Azure Architecture Autopilot
github/awesome-copilot
Designs Azure infrastructure from a natural-language description, or diagrams an existing resource group, then refines the design through conversation and deploys it with Bicep.
Unified Azure OpenAI model deployment skill with intelligent intent-based routing.
Install Deploy Model
$ npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install microsoft/GitHub-Copilot-for-Azure deploy-model --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/microsoft/GitHub-Copilot-for-Azure.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model .claude/skills/deploy-model && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "deploy-model" agent skill from https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model into .claude/skills/deploy-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-model", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-skills/skills/microsoft-foundry/models/deploy-modelType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install microsoft/GitHub-Copilot-for-Azure deploy-model --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/GitHub-Copilot-for-Azure.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model .agents/skills/deploy-model && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deploy-model" agent skill from https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model into .agents/skills/deploy-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-model", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install microsoft/GitHub-Copilot-for-Azure deploy-model --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/GitHub-Copilot-for-Azure.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model .cursor/skills/deploy-model && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deploy-model" agent skill from https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model into .cursor/skills/deploy-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-model", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/microsoft/GitHub-Copilot-for-Azure.git --path plugins/azure-skills/skills/microsoft-foundry/models/deploy-model--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install microsoft/GitHub-Copilot-for-Azure deploy-model --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/GitHub-Copilot-for-Azure.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model .gemini/skills/deploy-model && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "deploy-model" agent skill from https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model into .gemini/skills/deploy-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-model", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install microsoft/GitHub-Copilot-for-Azure deploy-modelInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/microsoft/GitHub-Copilot-for-Azure.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model .github/skills/deploy-model && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "deploy-model" agent skill from https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model into .github/skills/deploy-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-model", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install microsoft/GitHub-Copilot-for-Azure deploy-model --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/GitHub-Copilot-for-Azure.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model .opencode/skills/deploy-model && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deploy-model" agent skill from https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model into .opencode/skills/deploy-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-model", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
deploy-model- GitHub stars
- 255
- Used in
- 1 other repo
- Token cost
- ~1.8k tokens
- SKILL.md length
- 619 words
- Files
- 4 (incl. scripts)
- Skills in repo
- 56
- Repo updated
- First seen
- Licence
- MIT
At a glance
Unified Azure OpenAI model deployment skill with intelligent intent-based routing.
- Works in 4 steps: Run Capacity Discovery to find… → Present findings to user → Ask: "Would you like to deploy with… → …
- Create deployment
- SKILL.md covers Quick Reference, Intent Detection, Project Selection (All Modes) and Pre-Deployment Validation (All…, plus 2 more sections
- Runs PowerShell and Shell scripts from its folder; calls az
What it does
Deploy Model is an agent skill from microsoft/GitHub-Copilot-for-Azure, published by the product's own GitHub organization. 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 deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use…
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `TEST_PROMPTS.md` and `scripts/generate_deployment_url.sh`).
It sits in DevOps & Cloud, covering Deployment. It works with Microsoft Azure, Azure OpenAI and OpenAI. The repository describes itself as: GitHub Copilot for Azure. The licence is MIT.
When your agent uses it
- Create deployment
- Model deployment
- Deploy openai model
- Provision model
Example prompts
- “/deploy-model”
Requirements
- A Bash shell
- PowerShell
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Run Capacity Discovery to find regions/projects with sufficient quota
- Present findings to user
- Ask: "Would you like to deploy with quick defaults or customize settings?"
- Route to Preset or Customize based on answer
What it can do on your machine
Read from SKILL.md and the folder at commit d8f4f4e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (PowerShell and Shell), which the agent can run.
Shell commands in SKILL.md call:
az
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use az, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Deploy Model loads about 1.8k tokens when it runs. Until then it costs about 165 tokens; SKILL.md has 619 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from microsoft/GitHub-Copilot-for-Azure at commit d8f4f4e, republished under its MIT licence (© microsoft). 619 words, ~1,798 tokens.
Download SKILL.mdSave it as
.claude/skills/deploy-model/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- deploy-model
- description
- 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 deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
- license
- MIT
- metadata.author
- Microsoft
- metadata.version
- 1.0.0
Deploy Model
Scope — read this first. This skill creates model deployments out-of-band via Azure CLI / MCP / portal. For azd-managed Foundry projects (those scaffolded from
azd ai agent init), declare deployments inazure.yaml services.ai-project.deployments[]instead —azd ai agent initwrites the entry from the sample manifest andazd provisioncreates the deployment through Bicep. See foundry-agent/create/create-hosted.md for the Golden Path. Use this skill only for: (a) Foundry projects not managed by an azd project, (b) ad-hoc deployments outside the azd lifecycle.
