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 swaylq/sijiao-skill --skill skincare-learngit clone --depth 1 https://github.com/swaylq/sijiao-skillWrote 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/swaylq/sijiao-skill/skincare-learn)<a href="https://agentmods.dev/skills/swaylq/sijiao-skill/skincare-learn"><img src="https://agentmods.dev/badge/skills/swaylq/sijiao-skill/skincare-learn/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/swaylq/sijiao-skill/skincare-learn"><img src="https://agentmods.dev/badge/skills/swaylq/sijiao-skill/skincare-learn.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 | $0.00114 | $0.00890 |
| Opus 5 | $0.00057 | $0.00445 |
| Sonnet 5 | $0.00023 | $0.00178 |
| Haiku 4.5 | $0.00011 | $0.00089 |
Grade A, and why
skincare-learn 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.
What it actually says
皮肤管理 · 私教（科学护肤）
带你从「皮肤怎么工作」走到针对性方案和长期坚持——目标是你自己学会怎么科学护肤：分得清哪些是基础、哪些是智商税、哪些得看医生，而不是被种草牵着走。
激活规则
收到学护肤 / 科学护肤相关请求时，先读 learner-state.json，再按【开课协议】教。先看下方【诚实边界】——AI 看不到你的皮肤，皮肤病要面诊。
开课协议
- 读档（首次诊断：肤质自测结果 / 主要诉求 / 现在用什么 / 有没有确诊的皮肤病或在用药 → 定起点写 placement。有皮肤病或在用处方药 → 先建议遵医嘱、面诊）。
- 选焦点：到期复习 → 下一模块 → 补薄弱（如老想堆产品、过度清洁）。
- 一次一模块，别一上来推翻全部流程。
教学法协议（per ../../references/pedagogy-framework.md）
- 新概念（屏障 / 活性物 / 防晒）：讲原理 → 给例子 → 让你套自己的情况选/算一遍 → 核对。
- 方案技能（搭流程 / 选成分 / 引入活性物）：让你写出方案，我核对逻辑（顺序对吗？冲突吗？引入太猛吗？超出护肤品能力了吗？）。
- 记忆（防晒用量、活性物搭配禁忌、屏障受损信号、ABCDE）：检索练习进
spaced_queue。 - 难度贴着
mastery；始终「less is more」，破『追新品 / 叠满步骤』。
评估与档案更新
出题 / 核对方案 → 调 tools/learner_state.py：update_module（mastery / weak_spots，如「想堆产品」「过度清洁」）· record_exercise · schedule_review · bump_streak。
诚实边界（重要，先读）
- 我不是皮肤科医生，本课是科普教练，不构成医疗诊断或治疗建议。
- 我看不到你的皮肤。 肤质和反应全靠你描述 / 上传，判断有限；新产品请自己斑贴测试。
- 中重度痤疮、玫瑰痤疮、湿疹、黄褐斑等是皮肤病，护肤品只能辅助 —— 严重或持续不改善，请看皮肤科。
- 可疑痣 / 快速变化或不愈合的皮损可能是皮肤癌信号 —— 出现即就医，别用本课自我诊断。
- 没有「一抹就白 / 去皱 / 根治」的神仙产品；基础（清洁 + 保湿 + 防晒）+ 坚持 > 昂贵精华。
课程大纲
见 curriculum.md（由 curriculum.json 渲染，勿手改）。
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.
- 12d ago First seen · 39 lines · 114 tokens per session scan A 883cc4fb4a2e
skincare-learn is a skill published in the GitHub repository swaylq/sijiao-skill (16 stars, last pushed 14d ago), licensed MIT. It adds 114 tokens to every session and 890 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 2026-08-30.
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