How does this skill differ from simply asking an AI to write tests?

This skill enforces a strict Red-Green-Refactor workflow, ensuring every code change is preceded by a verifiable failing test to eliminate implementation bias and untested behavior.

What AI agents or platforms is this skill compatible with?

The skill is designed to integrate with most major AI coding agents that support custom tool-use or structured workflows, provided they have access to your local or CI testing environment.

Does this skill provide its own testing framework or do I need one already?

You will need an existing automated testing framework (like Jest, PyTest, or JUnit) configured in your project, as the skill relies on executing these suites to verify failure and success states.

Can I use this skill on an existing codebase that doesn't have any tests yet?

Yes, this skill is highly effective for legacy systems; it guides you through creating characterization tests to capture current behavior before you safely attempt any refactoring or bug fixes.

What exactly is included in the purchase of this skill?

You receive the specialized TDD logic, integration instructions for your AI agent, and a guide on interpreting 'valid' vs 'invalid' failure states to maximize code quality.

How are updates handled if my AI agent's software changes?

Updates are included with your purchase, and the skill is versioned to ensure compatibility as underlying LLM models and coding agent capabilities evolve.

Failing Test First TDD

Example session with this skill installed

I'm fixing a bug in our comments service: users can currently post comments containing only whitespace, and they shouldn't be able to. The create_comment function lives in app/services/comments.py and the suite is in tests/test_comments.py. Please add the validation using strict test-first discipline: write the failing test documenting the expected rejection, run it and confirm it fails for the right reason, then implement the minimal fix, re-run the targeted test and the full pytest suite, and report the observed red and green states.

Read your context and instructions
Compiled the failing test first

Summary

Added whitespace-comment rejection via a full RED → GREEN → IMPROVE cycle: one failing test observed first, minimal implementation second, whole suite green afterwards.

Stage	Command	Observed outcome
RED	`pytest tests/test_comments.py::test_rejects_comment_containing_only_whitespace -q`	Failed as intended — expected `{"ok": False}`, got `{"ok": True}`
GREEN	Same targeted test	Passed after a minimal `text.strip()` guard
SUITE	`pytest -q`	112 passed, 0 failed, no new warnings

def create_comment(text: str) -> dict:
    if not text.strip():
        return {"ok": False, "error": "Comment text is required"}
    return store.insert(text)

Next steps

Add tests for the empty string and tab/newline-only payloads to force generalization
Write characterization tests for the edit-comment path before refactoring it
Sweep other validators for the same missing .strip() guard

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About this skill

The problem

Writing tests after implementation often leads to biased coverage that only confirms what the code already does, rather than what it should do. This creates false confidence and makes bugs harder to catch during the development cycle.

What it does

Enforces a strict Red-Green-Improve cycle for every behavior change.
Identifies valid vs. invalid failure states to ensure tests are actually meaningful.
Guides the creation of minimal, honest implementations that solve the immediate requirement without over-engineering.
Provides clear criteria for safe refactoring only when the test suite is green.
Detects design smells like high coupling or unclear boundaries via test setup friction.

Why this beats prompting it yourself

General LLMs often skip straight to providing the solution, ignoring the critical failure step that proves the test's validity. This skill prevents implementation bias by forcing a stop at the "Red" phase, ensuring you never ship unverified behavior or hollow proofs.

Use cases

Fixing bugs by creating a failing reproduction before writing the patch.
Developing new features with a clear specification of the public API.
Safe refactors of legacy code using characterization tests as a safety net.
Implementing validation rules and permission checks with verified edge-case coverage.

Known limitations

Requires an existing automated testing framework. Not suitable for disposable research spikes or static configuration changes.

How to install

Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

~30 seconds

1
Download the ZIP
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2
Unzip into your skills folder
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3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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Failing Test First TDD