Unified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.
Quick Reference
| Mode | When to Use | Sub-Skill |
|---|---|---|
| Preset | Quick deployment, no customization needed | preset/SKILL.md |
| Customize | Full control: version, SKU, capacity, RAI policy | customize/SKILL.md |
| Capacity Discovery | Find where you can deploy with specific capacity | capacity/SKILL.md |
Intent Detection
Analyze the user's prompt and route to the correct mode:
User Prompt
│
├─ Simple deployment (no modifiers)
│ "deploy gpt-4o", "set up a model"
│ └─> PRESET mode
│
├─ Customization keywords present
│ "custom settings", "choose version", "select SKU",
│ "set capacity to X", "configure content filter",
│ "PTU deployment", "with specific quota"
│ └─> CUSTOMIZE mode
│
├─ Capacity/availability query
│ "find where I can deploy", "check capacity",
│ "which region has X capacity", "best region for 10K TPM",
│ "where is this model available"
│ └─> CAPACITY DISCOVERY mode
│
└─ Ambiguous (has capacity target + deploy intent)
"deploy gpt-4o with 10K capacity to best region"
└─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZERouting Rules
| Signal in Prompt | Route To | Reason |
|---|---|---|
| Just model name, no options | Preset | User wants quick deployment |
| "custom", "configure", "choose", "select" | Customize | User wants control |
| "find", "check", "where", "which region", "available" | Capacity | User wants discovery |
| Specific capacity number + "best region" | Capacity → Preset | Discover then deploy quickly |
| Specific capacity number + "custom" keywords | Capacity → Customize | Discover then deploy with options |
| "PTU", "provisioned throughput" | Customize | PTU requires SKU selection |
| "optimal region", "best region" (no capacity target) | Preset | Region optimization is preset's specialty |
Multi-Mode Chaining
Some prompts require two modes in sequence:
Pattern: Capacity → Deploy When a user specifies a capacity requirement AND wants deployment:
- Run Capacity Discovery to find regions/projects with sufficient quota
- Present findings to user
- Ask: "Would you like to deploy with quick defaults or customize settings?"
- Route to Preset or Customize based on answer
💡 Tip: If unsure which mode the user wants, default to Preset (quick deployment). Users who want customization will typically use explicit keywords like "custom", "configure", or "with specific settings".
Project Selection (All Modes)
Before any deployment, resolve which project to deploy to. This applies to all modes (preset, customize, and after capacity discovery).
Resolution Order
- Check
PROJECT_RESOURCE_IDenv var — if set, use it as the default - Check user prompt — if user named a specific project or region, use that
- If neither — query the user's projects and suggest the current one
Show full SKILL.md (263 more words)Show less
Confirmation Step (Required)
Always confirm the target before deploying. Show the user what will be used and give them a chance to change it:
Deploying to:
Project: <project-name>
Region: <region>
Resource: <resource-group>
Is this correct? Or choose a different project:
1. ✅ Yes, deploy here (default)
2. 📋 Show me other projects in this region
3. 🌍 Choose a different regionIf user picks option 2, show top 5 projects in that region:
Projects in <region>:
1. project-alpha (rg-alpha)
2. project-beta (rg-beta)
3. project-gamma (rg-gamma)
...⚠️ Never deploy without showing the user which project will be used. This prevents accidental deployments to the wrong resource.
Pre-Deployment Validation (All Modes)
Before presenting any deployment options (SKU, capacity), always validate both of these:
Model supports the SKU — query the model catalog to confirm the selected model+version supports the target SKU:
bash az cognitiveservices model list --location <region> --subscription <sub-id> -o jsonFilter for the model, extract
.model.skus[].nameto get supported SKUs.Subscription has available quota — check that the user's subscription has unallocated quota for the SKU+model combination:
bash az cognitiveservices usage list --location <region> --subscription <sub-id> -o jsonMatch by usage name pattern
OpenAI.<SKU>.<model-name>(e.g.,OpenAI.GlobalStandard.gpt-4o). Computeavailable = limit - currentValue.
