Deploy ML models to production by generating FastAPI servers for prediction serving, Docker containers for packaging, and Kubernetes configurations for orchestration. Monitor performance with drift detection, resolve latency issues, health checks, and version conflicts in a unified workflow.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
'%20stop-opacity%3D'0.16'%2F%3E%3Cstop%20offset%3D'1'%20stop-color%3D'rgb(200%2C90%2C60)'%20stop-opacity%3D'0.03'%2F%3E%3C%2FlinearGradient%3E%3C%2Fdefs%3E%3Crect%20width%3D'320'%20height%3D'200'%20fill%3D'url(%23g)'%2F%3E%3Ccircle%20cx%3D'250'%20cy%3D'56'%20r%3D'92'%20fill%3D'rgb(200%2C90%2C60)'%20fill-opacity%3D'0.06'%2F%3E%3Ccircle%20cx%3D'64'%20cy%3D'172'%20r%3D'58'%20fill%3D'rgb(200%2C90%2C60)'%20fill-opacity%3D'0.05'%2F%3E%3C%2Fsvg%3E)
npx claudepluginhub secondsky/claude-skills --plugin model-deploymentDeploy ML models to production
jeremylongshoreUse this agent when setting up CI/CD pipelines, configuring Docker containers, deploying applications to cloud platforms, setting up Kubernetes clusters, implementing infrastructure as code, or automating deployment workflows. Examples: <example>Context: User is setting up a new project and needs deployment automation. user: "I've built a FastAPI application and need to deploy it to production with proper CI/CD" assistant: "I'll use the deployment-engineer agent to set up a complete deployment pipeline with Docker, GitHub Actions, and production-ready configurations."</example> <example>Context: User mentions containerization or deployment issues. user: "Our deployment process is manual and error-prone. We need to automate it." assistant: "Let me use the deployment-engineer agent to design an automated CI/CD pipeline that eliminates manual steps and ensures reliable deployments."</example>
devsforgeAI/ML development: LLM architecture, prompt engineering, ML ops, and NLP with production deployment focus
nodnarbnitramAutomate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
secondskyDigitalOcean cloud deployment plugin for App Platform, Droplets, Kubernetes, Functions, Managed Databases, Spaces storage, and infrastructure management with official MCP server integration
vanman2024Expert Modal.com serverless cloud platform system with comprehensive Modal 1.0 SDK (May 2025) features, GPU functions (T4/L4/A10G/L40S/A100/H100/H200/B200), autoscaler configuration, @modal.concurrent/@modal.batched decorators, Sandboxes for isolated code execution, CloudBucketMount for S3/GCS, and production deployment patterns. PROACTIVELY activate for: (1) ANY Modal.com task, (2) GPU configuration with fallbacks and multi-GPU, (3) Autoscaler settings (min/max/buffer containers, scaledown_window), (4) Web endpoints (FastAPI, ASGI, WSGI, custom servers), (5) @modal.concurrent for request concurrency, (6) @modal.batched for dynamic batching, (7) Sandboxes for untrusted code execution, (8) Scheduling (Cron with timezone, Period), (9) Storage (Volumes with commit(), Dict with TTL, Queue, CloudBucketMount), (10) Parallel processing (.map(), .starmap(), .spawn(), .for_each()), (11) Container lifecycle (@modal.enter, @modal.method, @modal.exit), (12) Image building (uv_pip_install, run_function for model downloads), (13) Secrets and environment management, (14) Deployment and CI/CD with GitHub Actions, (15) Cost optimization and 2025 pricing. Provides: Modal 1.0 stable API patterns, GPU selection guide with per-second pricing, autoscaler tuning strategies, concurrency and batching for ML inference, Sandbox security patterns, CloudBucketMount for external data, complete CLI reference, debugging workflows, and production-ready configurations.
JosiahSiegelSAP Datasphere development skill with 3 specialized agents, 5 slash commands, and validation hooks. Use when building data warehouses on SAP BTP, creating analytic models, configuring data flows and replication flows, setting up connections, managing spaces and users, implementing data access controls, or using the datasphere CLI. Covers Data Builder, Business Builder, analytic models, 40+ connection types, real-time replication, task chains, content transport, and data marketplace.
secondskySAP Cloud Application Programming Model (CAP) development skill using Capire documentation. Use when: building CAP applications, defining CDS models, implementing services, working with SAP HANA/SQLite/PostgreSQL databases, deploying to SAP BTP Cloud Foundry or Kyma, implementing Fiori UIs, handling authorization, multitenancy, or messaging. Covers CDL/CQL/CSN syntax, Node.js and Java runtimes, event handlers, OData services, and CAP plugins.
secondskyComprehensive SAC scripting skill for SAP Analytics Cloud Analytics Designer and Optimized Story Experience. This skill should be used when the user asks to "create SAC script", "debug Analytics Designer", "optimize SAC performance", "planning operations in SAC", "filter data in SAC", "use DataSource API", "chart scripting", "table manipulation", "SAC event handlers", "version management", "data locking", "Optimized Story Experience API", "OSE scripting", "OSE widget API", "OSE DataSource", "story scripting API", "OSE planning API", "OSE method", "optimized story", "SAC story scripting", "story script", "SAC scripting", or works with SAC widgets, planning models, or analytics applications.
secondskyThis skill should be used when developing SAP Analytics Cloud (SAC) planning applications, including building planning-enabled stories, analytics designer applications with planning functionality, data actions, multi actions, version management, and planning workflows. Use when creating planning models, implementing data entry forms, configuring spreading/distribution/allocation, setting up data locking, building calendar-based planning processes with approval workflows, writing JavaScript scripts for planning automation, using the getPlanning() API, PlanningModel API, or DataSource API for planning scenarios, troubleshooting planning performance issues, integrating predictive forecasting into planning workflows, implementing Seamless Planning with SAP Datasphere, configuring BPC live connections for BW on HANA integration, building value driver trees for what-if analysis, or debugging data actions with tracing.
secondskySAP BTP Cloud Integration Automation Service (CIAS) skill for guided integration workflows. Use when: setting up CIAS subscriptions, configuring destinations, assigning roles (CIASIntegrationAdministrator, CIASIntegrationExpert, CIASIntegrationMonitor), planning integration scenarios, working with My Inbox tasks, monitoring scenario execution, troubleshooting CIAS errors, creating OAuth2 instances, configuring identity providers for CIAS, understanding CIAS security architecture, or integrating SAP products (S/4HANA, SuccessFactors, BTP services, SAP Build, IBP).
secondsky