Cloud, CI/CD, monitoring, and deployment (6.9K skills)
Assemble a reproducible Alchemy incident bundle with sanitized environment details, request IDs, timing, status evidence, and reproduction results. It helps teams prepare support evidence while screening out credentials and sensitive customer data before sharing.
Collect evidence for reproducible Algolia search or indexing failures, including client versions, reproduction steps, and redacted request details. Package the findings with a file manifest and secret scan results for review before a support handoff.
Design Alchemy traffic controls from observed account usage and workload patterns. This skill helps set shared admission limits, bounded retries, and load tests so teams can handle rate limits, protect critical requests, and define clear degraded behavior.
Review an Adobe integration before launch or a major change. This skill checks authentication, entitlements, API versions, data handling, asynchronous reliability, spending, monitoring, and rollback, then records a go, conditional go, or no-go decision with evidence.
Plan and verify separation between Adobe development, staging, and production environments. Produce an environment matrix, configuration manifest, isolation tests, and release controls covering credentials, storage, events, budgets, and ownership before making live changes.
Triage Adobe integration incidents across authentication, products, jobs, events, storage, spending, and App Builder. It helps classify the failure, preserve evidence, contain impact, test recovery with a bounded canary, and record customer impact and follow-up actions.
Plan controlled load tests for Adobe services and establish a conservative operating capacity. Measure queues, completion rates, errors, costs, and output correctness, then document scaling controls, emergency stop conditions, and evidence specific to the tested environment.
Deploy Adobe-backed services or App Builder apps with separate environments, verified build artifacts, and limited trial traffic. The workflow checks monitoring and rollback readiness, then records evidence for release decisions.
Design service-specific controls for Adobe workloads, including request admission, Retry-After handling, capped retries, and queues with spend limits. It helps respond to 429 errors and scaling pressure using verified service constraints and test evidence.
Plan capacity, queues, and retry rules for an Abridge integration using tenant-specific contracts and verified responses. Define how to handle overload, prevent duplicate clinical actions, and hand off work when processing cannot continue.
Review Adobe integrations for approved endpoints, versions, data handling, spending limits, and approval requirements. Produce clear allow and deny rules, enforcement points, tests, and evidence so teams can check risky actions before they run.
Build and deploy Google ADK agents to Vertex AI Agent Engine. Define sequential, parallel, or looping workflows and A2A interfaces for agent communication, then verify endpoints and add logging and retry behavior.
Build and validate Terraform for Vertex AI ADK infrastructure, including Agent Engine, networking, IAM permissions, code execution, and Memory Bank. It helps produce deployment settings and a checklist for security requirements such as VPC Service Controls.
Design recovery controls for asynchronous Adobe workloads, including duplicate prevention, time-limited status checks, service-specific queues, and failure isolation. Produce state models and recovery runbooks that help teams investigate uncertain jobs before approving a replay.
Design monitoring for Adobe integrations without logging secrets or content. Define structured logs, connect requests to jobs, establish service health measures, and prepare alerts, response procedures, and evidence retention checks.
Prepare an Abridge implementation by mapping tenant access, identity owners, roles, and EHR integration requirements to approved evidence. The result is a readiness inventory with verified facts, unresolved gaps, and actions that still need approval.
Choose an Adobe authentication flow based on who owns the data, confirm the required entitlement and product profile, and document secret handling. The workflow yields a reviewable setup plan and a read-only proof before integration work proceeds.
Use KubeKey’s `kk` commands to create and configure Kubernetes clusters, add or remove nodes, renew certificates, and manage offline installation artifacts. The skill explains inventory and config files, common workflows, prechecks, and troubleshooting guidance.
Turn authorized external reconnaissance into ongoing monitoring. Track changes in assets and findings across scans, watch public sources for adversary chatter, manage finding states, and send threshold-based webhook alerts with retry guidance and scheduled-run examples.
Prepare and review software releases by checking the Release Please pull request, version, changelog, and draft files. It guides verification and the manual publishing workflow, keeping release publication tied to explicit authorization.
Prepare OpenEnv environments for deployment to Hugging Face Spaces, check that the private Spaces work at runtime, and manage versioned collections and canonical references. It guides release decisions using build and usability checks.
Deploy an OpenEnv environment to Hugging Face Spaces with the OpenEnv CLI. Configure options such as visibility, web interface, base image, or hardware, then report the Space URL and check deployment health after the build completes.
Diagnose OpenEnv deployments on Hugging Face Spaces, identify build or runtime problems, and guide targeted recovery. It also covers checking whether a Space is healthy and reconciling release collection membership after redeployments.
Prepare Benny’s configuration for Slack-based bug triage and issue reproduction workflows. It guides setup of repository, tracker, routing, control, model, and budget choices, then checks the integrations and safeguards needed before creating or updating Cursor automations.