Build Scenario Tests logo

Build Scenario Tests

Community
tamdogood
build-scenario-tests

Inspect an unfamiliar repository, turn a focused Markdown behavior scenario into a deterministic test in the repository's native test stack, run it, and preserve traceability between intent and code. Use when asked to add scenario tests, compile acceptance criteria or Given/When/Then Markdown into executable tests, reproduce a user-visible regression, or convert a narrow workflow specification into stable web, API, CLI, desktop, or mobile interaction coverage. Do not use for broad exploratory journeys or agent-judged smoke tests.

Overview

Publishertamdogood
Repositorybuilder-essential-skills
Skill namebuild-scenario-tests
Stars
225
Forks
8
Bundled files
6
LicenseMIT
Links
  • 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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Build Scenario Tests 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.
    https://github.com/tamdogood/builder-essential-skills/tree/main/skills/build-scenario-tests
  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/tamdogood/builder-essential-skills.git /tmp/builder-essential-skills
mkdir -p .claude/skills
cp -r /tmp/builder-essential-skills/skills/build-scenario-tests .claude/skills/build-scenario-tests
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Build Scenario Tests 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 Build Scenario Tests 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 Build Scenario Tests 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.

Build Scenario Tests

Compile one small behavior contract into deterministic, repository-native test code. Understand the host project before choosing a harness, command, fixture, selector, or assertion.

Operating contract

  • Treat repository instructions and existing tests as authoritative.
  • Keep each scenario focused on one behavior and one reason to fail.
  • Compile into the test stack the repository already uses. Add a dependency only when no suitable harness exists and the user accepts the tradeoff.
  • Make setup, inputs, actions, and expected results deterministic.
  • Prefer public behavior over implementation details. Assert what a user or external caller can observe.
  • Keep the Markdown scenario beside the test or in the repository's established specification directory. Record the mapping in both artifacts.
  • Never weaken an assertion merely to make a test pass.

Workflow

1. Pass the repository-understanding gate

Read the nearest AGENTS.md or equivalent instructions, product README, contribution guide, manifests, test configuration, and the smallest relevant product documentation. Use repository search to find the implementation entry point, neighboring tests, fixtures, stable selectors, and validation commands. Trace the relevant action from its public entry point through the state boundary to the observable result. Do not read the entire tree without a reason.

Before editing, be able to state:

text
product surface:
behavior source:
relevant architecture and data flow:
runtime or start command:
existing test runner:
nearest tests to imitate:
fixture and state-isolation strategy:
narrow validation command:
full validation command:
known trust or destructive boundaries:

If a material field is unknown, search again. If the repository does not answer it, expose the gap and ask only for the missing product decision. Do not invent commands, selectors, credentials, or expected behavior.

2. Normalize the scenario

Read references/scenario-format.md. If the user provided prose, rewrite it into that contract without changing its intent.

Reject or split a scenario when it:

  • contains multiple independent behaviors;
  • depends on subjective visual judgment;
  • requires uncontrolled third-party state, real payment, or human authorization;
  • uses vague outcomes such as "works correctly";
  • cannot reset its data, time, randomness, or network dependencies.

Route a broad or agent-judged journey to a smoke-test workflow instead.

3. Map intent to the host test stack

Create a compact mapping before writing code:

Scenario elementRepository implementation
Preconditionsfixture, factory, seed, fake, or setup API
User actionpublic UI, CLI, HTTP, SDK, or native interaction
Oraclevisible state, output, response, durable state, or emitted event
Cleanuptransaction rollback, fixture teardown, or isolated temp state

Choose the narrowest existing harness that reaches the behavior:

  • web or desktop UI: the repository's browser or UI runner;
  • API or service: the native integration-test client;
  • CLI: the existing subprocess or command runner;
  • mobile: the project's native UI-test framework;
  • library: the public API through its current test runner.

Do not force a browser test onto behavior that a stable public API test can prove more directly.

4. Compile the deterministic test

Write the test in surrounding style. Preserve this traceability:

  • include the scenario ID in the test name or metadata;
  • link the scenario path from a short code comment when local conventions allow;
  • keep every oracle from the Markdown represented by an assertion;
  • use stable roles, labels, test IDs, public outputs, or API contracts;
  • isolate state with existing factories, fakes, temporary directories, or transactions;
  • control time, randomness, retries, and network calls when they affect results;
  • make cleanup run even after a failed assertion.

Do not copy the bundled demonstration code blindly. It contains fictional fixtures to show the mapping, not reusable project infrastructure.

5. Prove the compilation

Run the new test alone. Confirm it fails for the intended reason without the behavior or regression fix when that check is safe and practical. Then run the nearest relevant suite and the repository's required lint, type, build, and test commands.

Inspect failures semantically. Fix the test only when the test is wrong. Do not change product behavior unless the user asked for the implementation or fix.

Check for:

  • every Markdown oracle has a matching assertion;
  • no accidental live service, account, payment, or production dependency;
  • repeated runs use isolated state and stable ordering;
  • failure output identifies the broken behavior;
  • the scenario and compiled test link to each other.

6. Hand off the evidence

Report:

  1. what repository evidence informed the test design;
  2. the scenario path and compiled test path;
  3. the behavior and boundaries covered;
  4. commands run and results;
  5. anything intentionally left to integration or smoke coverage.

Demonstration examples

Use these to explain the Markdown-to-code model. When applying the skill, derive all commands, fixtures, and assertions from the actual repository.

Failure handling

  • No test harness: document the nearest viable seam and ask before adding a dependency.
  • Behavior is undocumented or contradictory: preserve the evidence and ask the product owner which outcome is authoritative.
  • Failure requires subjective or human judgment: keep the deterministic portion as a scenario test and route the rest to smoke coverage.
  • Test is flaky after two focused attempts: stop, preserve logs, and report the uncontrolled input instead of adding retries.

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 Build Scenario Tests AI skill do?

Inspect an unfamiliar repository, turn a focused Markdown behavior scenario into a deterministic test in the repository's native test stack, run it, and preserve traceability between intent and code. Use when asked to add scenario tests, compile acceptance criteria or Given/When/Then Markdown into executable tests, reproduce a user-visible regression, or convert a narrow workflow specification into stable web, API, CLI, desktop, or mobile interaction coverage. Do not use for broad exploratory journeys or agent-judged smoke tests.

Why use Build Scenario Tests on TypingMind?

Because you install it once and use it with any model. Build Scenario Tests 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 Build Scenario Tests in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tamdogood/builder-essential-skills/tree/main/skills/build-scenario-tests. 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 Build Scenario Tests?

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 Build Scenario Tests?

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

Is the Build Scenario Tests AI skill free?

Yes. It is published on GitHub by tamdogood 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