Borrowing it
Nothing to install: this file belongs to tan-yong-sheng/ai-vision-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/tan-yong-sheng/ai-vision-mcp/main/.claude/skills/tdd-workflow/SKILL.mdgit clone --depth 1 https://github.com/tan-yong-sheng/ai-vision-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/tan-yong-sheng/ai-vision-mcp/tdd-workflow)<a href="https://agentmods.dev/skills/tan-yong-sheng/ai-vision-mcp/tdd-workflow"><img src="https://agentmods.dev/badge/skills/tan-yong-sheng/ai-vision-mcp/tdd-workflow/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/tan-yong-sheng/ai-vision-mcp/tdd-workflow"><img src="https://agentmods.dev/badge/skills/tan-yong-sheng/ai-vision-mcp/tdd-workflow.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 | $0.00043 | $0.02455 |
| Opus 5 | $0.00022 | $0.01228 |
| Sonnet 5 | $0.00009 | $0.00491 |
| Haiku 4.5 | $0.00004 | $0.00246 |
Grade A, and why
tdd-workflow 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 12d 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
95% identical to tdd-workflow — 819 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 — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test-Driven Development Workflow
This skill ensures all code development follows TDD principles with comprehensive test coverage.
When to Activate
- Writing new features or functionality
- Fixing bugs or issues
- Refactoring existing code
- Adding API endpoints
- Creating new components
Core Principles
1. Tests BEFORE Code
ALWAYS write tests first, then implement code to make tests pass.
2. Coverage Requirements
- Minimum 80% coverage (unit + integration + E2E)
- All edge cases covered
- Error scenarios tested
- Boundary conditions verified
3. Test Types
Unit Tests
- Individual functions and utilities
- Component logic
- Pure functions
- Helpers and utilities
Integration Tests
- API endpoints
- Database operations
- Service interactions
- External API calls
E2E Tests (Playwright)
- Critical user flows
- Complete workflows
- Browser automation
- UI interactions
TDD Workflow Steps
Step 1: Write User Journeys
As a [role], I want to [action], so that [benefit]
Example:
As a user, I want to search for markets semantically,
so that I can find relevant markets even without exact keywords.
Step 2: Generate Test Cases
For each user journey, create comprehensive test cases:
describe('Semantic Search', () => {
it('returns relevant markets for query', async () => {
// Test implementation
})
it('handles empty query gracefully', async () => {
// Test edge case
})
it('falls back to substring search when Redis unavailable', async () => {
// Test fallback behavior
})
it('sorts results by similarity score', async () => {
// Test sorting logic
})
})
Step 3: Run Tests (They Should Fail)
npm test
# Tests should fail - we haven't implemented yet
Step 4: Implement Code
Write minimal code to make tests pass:
// Implementation guided by tests
export async function searchMarkets(query: string) {
// Implementation here
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 411 lines · 43 tokens per session scan A c7afac4aeb34
tdd-workflow is a skill published in the GitHub repository tan-yong-sheng/ai-vision-mcp (78 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 2,455 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to tdd-workflow, differing in 819 lines, and is treated as a copy.
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