Tdd logo

Tdd

Organization
AgentiveStack
tdd

Implement a feature or fix using pragmatic test-driven development. Tracer-bullet approach — one test, one implementation, repeat. Tests verify behavior through public interfaces, not implementation details. Use when implementing a slice from /slice or any work that needs tests.

Overview

PublisherAgentiveStack
Repositoryskills
Skill nametdd
Stars
72
Forks
17
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 AgentiveStack on GitHub. Read the source before you install it.

Installation

Install the Tdd 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/AgentiveStack/skills/tree/main/tdd
  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/AgentiveStack/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/tdd .claude/skills/tdd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tdd 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 Tdd 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 Tdd 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.

Pragmatic Test-Driven Development

Implement features using vertical tracer bullets: one test → one implementation → repeat. Tests describe WHAT the system does, not HOW it does it.

Philosophy

Good tests exercise real code paths through public interfaces. They read like specifications: "visitor is bucketed consistently across sessions" tells you exactly what capability exists. These tests survive refactors because they don't care about internals.

Bad tests mock internal collaborators, test private methods, or verify implementation details. Warning sign: test breaks when you refactor, but behavior hasn't changed.

Pragmatic mode (default): test-first for core logic and boundary contracts. Implementation-first for UI and glue code, but still write tests for complex state management. Always write tests — the question is only whether they come before or after the code.

Process

1. Discover project context

Read domain documentation relevant to the work:

  1. docs/contexts/<name>/CONTEXT.md — invariants and contracts to verify
  2. docs/UBIQUITOUS_LANGUAGE.md — use domain terms in test names

Also discover the project's test infrastructure:

  • Find existing test files to understand patterns and conventions
  • Identify the test runner and configuration (package.json scripts, vitest/jest config)
  • Find test utilities, factories, or helpers already in use

2. Plan the tests

Before writing any code:

  • Identify behaviors to test — not implementation steps. Frame as: "A [actor] can [action] resulting in [observable outcome]"
  • Classify each behavior:
    • Core logic / domain rules → test-first (RED → GREEN)
    • Context boundary contracts → test-first (RED → GREEN)
    • API endpoints with non-trivial logic → test-first
    • UI components / glue code → implement first, test after if complex
    • Trivial wiring → skip tests
  • Identify what to mock — ONLY at system boundaries:
    • External APIs (Stripe, OpenAI, etc.) → mock
    • Databases → prefer test database or in-memory substitute; mock only if no alternative
    • Time, randomness → mock
    • Your own modules → NEVER mock. Test through the real code.
  • List behaviors in priority order

Present the plan to the user. Confirm which behaviors to test and the testing approach.

3. Tracer bullet

Write ONE test that confirms ONE thing about the system — the simplest possible end-to-end path:

RED:   Write test → test fails (confirms test infrastructure works)
GREEN: Write minimal code to pass → test passes

This proves the path works. Don't optimize, don't handle edge cases yet.

4. Incremental loop

For each remaining behavior:

RED:   Write next test → fails
GREEN: Minimal code to pass → passes

Rules:

  • One test at a time — don't write the next test until the current one passes
  • Only enough code to pass the current test — don't anticipate future tests
  • Keep tests focused on observable behavior through public interfaces
  • Run all tests after each GREEN to catch regressions

5. Refactor

After a group of related tests pass, look for refactoring opportunities:

  • Extract duplication (in code, not in tests — test duplication is OK)
  • Deepen modules (move complexity behind simpler interfaces)
  • Apply SOLID principles where they emerge naturally
  • Consider what the new code reveals about existing code

Never refactor while RED. Get to GREEN first, then refactor.

Run all tests after each refactor step.

6. Update domain documentation

If the implementation revealed new invariants, clarified contracts, or surfaced terminology changes:

  • Update docs/contexts/<name>/CONTEXT.md
  • Update docs/UBIQUITOUS_LANGUAGE.md
  • Consider an ADR if a hard-to-reverse, surprising trade-off was made

Checklist per cycle

[ ] Test name describes behavior in domain language (not implementation)
[ ] Test uses public interface only
[ ] Test would survive internal refactor
[ ] Test mocks only at system boundaries (external APIs, DB, time)
[ ] Code is minimal for this test — no speculative features
[ ] All existing tests still pass

When to mock

DependencyApproach
External API (Stripe, OpenAI)Mock — you don't control it
DatabasePrefer test DB or in-memory substitute. Mock as last resort.
Time / randomnessMock — tests must be deterministic
File systemUsually mock. Use temp dirs if testing file I/O specifically.
Your own modulesNEVER mock. Test through the real code path.
Internal collaboratorsNEVER mock. If you need to mock your own code, the interface needs redesigning.

Anti-patterns

  • Horizontal slicing: writing all tests first, then all implementation. Produces tests that test imagined behavior, not actual behavior.
  • Testing implementation: verifying internal state, mocking internal modules, testing private methods.
  • Over-testing: testing every getter, every trivial function. Focus on behaviors that matter.
  • Test-after-forget: writing code without tests and "planning to add them later." In pragmatic mode, UI can be implementation-first, but tests should follow immediately.

Related skills

  • /slice — break work into slices before implementing with TDD
  • /spec — create the spec that defines what to build
  • /domain — stress-test the design against the domain model

Frequently asked questions

What does the Tdd AI skill do?

Implement a feature or fix using pragmatic test-driven development. Tracer-bullet approach — one test, one implementation, repeat. Tests verify behavior through public interfaces, not implementation details. Use when implementing a slice from /slice or any work that needs tests.

Why use Tdd on TypingMind?

Because you install it once and use it with any model. Tdd 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 Tdd in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgentiveStack/skills/tree/main/tdd. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tdd?

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 Tdd?

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

Is the Tdd AI skill free?

Yes. It is published on GitHub by AgentiveStack 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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