Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/dalestudy/skills/testingnpx skills add DaleStudy/skills --skill testinggit clone --depth 1 https://github.com/DaleStudy/skillsWhat 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 | $0.00119 | $0.01718 |
| Opus 5 | $0.00060 | $0.00859 |
| Sonnet 5 | $0.00024 | $0.00344 |
| Haiku 4.5 | $0.00012 | $0.00172 |
Grade A, and why
testing 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testing Library
React Testing Library 기반 테스트 작성 모범 관례 및 안티패턴 회피 가이드.
핵심 원칙
Testing Library의 철학: 사용자가 사용하는 방식대로 테스트하라
- 접근성 기반 쿼리 우선 - 실제 사용자가 요소를 찾는 방식 사용
- 구현 세부사항 테스트 금지 - 컴포넌트 내부 상태/메서드 직접 접근 지양
- 실제 사용자 행동 시뮬레이션 - userEvent 사용, fireEvent 지양
- 비동기 처리 명시적 대기 - waitFor, findBy 활용
쿼리 우선순위
Testing Library는 다양한 쿼리를 제공하지만, 접근성과 사용자 경험을 반영하는 순서로 사용해야 함.
권장 쿼리 순서 (높음 → 낮음)
getByRole(최우선) - 스크린 리더가 인식하는 방식getByLabelText- 폼 요소 (label과 연결된 input)getByPlaceholderText- placeholder가 명확한 경우getByText- 텍스트 콘텐츠로 검색getByDisplayValue- 현재 입력된 값으로 검색 (폼 요소)getByAltText- 이미지 alt 속성getByTitle- title 속성 (tooltip 등)getByTestId(최후 수단) - 다른 방법이 불가능할 때만 사용
상세 가이드: references/query-priority.md
사용자 상호작용 테스트
userEvent 사용 (권장)
import { render, screen } from '@testing-library/react';
import userEvent from '@testing-library/user-event';
test('사용자가 폼을 제출할 수 있다', async () => {
const user = userEvent.setup();
render(<LoginForm />);
await user.type(screen.getByRole('textbox', { name: /이메일/i }), '[email protected]');
await user.type(screen.getByLabelText(/비밀번호/i), 'password123');
await user.click(screen.getByRole('button', { name: /로그인/i }));
expect(await screen.findByText(/환영합니다/i)).toBeInTheDocument();
});
핵심:
userEvent.setup()호출 후 사용- 모든 user 메서드는
await필수 - 실제 브라우저 이벤트 순서 재현 (focus, keydown, keyup 등)
fireEvent 지양
// ❌ 나쁜 예 - fireEvent 사용
fireEvent.click(button);
fireEvent.change(input, { target: { value: "text" } });
// ✅ 좋은 예 - userEvent 사용
await user.click(button);
await user.type(input, "text");
상세 가이드: references/user-events.md
비동기 처리
findBy 쿼리 (권장)
// ✅ 좋은 예 - findBy 사용
const successMessage = await screen.findByText(/저장되었습니다/i);
expect(successMessage).toBeInTheDocument();
findBy = getBy + waitFor 조합 (자동으로 요소 나타날 때까지 대기)
What ships with it
7 files 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.
- 2d ago First seen · 193 lines · 119 tokens per session scan A bccd7bd1abc8
testing is a skill published in the GitHub repository DaleStudy/skills (4 stars, last pushed 4mo ago), licensed MIT. It adds 119 tokens to every session and 1,718 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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