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/microsoft-foundry

@04110d9
by microsoftmicrosoft/skills3.1k stars
351

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

Use this Skill: https://skilld.dev/gh/microsoft/skills/microsoft-foundry

This session only. Nothing lands on disk.

modelsdeploy-modelpresetEXAMPLES.md

≈748 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Examples: preset

Example 1: Fast Path — Current Region Has Capacity

Scenario: Deploy gpt-4o to project in East US, which has capacity. Result: Deployed in ~45s. No region selection needed. 100K TPM default, GlobalStandard SKU.

Example 2: Alternative Region — No Capacity in Current Region

Scenario: Deploy gpt-4-turbo to dev project in West US 2 (no capacity). Result: Queried all regions → user selected East US 2 (120K available) → deployed in ~2 min.

Example 3: Create New Project in Optimal Region

Scenario: Deploy gpt-4o-mini in Europe for data residency; no existing European project. Result: Created AI Services hub + project in Sweden Central → deployed in ~4 min with 150K TPM.

Example 4: Insufficient Quota Everywhere

Scenario: Deploy gpt-4 but all regions have exhausted quota. Result: Graceful failure with actionable guidance:

  1. Request quota increase via the quota skill
  2. List existing deployments consuming quota
  3. Suggest alternative models (gpt-4o, gpt-4o-mini)

Example 5: First-Time User — No Project

Scenario: Deploy gpt-4o with no existing Microsoft Foundry project. Result: Full onboarding in ~5 min — created resource group, AI Services hub, project, then deployed.

Example 6: Deployment Name Conflict

Scenario: Auto-generated deployment name already exists. Result: Appended random hex suffix (e.g., -7b9e) and retried automatically.

Example 7: Multi-Version Model Selection

Scenario: Deploy "latest gpt-4o" when multiple versions exist. Result: Latest stable version auto-selected. Capacity aggregated across versions.

Example 8: Anthropic Model (claude-sonnet-4-6)

Scenario: Deploy claude-sonnet-4-6 (Anthropic model requiring modelProviderData). Result: User prompted for industry selection → tenant country code and org name fetched automatically → deployed via ARM REST API with modelProviderData payload in ~2 min. Capacity set to 1 (MaaS billing).


Summary of Scenarios

Scenario Duration Key Features
1: Fast Path ~45s Current region has capacity, direct deploy
2: Alt Region ~2m Region selection, project switch
3: New Project ~4m Project creation in optimal region
4: No Quota N/A Graceful failure, actionable guidance
5: First-Time ~5m Complete onboarding
6: Name Conflict ~1m Auto-retry with suffix
7: Multi-Version ~1m Latest version auto-selected
8: Anthropic ~2m Industry prompt, tenant info, REST API deploy

Common Patterns

A: Quick Deploy     Auth → Get Project → Check Region (✓) → Deploy
B: Region Select    Auth → Get Project → Region (✗) → Query All → Select → Deploy
C: Full Onboarding  Auth → No Projects → Create Project → Deploy
D: Error Recovery   Deploy (✗) → Analyze → Fix → Retry

Source: SKILL.md on GitHub

2 warnings3d4 checks · Risk SAFE
  • Gen Agent Trust Hub3d

    This skill provides a comprehensive environment for managing the end-to-end lifecycle of AI agents, models, and infrastructure on Microsoft Foundry. It includes sub-skills for deployment, evaluation, fine-tuning, and troubleshooting. The skill utilizes dynamic code execution and shell command wrappers, which are used within the context of local development and cloud orchestration. All external resources and dependencies originate from trusted organizations and well-known services.

  • Socket3d

    2 alerts: gptSecurity, gptAnomaly

  • Snyk3d

    Risk: LOW · No issues

  • Runlayer7mo

    36/36 files flagged

Signed by skilld at 04110d9. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated last week
metadata
{
  "author": "Microsoft",
  "version": "1.2.26"
}

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