Story test

This CLI uses AI and large language models (LLMs) to evaluate plain-English tests (e.g., "The hero wins in the end") against Markdown stories or other text. It reports whether each test passes or fails.

Supports OpenAI, Anthropic, and local Ollama models.

Use cases

This tool can help with:

• Writing: Make sure your story's main points are addressed.
• Editing: Build and grow a repository with standard tests you want to run on manuscripts, and make specific tests for specific genres, styles, etc.
• White papers / notes: Run tests against your notes or papers. Does my paper convey X point clearly after my latest changes?
• Contracts: Run tests against your contracts. Is X case covered?

The problem (a.k.a. how this started)

It's always good to start with the problem we want to solve.

As a writer, I keep:

• Notes
• To-do lists
• Character sheets/descriptions
• Plot summaries
• Checklists that I have to re-check with every new draft, e.g. before sending the story to an editor
• Notes and snippets from books on writing, with advice I’d love to think I'm sticking to, but can’t keep track of

But I'm also a software engineer, and testing is part of my job.

And I noticed that all of these can be expressed as natural language tests (in plain English, or Spanish, or any language).

And now, with AI and LLMs, natural-language tests can be automated.

🚀 Getting Started

Install the CLI from PyPI with pipx so it is available from any directory in an isolated Python environment:

pipx install story-test

Alternatively, install it into the active Python environment:

python -m pip install --upgrade story-test

The default provider is Ollama. Install Ollama separately and pull the default model if you use it:

ollama pull qwen3:8b

For OpenAI or Anthropic, no local model runtime is needed; provide OPENAI_API_KEY or ANTHROPIC_API_KEY and select the provider as shown above.

Usage

Create a test file containing assertions about a story:

tests:
  - name: author
    assertion: The story was written by Edgar Allan Poe.
  - name: ending
    assertion: The narrator confesses at the end of the story.

Run the tests against one or more Markdown files:

story-test story.tests.yml story.md
story-test story.tests.yml chapter-1.md chapter-2.md

Failed assertions are reported without failing the process by default. For CI, use strict mode:

story-test story.tests.yml story.md --fail-on-test-failure

The default provider is Ollama. For hosted OpenAI usage, set the API key and select the provider explicitly:

export OPENAI_API_KEY=your-key
story-test story.tests.yml story.md --provider openai --model gpt-4.1-mini

For Anthropic:

export ANTHROPIC_API_KEY=your-key
story-test story.tests.yml story.md \
  --provider anthropic --model claude-opus-5-5

If you are using a Claude Code authorization token, Anthropic's SDK also accepts:

export ANTHROPIC_AUTH_TOKEN=your-token

Ollama remains available as an optional local provider:

STORY_TEST_PROVIDER=ollama sh install.sh
story-test story.tests.yml story.md --provider ollama --model qwen3:8b

Advanced uses

Test-driven writing

For plotters and technical writers who start with requirements before they have a document, this app can be used to apply the basic Test-driven development (TDD) cycle to writing: define what you want the text to do, draft, run the checks, and revise based on the results.

Start by turning your outline, character notes, or revision checklist into plain-English assertions. For example:

tests:
  - name: protagonist-goal
    assertion: The protagonist's goal is clear by the end of the first chapter.
  - name: planted-clue
    assertion: The brass key is introduced before it is used to open the cellar.
  - name: ending-resolves-conflict
    assertion: The ending resolves the central conflict between the sisters.

Then run the tests against your draft:

story-test story.tests.yml story.md

An assertion failing just means that the draft has not met that intention yet, or the assertion needs to be clarified.

How to apply it to writing:

1. Start with your notes, outline, or requirements. Write a handful of checks for things you want the story to establish, include, or resolve.
2. Create a draft and run the checks. Early failures are expected; use them to spot intentions that are not yet showing up in the text.
3. After a writing session or major revision, run the same checks again. Add, remove, or refine checks as your plans change.

CI for writing

If your manuscript lives in Git, you can run the checks on every push or pull request. That gives you an automatic warning when a revision breaks a test. You can also add strict CI failure when the changes break the manuscript, to ensure that they cannot be merged into the main branch.

Maintaining this repo

Development Setup

The repository uses direnv, Devbox, Taskfile, and pre-commit for a reproducible development environment and automatic schema/YAML validation.

# Automatically enter devbox via direnv (if available)
direnv allow

# Enter dev environment
devbox shell

# Install pre-commit hooks
task pre-commit:install

Tasks

Run task to see a list of available tasks.

Install the development dependencies with:

task environment:dev:install

For a local user installation, run the bootstrap script from this repository:

sh install.sh

This creates an isolated Python environment and installs the story-test command. The installed command can then be used from any directory:

story-test path/to/story.tests.yml path/to/story.md

For an Ollama installation, set STORY_TEST_PROVIDER before running the installer to pull a local model:

STORY_TEST_PROVIDER=ollama STORY_TEST_MODEL=qwen3:30b-a3b sh install.sh

Install Ollama separately only when using the local provider, then download the model through Task:

task environment:ollama:install

To use a model already installed locally:

OLLAMA_MODEL=qwen3:30b-a3b task environment:ollama:install

Run the sample story tests. Failed story assertions are reported but do not fail the task by default:

task test:example-story

The OpenAI and Anthropic providers use their standard OPENAI_API_KEY and ANTHROPIC_API_KEY environment variables. The Ollama provider uses STORY_TEST_MODEL and STORY_TEST_CONTEXT_LENGTH and is the default.

The model can be changed with STORY_TEST_MODEL, and the context window can be changed with STORY_TEST_CONTEXT_LENGTH.

To make failed story assertions fail the task, use the strict variant:

task test:example-story:strict

Run the complete local validation suite:

task test:all

This runs schema validation, Python unit tests, and the sample story tests.

The GitHub Actions workflow validates and builds the package without running an AI provider. Qwen open-weight models are Apache 2.0 licensed and Ollama is MIT licensed; always review the license for the exact model tag you deploy.

Run formatting and pre-commit checks with:

task format:check
task pre-commit:run

Before publishing a release, build and validate both distribution formats:

task package:check

This creates the wheel and source archive under dist/ and validates them with Twine. Increment the version in pyproject.toml before building a new release.