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/itlearning/study-ios/swift-quiznpx skills add ITlearning/study-ios --skill swift-quizgit clone --depth 1 https://github.com/ITlearning/study-iosWhat 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 | $0.00024 | $0.02763 |
| Opus 5 | $0.00012 | $0.01381 |
| Sonnet 5 | $0.00005 | $0.00553 |
| Haiku 4.5 | $0.00002 | $0.00276 |
Grade A, and why
swift-quiz 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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
swift-quiz - Swift/iOS Quiz
Adaptive Swift/iOS quiz with two modes: (두 가지 모드를 지원하는 Swift/iOS 적응형 퀴즈)
- Classic: quick checks with lighter explanation requirements
- Mastery: strict reasoning and mechanism-focused feedback
Instructions
Terminology Policy (초심자 친화 용어 규칙)
Prefer Korean-first labels in user-facing text, and keep English as secondary reference in parentheses. (사용자에게 보여주는 단계명은 한국어를 우선하고, 영어는 괄호로 보조 표기한다.)
Examples: (예시)
근거 설명 (WHY reasoning)통과 / 보완 필요(원문 표기: pass / fail)격리 경계 (isolation boundary): actor 바깥에서 내부 상태를 직접 바꾸지 못하게 하는 경계경합 상태 (data race): 여러 작업이 같은 값을 동시에 바꿔 결과가 꼬이는 문제
When a technical term appears for the first time, add a one-line plain-language gloss. (처음 나오는 기술 용어는 한 줄 쉬운 설명을 바로 붙인다.)
Step 0: Language Selection
Ask the user to choose a language at the start using a selection flow, not free-form text input. (스킬 시작 시 자유 입력이 아니라 선택형 방식으로 언어를 받는다)
- 한국어 - 한국어로 퀴즈를 풉니다
- English - Take the quiz in English
Use platform-specific selection behavior:
- Codex CLI: use native option selection (
request_user_inputwith 2 options) when available. If unavailable, show numbered choices and ask for1or2. - Claude Code: use native option selection UI when available. If unavailable, show numbered choices.
- Gemini CLI: use native option selection UI when available. If unavailable, show numbered choices.
Choice mapping: 1 → 한국어, 2 → English
Use the selected language for all communication. Code and Swift keywords stay in English. (선택한 언어로 이후 모든 소통을 진행한다. 코드와 Swift 키워드는 영어 그대로 유지한다.)
Study Mode Selection
Ask the user to choose a study mode using the same selection pattern: (같은 선택 방식으로 학습 모드를 선택하게 한다)
-
Classic (설명 중심, 추천)
- 빠르게 실력을 점검할 때 적합
- 정답/오답 중심으로 진행하고, 해설은 간결하게 제공
- 부담 없이 복습하기 좋은 모드
-
Mastery (훈련 중심)
- 정답뿐 아니라 이유(WHY)까지 검증
- 메커니즘 설명이 있어야 높은 난이도로 진행
- 약점 교정과 실전 대비에 적합
Choice mapping: (선택값 매핑)
1-> Classic2-> Mastery
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 · 292 lines · 24 tokens per session scan A cbd571424c55
swift-quiz is a skill published in the GitHub repository ITlearning/study-ios (19 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 2,763 once invoked, about $0.0001 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 2026-08-30.
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