split by 4xx/5xx/429, proactive partner notification with cooldowns against alert fatigue, and a provider-side fleet view of which integrations are breaking. Use whenever the user mentions a developer-facing error dashboard, exposing request logs to integrators, alerting partners about broken integrations, or cutting integration support tickets - even if they never say "observability". Do NOT use for platform-wide incident comms - use samber/developer-platform-skills@api-status-communication instead.
Build generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing Bedrock models (Claude, Llama, Nova, Titan). Also for KB migration to Managed, prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations, chunking strategies, and Bedrock model selection. Covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend. For SageMaker model discovery, customization, or endpoints, use aws-ai-ml.
Develop a Base44 app remotely inside Base44's cloud sandbox using your own agent — no local checkout and no deploy/push commands. The implementation is remote: writing a resource file into the sandbox is what ships it (backend functions, realtime actors, entities, and agents all auto-sync from the file you write), and OAuth connectors are set up against the remote app via MCP tools or the projectless `base44 connectors` CLI. This skill is the place for learning what you can author in the sandbox, how backend functions, actors, entities, and agents are structured, and how to connect a connector without a local filesystem. Triggers on 'develop my Base44 app remotely', 'no local files', 'cloud sandbox', 'create an entity/agent remotely', 'add realtime/multiplayer/presence remotely', 'connect a connector remotely', 'bring my own agent', or any work editing a Base44 app inside a sandbox.
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.