Skill UI

Automates guidance for deploying machine learning models with quantization, covering tooling, best practices, and production readiness for inference, MLops, and monitoring tasks.

Automates guidance for model registry manager tasks within ML deployment, covering serving, MLOps pipelines, monitoring, and production optimization whenever that trigger phrase appears.

High-level PyTorch Lightning streamlines training loops with automatic distributed support (DDP/FSDP/DeepSpeed), callbacks, checkpointing, logging, and device management so you can scale from a laptop to multi-node clusters without boilerplate.

A comprehensive guide for configuring and managing organizational access control in Replit Enterprise. This skill covers defining role hierarchies (Owner, Admin, Manager, Editor, Viewer), implementing custom groups, integrating Single Sign-On (SSO/SAML), restricting deployment permissions (Production/Staging), and setting up detailed audit logging. Use this when securing a large development team, setting up strict security policies, or performing quarterly access reviews.

Guides production ML engineering with patterns for deploying models, building MLOps pipelines, monitoring drift, integrating LLMs and RAG systems, and enforcing feature-store, retraining, and cost controls in live environments.

Automates Snowflake data warehouse workflows via the Composio MCP, letting you list databases, schemas, and tables, run SQL (SELECT, DDL, DML), and integrate those operations into cross-app pipelines for faster data ops.

Automates guidance for streaming inference setup, covering best practices in ML deployment, serving, monitoring, and production-ready configurations whenever you ask about streaming inference patterns or implementation.

Automates generating TorchServe deployment configurations with guided best practices for model serving, MLOps pipelines, inference, and production monitoring so ML deployment workflows stay consistent and reliable.

Automates Triton inference config guidance for ML deployment, covering serving, MLOps pipelines, monitoring, and production-ready configurations aligned with best practices.

Automates Vertex AI deployment tasks, offering step-by-step guidance, production-ready code, and validation for model serving, MLOps pipelines, monitoring, and optimization when you mention Vertex AI Deployer.

Automated guidance for YAML configuration validation tasks within the DevOps Basics domain, covering version control, containers, CI/CD, and infrastructure fundamentals while producing production-ready code and configurations.

This comprehensive toolkit provides 23 production-ready, specialized engineering skills covering the entire software development lifecycle (SDLC). Expertise areas include full-stack development, system architecture, DevOps, cybersecurity, AI/ML, and data engineering. It is designed to guide advanced AI agents (like Claude or Codex) through complex, multi-disciplinary development tasks, ensuring high code quality and robust system design.