create-workflow-python

name	create-workflow-python
description	This skill creates a Dapr workflow application in Python. Use this skill when the user asks to "create a workflow in Python", "write a Python workflow application" or "build a workflow app in Python".
allowed-tools	["Bash(uv venv:*)","Bash(uv sync:*)","Bash(dapr:*)","mcp__ide__getDiagnostics"]

Create Dapr Workflow Python Application

Overview

This skill describes how to create a Dapr Workflow application using Python.

Execution Order

You MUST follow these phases in strict order:

1. Check specification - Check if the user specified what needs to be built.
2. Project Setup — Create all files and folders.
3. Verify — Verify that the project builds.
4. Create README.md — Create a readme that summarizes what is built and how to run & test the application. Do not provide instructions at the end of this phase.
5. Show final message - Your LAST output MUST be EXACTLY the message defined in the ## Show final message section. Do NOT add any other text, summary, or commentary after it.

Check specification

If you don't have enough context what to build, ask the user the following clarifying questions one by one using an interview style:

1. What is the purpose of the workflow application?
2. Describe how workflow should work. Which patterns should be used?
3. Specify the input and output objects of the workflow.
4. What's the name of this project? This will be used as the folder name. Don't use any spaces in this name.

Prerequisites

The following must be installed by the user before this skill can run:

• Python 3.12+
• uv (Astral)
• Docker or Podman
• Dapr CLI

Additional runtime dependencies (handled during project setup):

• Python package: dapr-ext-workflow version 1.18.0 (pulls in dapr 1.18.0)
• Start the Diagrid Dev Dashboard: docker run -p 8080:8080 ghcr.io/diagridio/diagrid-dashboard:latest

Project Setup

Create the project root folder inside the current location where the terminal is open:

mkdir <ProjectRoot>
cd <ProjectRoot>

The should start with the and end with -app: -app.

Folder structure

<ProjectRoot>/
├── .gitignore
├── dapr.yaml
├── local.http
├── resources/
│   └── statestore.yaml
└── <ProjectName>/
    ├── pyproject.toml
    ├── main.py
    ├── models.py
    ├── runtime.py
    ├── workflow.py
    └── activities.py

.gitignore

Python style .gitignore file in the project root. See REFERENCE.md for full example.

dapr.yaml

Multi-app run file in the project root. Configures the Dapr sidecar and points to the resources folder. See REFERENCE.md for full example and key points.

resources/statestore.yaml

Dapr Workflow requires a state store component (with actorStateStore set to "true"). See REFERENCE.md for full example and key points.

pyproject.toml

Python configuration file used by packaging tools. See REFERENCE.md for full example.

main.py

Main entry for the Python workflow application. See REFERENCE.md for full example.

Models

Pydantic types for workflow and activity input/output, placed in a models.py file. Models must be serializable since Dapr persists workflow state. As of dapr-ext-workflow 1.18, Pydantic models can be passed directly to schedule_new_workflow, call_activity, and call_child_workflow — no manual model_dump() is needed. See REFERENCE.md for full example and key points.

Runtime file

The runtime.py file creates the shared WorkflowRuntime instance (wfr). Both workflow.py and activities.py import wfr from this file. This avoids a circular import between workflow.py and activities.py. See REFERENCE.md for full example.

Workflow file

A workflow is defined using the @wfr.workflow(name="") attribute. The workflow code is placed in a workflow.py file. It imports wfr from runtime.py. See REFERENCE.md for full example, key points, determinism rules, and workflow patterns (chaining, fan-out/fan-in, sub-workflows).

Activities file

A workflow activity is defined using the @wfr.activity(name="") attribute. The activity code is placed in an activities.py file. It imports wfr from runtime.py. See REFERENCE.md for full example and key points.

local.http

HTTP request file for testing the workflow endpoints. Contains a start request (POST) to schedule a new workflow instance and a status request (GET) to query the workflow state. Uses the <app-port> from dapr.yaml. See REFERENCE.md for full example.

Verify

IMPORTANT: After Project Setup you MUST run these exact verification instructions:

1. Run uv venv in the <ProjectName> folder to create a virtual environment.
2. Run uv sync in the <ProjectName> folder to install dependencies.

Create README.md

IMPORTANT: After Verify you MUST run these instructions:

Create a README.md file inside the folder.

The README contains the following sections:

1. Summary of what this folder contains.
2. Architecture description that explains the technology stack and prerequisites to run it locally. DO NOT suggest to run Redis separately since it's part of the Dapr installation and is running in a container already.
3. A mermaid diagram that explains the workflow.
4. How to start the application using the Dapr CLI.
5. List the available endpoints in the main.py file and provide examples how to call these using curl. Also include a link to the local.http file.
6. How to inspect the workflow execution using the Diagrid Dev Dashboard.
7. How to run the application with Diagrid Catalyst to visually inspect the workflow.

See REFERENCE.md for additional instructions on running locally and running with Catalyst.

Show final message

IMPORTANT: This is the LAST step. After Create README.md, your final output MUST be ONLY the message below — no preamble, no summary, no additional commentary, only replace the with the actual value:

The workflow application is created. Open the README.md file in the folder for a summary and instructions for running locally.