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Found 1,587 Skills
Automates declarative resource creation and provisioning for data pipelines, supporting BigQuery, Dataform, Dataproc, BigQuery Data Transfer Service (DTS), and other resources. It manages environment-specific configurations (dev, staging, prod) through a deployment.yaml file. Use when: - Modifying or creating deployment.yaml for deployment settings. - Resolving environment-specific variables (e.g., Project IDs, Regions) for deployment. - Provisioning supported infrastructure like BigQuery datasets/tables, Dataform resources, or DTS resources via deployment.yaml. Do not use when: - Resources already exist. - Managing resources not supported by `gcloud beta orchestration-pipelines resource-types list`. - Managing general cloud infrastructure (VMs, networks, Kubernetes, IAM policies), which are better suited for Terraform. - Infrastructure spans multiple cloud providers (AWS, Azure, etc.). - Already uses Terraform for the target resources.
Set up CI/CD pipelines for Adobe App Builder projects. Generates GitHub Actions workflows using adobe/aio-cli-setup-action@3 and adobe/aio-apps-action@3.3.0, plus patterns for Azure DevOps and GitLab CI. Handles OAuth S2S secrets injection, multi-workspace promotion (stage → prod), deploy gating with manifest validation. Use this skill whenever the user mentions CI/CD for App Builder, GitHub Actions for aio deploy, automated deployment pipelines, continuous integration, continuous delivery, deploy automation, multi-environment promotion, aio app add ci, or wants to automate their App Builder build and release process. Also trigger when users mention deploy workflows, release pipelines, or GitHub secrets for App Builder.
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Queries Tilt resource status, logs, and manages dev environments. Use when checking deployment health, investigating errors, reading logs, or working with Tiltfiles.
Security audit guidelines for web applications and REST APIs based on OWASP Top 10 and web security best practices. Use when checking code for vulnerabilities, reviewing auth/authz, auditing APIs, or before production deployment.
Complete guide for building scalable microservices with Express.js including middleware patterns, routing strategies, error handling, production architecture, and deployment best practices
Creates safe rollback procedures for deployments with automated workflows, rollback runbooks, version management, and incident response. Use for "rollback automation", "deployment recovery", "incident response", or "production rollback".
Deploy prompt-based Azure AI agents from YAML definitions to Azure AI Foundry projects. Use when users want to (1) create and deploy Azure AI agents, (2) set up Azure AI infrastructure, (3) deploy AI models to Azure, or (4) test deployed agents interactively. Handles authentication, RBAC, quotas, and deployment complexities automatically.
Creates and validates Azure Resource Manager (ARM) templates for infrastructure deployment. Use when creating ARM templates, deploying Azure infrastructure as code, or validating Azure templates.
Complete guide to Kernel CLI - cloud browser platform with automation, deployment, and management
Expert ML engineering covering model development, MLOps, feature engineering, model deployment, and production ML systems.
Expert delivery management covering continuous delivery, release management, deployment coordination, and service operations.