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Test Driven Development

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mikeyobrien
test-driven-development

Unified TDD skill with three input modes — from spec, from task, or from description. Enforces test-first development using repository patterns, with proptest guidance and backpressure integration.

Overview

Publishermikeyobrien
Repositoryralph-orchestrator
Skill nametest-driven-development
Stars
3.2K
Forks
299
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Test Driven Development 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/mikeyobrien/ralph-orchestrator/tree/main/.claude/skills/test-driven-development
  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/mikeyobrien/ralph-orchestrator.git /tmp/ralph-orchestrator
mkdir -p .claude/skills
cp -r /tmp/ralph-orchestrator/.claude/skills/test-driven-development .claude/skills/test-driven-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Test Driven Development 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 Test Driven Development 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 Test Driven Development 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.

Test-Driven Development

Overview

One skill for all TDD workflows. Enforces test-first development using existing repository patterns. Three input modes handle different entry points — specs, task files, or ad-hoc descriptions — but the core cycle is always RED → GREEN → REFACTOR.

Input Modes

Detect the input type and follow the corresponding mode:

Mode A: From Spec (.spec.md)

Use when the input references a .spec.md file with Given/When/Then acceptance criteria.

  1. Locate and parse the spec file — extract all Given/When/Then triples
  2. Generate one test stub per criterion with todo!() bodies:
    rust
    /// Spec: <spec-file> — Criterion #<N>
    /// Given <given text>
    /// When <when text>
    /// Then <then text>
    #[test]
    fn <spec_name>_criterion_<N>_<slug>() {
        todo!("Implement: <then text>");
    }
  3. Verify stubs compile but fail: cargo test --no-run -p <crate>
  4. Proceed to the TDD Cycle to make stubs pass

Programmatic support: ralph_core::preflight::{extract_acceptance_criteria, extract_criteria_from_file, extract_all_criteria} can parse criteria from spec files.

Mode B: From Task (.code-task.md)

Use when the input references a .code-task.md file or a specific implementation task.

  1. Read the task and identify acceptance criteria or requirements
  2. Discover patterns (see Pattern Discovery)
  3. Design test scenarios covering normal operation, edge cases, and error conditions
  4. Write failing tests for all requirements before any implementation
  5. Proceed to the TDD Cycle

Mode C: From Description

Use for ad-hoc tasks without a spec or task file.

  1. Clarify requirements from the description
  2. Discover patterns (see Pattern Discovery)
  3. Write failing tests targeting the described behavior
  4. Proceed to the TDD Cycle

Pattern Discovery

Before writing tests, discover existing conventions:

bash
rg --files -g "crates/*/tests/*.rs"
rg -n "#\[cfg\(test\)\]" crates/

Read 2-3 relevant test files near the target code. Mirror:

  • Test module layout, naming, and assertion style
  • Fixture helpers and test utilities
  • Use of tempfile, scenarios, or harnesses

TDD Cycle

1) RED — Failing Tests

  • Write tests for the exact behavior required
  • Run tests to confirm failure for the right reason
  • If tests pass without implementation, the test is wrong

2) GREEN — Minimal Implementation

  • Write the minimum code to make tests pass
  • No extra features or refactoring during this step

3) REFACTOR — Clean Up

  • Improve implementation and tests while keeping tests green
  • Align with surrounding codebase conventions
  • Re-run tests after every change

Proptest Guidance

Use proptest only when ALL of:

  • Function is pure (no I/O, no time, no globals)
  • Deterministic output for given input
  • Non-trivial input space or edge cases
rust
proptest! {
    #[test]
    fn round_trip(input in "[a-z0-9]{0,32}") {
        let encoded = encode(input.as_str());
        let decoded = decode(&encoded).expect("should decode");
        prop_assert_eq!(decoded, input);
    }
}

Don't introduce proptest as a new dependency without strong justification.

Backpressure Integration

Include coverage evidence in completion events:

bash
ralph emit "build.done" "tests: pass, lint: pass, typecheck: pass, audit: pass, coverage: pass (82%)"

Run cargo tarpaulin --out Html --output-dir coverage --skip-clean when feasible. If coverage cannot be run, state why and include targeted test evidence instead.

Test Location Rules

  • Spec maps to a single module → inline #[cfg(test)] tests
  • Spec spans multiple modules → integration test in crates/<crate>/tests/
  • CLI behavior → crates/ralph-cli/tests/
  • Follow existing patterns in the target crate

Anti-Patterns

  • Writing implementation before tests
  • Generating tests that pass without implementation
  • Copying tests from other crates without adapting to local patterns
  • Adding proptest when a simple example test suffices
  • Emitting completion events without coverage evidence

Frequently asked questions

What does the Test Driven Development AI skill do?

Unified TDD skill with three input modes — from spec, from task, or from description. Enforces test-first development using repository patterns, with proptest guidance and backpressure integration.

Why use Test Driven Development on TypingMind?

Because you install it once and use it with any model. Test Driven Development 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 Test Driven Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikeyobrien/ralph-orchestrator/tree/main/.claude/skills/test-driven-development. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Test Driven Development?

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 Test Driven Development?

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

Is the Test Driven Development AI skill free?

Yes. It is published on GitHub by mikeyobrien 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.

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