⚠️ Warning: Only present options that pass both checks. Do NOT show hardcoded SKU lists — always query dynamically. SKUs with 0 available quota should be shown as ❌ informational items, not selectable options.
💡 Quota management: For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the quota skill instead of duplicating that guidance inline.
Prerequisites
All deployment modes require:
- Azure CLI installed and authenticated (
az login) - Active Azure subscription with deployment permissions
- Microsoft Foundry project resource ID (or agent will help discover it via
PROJECT_RESOURCE_IDenv var)
Sub-Skills
- preset/SKILL.md — Quick deployment to optimal region with sensible defaults
- customize/SKILL.md — Interactive guided flow with full configuration control
- capacity/SKILL.md — Discover available capacity across regions and projects
© microsoft, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (scripts) in plugins/azure-skills/skills/microsoft-foundry/models/deploy-model of microsoft/GitHub-Copilot-for-Azure.
- SKILL.md
- TEST_PROMPTS.md
- scripts/generate_deployment_url.ps1
- scripts/generate_deployment_url.sh
Open the folder on GitHubat commit d8f4f4e
Used in 3 other repositories
We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in microsoft/GitHub-Copilot-for-Azure, which our catalogue first saw on October 7, 2026.
- microsoft/azure-skillsskills/microsoft-foundry/models/deploy-model.github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model
- microsoft/skills.github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model
- aiskillstore/marketplaceskills/microsoft/deploy-modelskills/microsoft/microsoft-foundry/models/deploy-model
Compare with similar skills
Deploy Model next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Deploy Model this skillmicrosoft/GitHub-Copilot-for-Azure | 255 | 1 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Azure Architecture Autopilotgithub/awesome-copilot | 40k | 1 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Letta Configurationletta-ai/skills | 147 | — | ~1.3k | Automated safety check: Notes | MIT | |
| Azure AI Deploytimothywarner-org/claude-code | 224 | — | ~731 | Automated safety check: Notes | MIT | |
| Azure AI Projects TSmicrosoft/skills | 3.1k | 6 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Azure Video IndexerMicrosoftDocs/Agent-Skills | 775 | — | ~2.4k | Automated safety check: Pass | CC-BY-4.0 |
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Questions about Deploy Model
What does Deploy Model do?
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Deploy Model is an agent skill from microsoft/GitHub-Copilot-for-Azure, published by the product's own GitHub organization. Unified Azure OpenAI model deployment skill with intelligent intent-based routing.
When should I use Deploy Model?
Deploy Model fits situations like: create deployment; model deployment; deploy openai model; provision model.
How do I install Deploy Model in Claude Code?
Run `npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a claude-code`. Or copy the skill folder (plugins/azure-skills/skills/microsoft-foundry/models/deploy-model in microsoft/GitHub-Copilot-for-Azure) into .claude/skills/deploy-model in your project. Claude Code loads it when a task matches its description.
How do I install Deploy Model in Codex?
Run `npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a codex`. Or copy the skill folder (plugins/azure-skills/skills/microsoft-foundry/models/deploy-model in microsoft/GitHub-Copilot-for-Azure) into .agents/skills/deploy-model in your project. Codex loads it when a task matches its description.
Can I use Deploy Model in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add microsoft/GitHub-Copilot-for-Azure --skill deploy-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deploy-model, .gemini/skills/deploy-model, .github/skills/deploy-model and .opencode/skills/deploy-model in your project.
What does Deploy Model need to run?
Going by SKILL.md and its folder, Deploy Model needs PowerShell and a shell for the scripts in its folder and the command-line tools its instructions call (az). Our summary lists: A Bash shell; PowerShell.
Does Deploy Model access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Deploy Model safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Deploy Model use?
Deploy Model is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Deploy Model use?
About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Deploy Model?
Skills that share tags, products or a category with Deploy Model: Azure Architecture Autopilot (github/awesome-copilot, 40k stars), Letta Configuration (letta-ai/skills, 147 stars), Azure AI Deploy (timothywarner-org/claude-code, 224 stars) and Azure AI Projects TS (microsoft/skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Deploy Model?
microsoft (a GitHub organization, an official publisher) maintains it in microsoft/GitHub-Copilot-for-Azure, which has 255 GitHub stars. The repository holds 56 skills in this directory. The repository was last updated on October 7, 2026.
Source: microsoft/GitHub-Copilot-for-Azure on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.