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Found 259 Skills
Comprehensive Cloud Foundry CLI (cf CLI v8) assistant for generating commands, automating app deployments, managing services, and troubleshooting errors. Use this skill whenever the user mentions "cf", "cf cli", "Cloud Foundry", "cf push", "cf login", "cf target", app deployment to CF, service binding, buildpacks, routes, domains, orgs and spaces, manifests, or any task involving the Cloud Foundry command line — even if they just say something like "push my app" or "how do I bind a service". Also trigger when the user asks about scaling apps, managing environment variables in CF, creating user-provided services, rolling deployments, SSH into app containers, or writing CF manifests.
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).
Tests Amazon MWAA workflow execution end-to-end: trigger a run and monitor 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 terminal state, and on failure delegates diagnosis to debugging-mwaa-workflow and artifact/redeploy 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 for diagnosing why a run failed or root-causing an error (handled by debugging-mwaa-workflow).
Sets up CloudWatch Application Observability (also called Omni) for the first time. Covers creating an Omni Space or Domain; configuring and listing Omni access grants (who has access and at what level) and access profiles bounding what async alerts, integrations, or agents can do; instrumenting an application or AI agent with the ADOT SDK so traces reach CloudWatch Omni (Python/Node/Java/.NET on EC2/ECS/EKS/Lambda) — CloudWatch Omni only; for Application Signals (auto-instrumentation, monitored service, ServiceEvents, reporting telemetry) use aws-observability — including no-image-rebuild and .NET CoreCLR profiler env vars; ingesting Azure telemetry via the CloudWatch agent on an Azure VM or AKS cluster; connecting Slack to a Space (and whether GitHub can be connected); and registering a custom MCP tool server over HTTP or stdio and choosing its auth (API key, bearer, or OAuth2). For using a Space already set up — querying, dashboards, Omni alerts, or defining Omni resources as code — use aws-observability.
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Comprehensive SAP BTP command line interface (btp CLI) assistant for generating commands, automating multi-step workflows, and troubleshooting errors. Use this skill whenever the user mentions "btp", "BTP CLI", "SAP BTP command line", subaccount management via CLI, entitlement assignment via terminal, service instance creation with btp, role collection assignment from the command line, or any task involving the SAP Business Technology Platform CLI — even if they just say something like "set up my BTP subaccount" or "how do I log in to my global account from the terminal". Also trigger when the user asks about scripting or automating SAP BTP operations, parsing btp output with jq, or managing Cloud Foundry / Kyma environments through the btp CLI.
Writes and curates business metadata in an AWS data catalog: the Amazon SageMaker Catalog (Amazon DataZone), or the AWS Glue Data Catalog. Detects which catalog platform an account uses before writing. Triggers on: add a description to a table, document a dataset, write a README, set an owner, add column comments, tag with a glossary term, create a business glossary, create a metadata form, publish an asset, create a data product, run the data source crawl, delete a catalog asset, attach a conventions document to a table as a catalog skill asset, which assets in my project are missing descriptions or owners and what to fix first, which catalog does this account use and what can I do with it. Not for: inventorying or listing tables (use exploring-data-catalog), locating one table (use finding-data-lake-assets), counting gaps domain-wide with SQL over the exported metadata snapshot (use querying-aws-sagemaker-catalog), or granting or revoking access (use managing-data-asset-access)
Grants, requests, audits and removes access to data assets in an AWS data catalog (the Amazon SageMaker Catalog supported by Amazon DataZone) through catalog subscriptions over published listings. Covers requesting a subscription, approving or rejecting requests with a recorded decision, sharing a listing with a project, revoking one or many subscriptions, and confirming grant settlement. Triggers on: give another team access to a table, request access to a dataset, approve or reject an access request, share a listing with a project, who has access to this table, revoke access, bulk revoke, is the access actually gone, subscription grant status. Not for: IAM roles, policies or STS (use aws-iam), S3 bucket policies (use securing-s3-buckets), Clean Rooms permissions (use aws-cleanrooms), or writing catalog metadata, glossaries and forms, publishing listings, or retiring or deleting an asset that other teams are subscribed to and the order of operations that needs (use managing-data-catalog)
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Deploys, queries, and debugs AWS Marketplace usage-based (PAYG) metering — the pipeline (ResolveCustomer, BatchMeterUsage, EventBridge via SAM) and querying/debugging metering records, statuses, failures, discrepancies. Covers Concurrent Agreements (CustomerAWSAccountId + LicenseArn), multi-product, CloudTrail Event History (90 days; never CloudTrail Lake), Seller Reports (Billed Revenue/Usage columns + CSV/SDDS parsing), existing-stack detection, metering region for BatchMeterUsage/MeterUsage (DisabledApiException), metered AMI/Container + IAM roles, where registration data lives (customer-profile table), legacy ProductCode + CustomerIdentifier, DuplicateRecord/CustomerNotSubscribed, bad LicenseArn. Also SCOPES adjacent asks — flat-rate/SaaS-Subscription "do I still meter", CCP included-units + overage, contract term/annual renewal, forecasting future bills/usage — with the right answer or a one-line redirect. Use for any BatchMeterUsage/ResolveCustomer or SaaS usage-billing question.
Migrate workloads from Microsoft Azure to AWS. Triggers on: migrate from Azure, Azure to AWS, move off Azure, migrate AKS to EKS, migrate App Service or Azure VMs to AWS compute, migrate Azure SQL or Azure Database to RDS, migrate Cosmos DB to DynamoDB, migrate Azure OpenAI to Bedrock, move Azure AI or agentic workloads to AWS, estimate AWS costs for my Azure infrastructure, what-if workshop. Runs a 6-phase process: discover Azure resources from Terraform, the live `az` CLI, app code, and billing exports, then clarify, design, estimate costs, optionally reprice scenarios, generate artifacts, and collect feedback. Clarify gates Design, Estimate, and Generate; Generate is opt-in at the post-Estimate decision gate. Bicep/ARM discovery is not yet implemented; a Bicep/ARM-only workspace halts. Do not use for: GCP migrations (see gcp-to-aws), Heroku migrations (see heroku-to-aws), general AWS architecture advice (see architect-for-startups), AWS-to-Azure reverse migration, or Azure-to-Azure refactoring.
Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.