Borrowing it
Nothing to install: this file belongs to shashankswe2020-ux/whoop-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/shashankswe2020-ux/whoop-mcp/main/.github/skills/test-driven-development/SKILL.mdgit clone --depth 1 https://github.com/shashankswe2020-ux/whoop-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shashankswe2020-ux/whoop-mcp/test-driven-development)<a href="https://agentmods.dev/skills/shashankswe2020-ux/whoop-mcp/test-driven-development"><img src="https://agentmods.dev/badge/skills/shashankswe2020-ux/whoop-mcp/test-driven-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shashankswe2020-ux/whoop-mcp/test-driven-development"><img src="https://agentmods.dev/badge/skills/shashankswe2020-ux/whoop-mcp/test-driven-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.03289 |
| Opus 5 | $0.00025 | $0.01644 |
| Sonnet 5 | $0.00010 | $0.00658 |
| Haiku 4.5 | $0.00005 | $0.00329 |
Grade A, and why
test-driven-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
83% identical to test-driven-development — 25 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 380 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test-Driven Development
Overview
Write a failing test before writing the code that makes it pass. For bug fixes, reproduce the bug with a test before attempting a fix. Tests are proof — "seems right" is not done. A codebase with good tests is an AI agent's superpower; a codebase without tests is a liability.
When to Use
- Implementing any new logic or behavior
- Fixing any bug (the Prove-It Pattern)
- Modifying existing functionality
- Adding edge case handling
- Any change that could break existing behavior
When NOT to use: Pure configuration changes, documentation updates, or static content changes that have no behavioral impact.
Related: For browser-based changes, combine TDD with runtime verification using Chrome DevTools MCP — see the Browser Testing section below.
The TDD Cycle
RED GREEN REFACTOR
Write a test Write minimal code Clean up the
that fails ──→ to make it pass ──→ implementation ──→ (repeat)
│ │ │
▼ ▼ ▼
Test FAILS Test PASSES Tests still PASS
Step 1: RED — Write a Failing Test
Write the test first. It must fail. A test that passes immediately proves nothing.
// RED: This test fails because createTask doesn't exist yet
describe('TaskService', () => {
it('creates a task with title and default status', async () => {
const task = await taskService.createTask({ title: 'Buy groceries' });
expect(task.id).toBeDefined();
expect(task.title).toBe('Buy groceries');
expect(task.status).toBe('pending');
expect(task.createdAt).toBeInstanceOf(Date);
});
});
Step 2: GREEN — Make It Pass
Write the minimum code to make the test pass. Don't over-engineer:
// GREEN: Minimal implementation
export async function createTask(input: { title: string }): Promise<Task> {
const task = {
id: generateId(),
title: input.title,
status: 'pending' as const,
createdAt: new Date(),
};
await db.tasks.insert(task);
return task;
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 380 lines · 50 tokens per session scan A fc76d105d467
test-driven-development is a skill published in the GitHub repository shashankswe2020-ux/whoop-mcp (151 stars, last pushed 5d ago), licensed MIT. It adds 50 tokens to every session and 3,289 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to test-driven-development, differing in 25 lines, and is treated as a copy.
Other skills, from other repositories
afrexai-claude-code-production
Complete Claude Code productivity system — project setup, prompting patterns, sub-agent orchestration, context management, debugging, refactoring, TDD, and shipping 10X faster. Zero scripts needed.
Vibe Coding Mastery
The complete operating system for building software with AI. From first prompt to production deployment — prompting frameworks, architecture patterns, testing strategies, debugging playbooks, and production graduation checklists. Works with Claude Code, Cursor, Windsurf, Copilot, and any AI coding tool.
agentic-eval
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: Implementing self-critique and reflection loops Building evaluator-optimizer pipelines for quality-critical generation Creating test-driven code refinement workflows Designing rubric-based or LLM-as-judge evaluation systems…
contract
Outcome-driven Cortex function development — declares a behavioral contract before generation begins, enforces evidence-tiered proof before $ship, and defends against the self-oracle evaluation failure mode.
spec-driven-development
Design and run a Spec-Driven Development (SDD) pipeline for AI software factories — where structured specifications are the input, AI agents generate the code, and quality gates enforce correctness at each phase: SPECIFY → DECOMPOSE → IMPLEMENT → VERIFY → DELIVER. Use when building or refining a spec-driven pipeline…
red-green-refactor
Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like…