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 skills add kimlawtech/korean-privacy-terms --skill privacy-usgit clone --depth 1 https://github.com/kimlawtech/korean-privacy-termsWrote 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/kimlawtech/korean-privacy-terms/privacy-us)<a href="https://agentmods.dev/skills/kimlawtech/korean-privacy-terms/privacy-us"><img src="https://agentmods.dev/badge/skills/kimlawtech/korean-privacy-terms/privacy-us/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/kimlawtech/korean-privacy-terms/privacy-us"><img src="https://agentmods.dev/badge/skills/kimlawtech/korean-privacy-terms/privacy-us.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00101 | $0.02894 |
| Opus 5.5 | $0.00040 | $0.01158 |
| Sonnet 5.5 | $0.00020 | $0.00579 |
| Haiku 4.5 | $0.00010 | $0.00289 |
Grade A, and why
privacy-us 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 1mo 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
privacy-us — 미국 CCPA/CPRA 전용 스킬
호출 즉시 출력
Starting US (CCPA/CPRA) Privacy Policy generator.
Covers: CCPA/CPRA (California) + major state laws
VCDPA (Virginia), CPA (Colorado), CTDPA (Connecticut), UCPA (Utah),
ICDPA (Indiana 2026), KCDPA (Kentucky 2026), RIDPA (Rhode Island 2026)
인터뷰는 영문·CCPA 용어로 진행합니다. (사용자 답변은 한국어로 해도 됩니다)
몇 가지만 여쭤볼게요.
법령 근거 (MUST READ)
./jurisdictions/us-ccpa/ccpa-checklist.md— CCPA/CPRA 15개 공개 의무·7대 권리·SPI 11종·주법 비교./references/glossary.md— 용어 풀이./references/design-system-detection.md
인터뷰 범위
./scripts/interview.md 중 다음만 수행:
유지 (공통)
- Step 1 서비스 소개
- Step 2 수집 항목 (CCPA 카테고리로 재분류)
- Step 6 처리위탁 (→ Service Providers)
- Step 9 시행일·개정
- Step 9-US 전부 (Q9US-1~10, 아래 참조)
- Step 10 디자인 스타일
- Step 11 최종 확인
생략 (한국 전용)
- Step 3·4·5·7·8 (CCPA에서는 Q9US로 대체)
Step 9-US 질문 (10문항)
Q9US-1. CCPA 적용 여부 확인
미국 CCPA는 다음 중 하나 해당 시 적용됩니다. (§1798.140(d), 2025.1.1 CPI 조정 기준)
1) 직전 회계연도 총 매출 USD 26,625,000 이상
2) 연간 100,000명 이상 CA 거주자·가구·기기 정보를 구매·판매·공유
3) 연 매출 50% 이상이 개인정보 판매·공유에서 발생
이 중 해당되는 것이 있나요? 매출·규모 적어도 캘리포니아 거주자를 대상으로 하면 주의 필요.
CPPA가 매년 1월에 CPI로 금액 재조정하므로 배포 전 최신 값 재확인 권장.
Q9US-2. 사업자 기본 정보
CCPA Privacy Policy에 반드시 포함되어야 할 정보입니다.
- 법인명
- 주소
- 연락 이메일
- 전화번호 (toll-free 권장)
- Privacy Officer 이름·이메일 (있는 경우)
Q9US-3. PI 카테고리·출처·목적·보유기간 매트릭스
CCPA는 수집한 개인정보를 "카테고리"별로 정리해 공개합니다.
예: Identifiers — 이름, 이메일, IP → 출처: Consumer / Device → 목적: 계정 관리, 분석 → 보유: 회원 탈퇴 후 1년
카테고리 기준 (§1798.140(v) 11종):
1. Identifiers (이름·이메일·IP·Customer ID)
2. Customer records (주소·전화·금융정보)
3. Protected class characteristics (성별·인종 등 민감)
4. Commercial information (구매·구독 기록)
5. Biometric information
6. Internet or network activity (브라우징·앱 이용)
7. Geolocation data
8. Sensory data (오디오·비디오)
9. Professional or employment-related information
10. Education information
11. Inferences (프로필 분석 결과)
수집 변수: piCategories[] = [{ category, examples, sources, purposes, retention }]
What ships with it
4 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.
- 1mo ago First seen · 288 lines · 101 tokens per session scan A 3a1da51a22cd
privacy-us is a skill published in the GitHub repository kimlawtech/korean-privacy-terms (591 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 101 tokens to every session and 2,894 once invoked, about $0.0004 per session on Opus 5.5. 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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