codex

Microsoft Foundry

Skills and interactive Copilot canvas for designing, configuring, testing, deploying, evaluating, and managing Microsoft Foundry agents and models.

0 on GitHubby Microsoftv1.2.1+codex.20260803130326MITupdated 3 months agoCloud

Plugin microsoft-foundry in snowmy-tech/azure-plugins. Commands read from the repo's manifest — 1 month ago.

This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (6)

  • microsoft-foundry

    Deploy, evaluate, fine-tune, and manage Foundry agents end-to-end with azd: hosted agent scaffold/run/deploy, prompt agent create, batch eval, continuous eval, prompt optimizer, Agent Optimizer scaffold, agent.yaml, dataset curation from traces, model fine-tuning (SFT/DPO/RFT). USE FOR: azd ai agent

  • finetuning

    Fine-tune models on Azure AI Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job,

  • deploy-model

    Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deploym

  • capacity

    Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity

  • customize

    Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR:

  • preset

    Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region ca

MCP servers (3)

  • azure

    runs: npx -y @azure/mcp@latest server start

  • foundry-mcp

    connects to: https://mcp.ai.azure.com

  • microsoft-docs

    connects to: https://learn.microsoft.com/api/mcp

View source on GitHub learn.microsoft.com

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