Skill UI

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.

Use the composio CLI to orchestrate complex, end-to-end product releases across multiple services: Stripe for billing updates, Supabase for database migrations, and Vercel for frontend deployments. This single pipeline ensures that billing models, database schemas, and user interfaces are consistently updated in the correct sequence, guaranteeing reliable full-stack product launches.

Expert guidance for the Fabric CLI (fab) and REST APIs covering workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment, and admin, including capacity unit cost estimation.

Provides a comprehensive CLI reference for Tabular Editor 2 (TE2). It enables automated model management, including deployment to XMLA endpoints, running embedded scripts, performing Best Practice Analysis (BPA), and handling model exports (TMDL). This skill is essential for integrating data model lifecycle management into CI/CD pipelines and automating repetitive tasks from the command line.

A comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

A comprehensive white-box security auditing workflow designed for proprietary source code. It integrates static analysis security testing (SAST) tools like Semgrep and CodeQL to detect common vulnerabilities (e.g., injection, IDOR, crypto misuse), followed by mandatory manual threat modeling and verification steps. Applicable for PR reviews and pre-deployment security checks.

A high-level TypeScript SDK for managing Azure AI Foundry projects, enabling creation of AI agents, model deployments, datasets, and evaluations. Supports various tools like code interpreter, search, and MCP for building intelligent applications.

This skill helps developers apply Domain-Driven Design tactical patterns to structure code effectively. It involves using entities, value objects, aggregates, repositories, and domain events to enforce explicit invariants. Use this when translating domain rules into code structures or refactoring anemic models. It focuses on design boundaries and behavior-rich objects rather than strategic boundaries or deployment architecture.

This skill creates comprehensive technical documentation from existing codebases by analyzing architecture, design patterns, and implementation details. It produces long-form technical manuals and ebooks that explain system boundaries, key components, and interactions. The output includes design rationale, data models, and deployment considerations, suitable for onboarding new team members, architectural reviews, and long-term maintenance of complex software systems.

Establish model registry standards, governance controls, metadata schemas, approvals, and lifecycle policies for enterprise AI deployments. Includes MLflow setup, required metadata schema, and registration scripts for traceability and policy-driven promotion.

Deploy ML models on Kubernetes with KServe and NVIDIA Triton Inference Server. Covers canary deployments, autoscaling, GPU-aware scheduling, and traffic splitting for production inference workloads.