Python Appservice Deploy logo

Python Appservice Deploy

OrganizationPopular
microsoft
python-appservice-deploy

Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.

Overview

Publishermicrosoft
Repositoryazure-skills
Skill namepython-appservice-deploy
Stars
1.5K
Forks
246
Bundled files
12
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 12 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Python Appservice Deploy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/azure-skills.git /tmp/azure-skills
mkdir -p .claude/skills
cp -r /tmp/azure-skills/skills/python-appservice-deploy .claude/skills/python-appservice-deploy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Appservice Deploy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Python Appservice Deploy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Python Appservice Deploy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Python on Azure App Service — Code Deploy

Deploys Python (Flask, Django, FastAPI, generic) code to Azure App Service Linux (P0v3, Python 3.14). Creates RG + Plan + Web App if missing. Hand off to azure-prepare for VNet, Key Vault, databases, or IaC.

MCP tools used: mcp_azure_mcp_subscription_list, mcp_azure_mcp_group_list, mcp_azure_mcp_appservice, mcp_azure_mcp_azd (when azure.yaml is present).

Workflow

  1. Resolve context — smart defaults, minimal prompts. Only the app name is interactive; RG (<app>-rg), Plan (<app>-plan), region (current az default or eastus2), subscription are derived. create-app.md §1.
  2. Detect framework (advisory, never blocks). detect.md.
  3. Choose path — azure.yaml host: appservice → deploy-azd.md; else deploy-azcli.md.
  4. Ensure RG → Plan (P0v3 --is-linux) → Web App (--runtime "PYTHON:3.14") exist. On transient ARM errors, follow transient-retry.md. create-app.md.
  5. Set startup — Flask/Django: none (Oryx auto-detects). FastAPI: always python -m uvicorn main:app --host 0.0.0.0. Other: warn. startup-commands.md.
  6. Set SCM_DO_BUILD_DURING_DEPLOYMENT=true.
  7. Deploy — azd deploy or az webapp deploy --type zip --track-status false.
  8. STOP. Print the post-deploy message (post-deploy-message.md) and end the turn.

Hard rules

  • ⛔ NO POST-DEPLOY VERIFICATION — after deploy returns, do not run az webapp log tail, curl, Invoke-WebRequest, or any health probe. App Service needs 2–3 min to warm; a quiet log or early 5xx is not failure.
  • ⛔ SHELL SAFETY — for --runtime always use "PYTHON:3.14" (colon). Never "PYTHON|3.14" (pipe is a shell operator).
  • ⛔ NEVER az webapp up — deprecated. Use Step 7 commands.
  • ✅ URL FORMAT — present endpoints as https://... URLs.

Error Handling

See errors.md for the full symptom → cause → fix matrix. Quick triage: missing plan/app → re-run Step 4; container ping timeout on 8000 → fix startup (Step 5); ModuleNotFoundError after deploy → ensure Step 6 ran, redeploy.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Python Appservice Deploy AI skill do?

Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.

Why use Python Appservice Deploy on TypingMind?

Because you install it once and use it with any model. Python Appservice Deploy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Python Appservice Deploy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/azure-skills/tree/main/skills/python-appservice-deploy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Python Appservice Deploy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Python Appservice Deploy?

As many as you like. As long as a model supports skills, you can use Python Appservice Deploy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Python Appservice Deploy AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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