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Bdd With Approvals

Community
lexler
bdd-with-approvals

Scannable BDD tests written in domain language. Use when doing BDD.

Overview

Publisherlexler
Repositoryskill-factory
Skill namebdd-with-approvals
Stars
239
Forks
63
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Bdd With Approvals 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/lexler/skill-factory/tree/main/output_skills/testing/bdd-with-approvals
  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/lexler/skill-factory.git /tmp/skill-factory
mkdir -p .claude/skills
cp -r /tmp/skill-factory/output_skills/testing/bdd-with-approvals .claude/skills/bdd-with-approvals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bdd With Approvals 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 Bdd With Approvals 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 Bdd With Approvals 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.

BDD with Approval Tests

The Problem

Specifications live in documents. They drift from reality because nothing enforces them.

Tests verify implementation. Written after code, they document what IS, not what SHOULD BE. They're noisy. You can't glance at them and quickly validate correctness.

You need an artifact that:

  • Captures expected behavior before code exists
  • Stays in sync because it's executable
  • A human can validate at a glance

Executable Specifications

The fixture file IS that artifact. Write it BEFORE implementation.

Think through scenarios by creating approval files. Describe expected behavior in domain language. Implementation is driven by making these specs pass. Specs stay executable, never go stale.

A human looks at the fixture and immediately sees: correct or not. No translation between "spec" and "test". They're the same artifact.

For the approval testing technique itself (verify, scrubbers, combinations), see /approval-tests. For nulled infrastructure in system tests, see /nullables.

Approved Fixtures

Test files combining input and expected output in a format designed for human validation.

## Input
(context, parameters, initial state)

## Output
(expected results, side effects, final state)

Test runner reads fixtures, executes code, compares output. Adding test cases = adding files, not code.

Design the format for YOUR domain:

  • Grid/spatial problems → ASCII art
  • Transformations → before/after
  • Workflows → step sequences with results
  • API interactions → request/response pairs

See references/approved-fixtures.md for examples.

Format Design

The question: Can someone validate correctness in <5 seconds?

Design for human eyes, not machine parsing. Match the domain's natural representation. How you'd explain it on a whiteboard.

What makes formats scannable:

  • Columnar layouts with visual alignment
  • Consistent structure across all cases
  • Whitespace that groups related elements

Avoid:

  • Dense JSON (hard to scan)
  • Single-line formats (no visual structure)
  • Formats requiring mental parsing

Implementation

One-time per domain:

  1. Parser - extracts input from fixture format
  2. Formatter (printer) - converts actual output to fixture format
  3. Single test file discovers and runs all fixtures

Keep parser/formatter simple. Format should be close to natural representation.

Approved Logs

Turn production logs into tests by copying and fixing incorrect lines. Quick bug reproduction.

Caveat: Logs are for runtime observability, not test validation. Tying tests to log format creates coupling. Log changes break tests. Use sparingly when logs happen to capture the behavior well.

See references/approved-logs.md.

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 Bdd With Approvals AI skill do?

Scannable BDD tests written in domain language. Use when doing BDD.

Why use Bdd With Approvals on TypingMind?

Because you install it once and use it with any model. Bdd With Approvals 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 Bdd With Approvals in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lexler/skill-factory/tree/main/output_skills/testing/bdd-with-approvals. 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 Bdd With Approvals?

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 Bdd With Approvals?

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

Is the Bdd With Approvals AI skill free?

Yes. It is published on GitHub by lexler under the Apache-2.0 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.

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