Automating New Projects in Todoist with Python

Yesterday I built a small, friendly set of scripts to remove the friction of spinning up a new project in Todoist. Instead of clicking around to add sections and tasks, I now drop a short JSON file into a folder and run one command. Two minutes later, the whole project is ready for real work.

Repo: devandapaige/todoist

What it does

• Creates or reuses a project: If a project with the same name exists, it adds to it rather than duplicating it.
• Builds structure fast: Adds sections, tasks, and optional sub‑tasks from a plain JSON file.
• Keeps focus on your flow: I designed this to reduce cognitive load at the exact moment I’m shifting into a new initiative.

Why this matters for brain-and-energy aware work

The first few minutes of any project are decision-heavy. By externalizing the structure—what sections I use, which tasks always start a project—I protect my energy for the meaningful parts. This is a tiny automation that creates space for momentum and reduces startup anxiety.

Quick start

1. Create a virtual environment and install deps

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install todoist-api-python python-dotenv

2. Add your Todoist API token

echo "TODOIST_API_TOKEN=your-token-here" > .env

3. Define your project data

{
  "project_name": "Client Project Template",
  "sections": [
    {
      "name": "Planning",
      "tasks": [
        {
          "content": "Kickoff notes",
          "sub_tasks": [
            { "content": "Confirm goals" },
            { "content": "List stakeholders" }
          ]
        },
        { "content": "Define scope", "sub_tasks": [] }
      ]
    },
    {
      "name": "Execution",
      "tasks": [ { "content": "Set up repo" }, { "content": "First milestone" } ]
    }
  ]
}

4. Run the importer

python import_to_todoist.py

You’ll see progress logs as the project, sections, tasks, and sub‑tasks are created. If the project already exists, the script adds the new structure to it.

Notes and tradeoffs

• The importer intentionally adds tasks each run; it doesn’t try to de‑duplicate similar items. That keeps the script simple and predictable when you’re iterating on templates.
• Put the .env file next to import_to_todoist.py. The script loads it automatically.
• If you prefer environment variables, export TODOIST_API_TOKEN instead of using .env.

How I’m using it

I turn long AI chats into actionable Todoist projects by asking the model for a single JSON payload in the importer’s format. I paste that JSON into project_data.json and run the script—done.

Prompt I use (short and strict)

You are helping me turn our conversation into an actionable Todoist project.

Return ONLY valid JSON in this exact schema (no prose):
{
  "project_name": "<short-name>",
  "sections": [
    { "name": "<section>", "tasks": [
      { "content": "<task>", "sub_tasks": [ { "content": "<subtask>" } ] }
    ]}
  ]
}

Rules:
- Use 3–6 sections that reflect this chat’s themes.
- Each section: 3–7 tasks.
- Use clear, verb-led task names.
- Include sub_tasks only where they reduce ambiguity.
- Do not include comments, markdown, or trailing commas.

Workflow

1. Summarize the chat in my head into 3–6 themes (sections).
2. Run the prompt with a one‑line context: “Topic: what we discussed.”
3. Paste result into project_data.json.
4. python import_to_todoist.py to create the project.

This isn’t flashy; it’s deliberately boring—in a good way. It removes the micro‑decisions that slow me down and lets me get to the work that actually needs my attention.

With digital care,

The Pythoness Programmer

Amanda

Reference: the full README, setup steps, and example JSON live in the repo: devandapaige/todoist.

Find the code on GitHub: devandapaige/todoist