Shortcut Python API Docs | dltHub

Shortcut is a project management platform offering a REST API to access stories, epics, projects, iterations, labels, members, documents and related resources. The REST API base URL is https://api.app.shortcut.com/api/v3 and All requests require a Shortcut API token sent in the Shortcut-Token header.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Shortcut data in under 10 minutes.

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What data can I load from Shortcut?

Here are some of the endpoints you can load from Shortcut:

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How do I authenticate with the Shortcut API?

Token-based authentication using a per‑user API token sent in the Shortcut-Token HTTP header.

1. Get your credentials

1. Log in to your Shortcut workspace at https://app.shortcut.com/.
2. Go to Settings > Account > API Tokens (or visit https://app.shortcut.com/settings/account/api-tokens).
3. Create a new API token, give it a descriptive name and copy the token value.
4. Store the token securely (e.g., as an environment variable or in secrets.toml for dlt).
5. Use the token in requests via the Shortcut-Token header: Shortcut-Token: YOUR_TOKEN

2. Add them to .dlt/secrets.toml

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[sources.shortcut_project_management_source]
shortcut_api_token = "your_shortcut_api_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Shortcut API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python shortcut_project_management_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline shortcut_project_management_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset shortcut_project_management_data
The duckdb destination used duckdb:/shortcut_project_management.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline shortcut_project_management_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads stories and projects from the Shortcut API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def shortcut_project_management_source(shortcut_api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.app.shortcut.com/api/v3",
            "auth": {
                "type": "api_key",
                "api_key": shortcut_api_token,
            },
        },
        "resources": [
            {"name": "stories", "endpoint": {"path": "stories"}},
            {"name": "projects", "endpoint": {"path": "projects"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="shortcut_project_management_pipeline",
        destination="duckdb",
        dataset_name="shortcut_project_management_data",
    )
    load_info = pipeline.run(shortcut_project_management_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("shortcut_project_management_pipeline").dataset()
sessions_df = data.stories.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM shortcut_project_management_data.stories LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("shortcut_project_management_pipeline").dataset()
data.stories.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Shortcut data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify your Shortcut-Token header is present and correct. Tokens can be invalidated; regenerate a token from your account settings if needed. Do not pass the token in the URL (query param) — this method is deprecated.

Rate limiting

The API rate limit is 200 requests per minute. Exceeding this returns 429 Too Many Requests. Implement exponential backoff and respect the API’s pagination to reduce request volume.

Pagination and search quirks

Some list endpoints are simple top-level arrays. Search and some paginated endpoints (e.g., /search, /epics/paginated) return an object with a data array and next token: {"data": [...], "next": "...", "total": N}. When using search, prefer the returned next token as the full path/query for subsequent pages to preserve stable ordering.

Common error responses

• 400: Bad request / schema mismatch
• 401: Unauthorized (invalid/missing token)
• 403: Forbidden / DisabledFeatureError
• 404: Not found
• 422: Unprocessable (e.g., cannot delete resource)
• 429: Too Many Requests (rate limit exceeded)

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack