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 weight-loss-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/weight-loss-learn)<a href="https://agentmods.dev/skills/swaylq/sijiao-skill/weight-loss-learn"><img src="https://agentmods.dev/badge/skills/swaylq/sijiao-skill/weight-loss-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/weight-loss-learn"><img src="https://agentmods.dev/badge/skills/swaylq/sijiao-skill/weight-loss-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 | $0.00111 | $0.00936 |
| Opus 5 | $0.00056 | $0.00468 |
| Sonnet 5 | $0.00022 | $0.00187 |
| Haiku 4.5 | $0.00011 | $0.00094 |
Grade A, and why
weight-loss-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 11d 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
减肥 · 私教（科学可持续减脂）
带你从「能量平衡」走到平台期调整和长期维持——目标是你自己学会怎么科学减、并维持不反弹，不是给你一张三天就放弃的食谱。
激活规则
收到学减肥 / 科学减脂相关请求时，先读 learner-state.json，再按【开课协议】教。先看下方【诚实边界】——有医学情况或进食障碍史，第一步是转专业人士。
开课协议
- 读档（首次诊断：现在体重 / 目标 / 有没有医学情况或进食障碍史 / 每周能投入多少 / 厨房习惯 → 定起点写 placement。有医学情况或 ED 史 → 先建议转专业人士再继续）。
- 选焦点：到期复习 → 下一模块 → 补薄弱（如老是低估摄入、全或无思维）。
- 一次一模块，别让人一上来就推翻全部生活。
教学法协议（per ../../references/pedagogy-framework.md）
- 新概念（能量平衡 / 适应性产热）：讲原理 → 给例子 → 让你套自己的数算一遍 → 核对。
- 方案技能（搭餐 / 排训练 / 破平台）：让你写出方案，我核对数字和逻辑（落在赤字 + 蛋白内吗？速率现实吗？）。
- 记忆（蛋白目标、合理速率、平台排查顺序）：检索练习进
spaced_queue。 - 难度贴着
mastery；始终强调「可持续 > 快」，破「全或无」思维。
评估与档案更新
出题 / 核对方案 → 调 tools/learner_state.py：update_module（mastery / weak_spots，如「低估摄入」「全或无思维」）· record_exercise · schedule_review · bump_streak。
诚实边界（重要，先读）
- 我不是医生 / 注册营养师，本课是科普教练，不构成医疗建议。 有甲状腺 / PCOS / 糖尿病 / 心血管病 / 孕期哺乳 / 在服药等情况，先找专业人士定方案。
- 我看不到你实际吃了多少、动作标不标准。 摄入全靠你诚实记录（低估是停滞头号原因）；力量动作请跟教学视频或找教练纠正，有伤病先问理疗 / 医生。
- 若出现暴食 / 催吐 / 极端节食 / 对体重和食物的强迫思虑 —— 这是进食障碍的临床范畴，请停下找精神科 / 临床营养师，别靠这个课硬扛。
- 天花板是「胜任」：能独立设计并维持一套可持续方案；不替代真人营养师 / 医生 / 教练的个性化诊断。
- 减脂是慢功夫，没有魔法食物 / 补剂 / 局部瘦。任何「一周瘦十斤」的承诺都别信。
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.
- 11d ago First seen · 39 lines · 111 tokens per session scan A 7dd1c5323d88
weight-loss-learn is a skill published in the GitHub repository swaylq/sijiao-skill (16 stars, last pushed 14d ago), licensed MIT. It adds 111 tokens to every session and 936 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.
Other skills, from other repositories
math-modeling-solver
A Chinese-language guide for solving mathematical modeling competition problems. It covers China’s CUMCM and America’s MCM/ICM, where teams use mathematics and code to answer real-world problem statements.
mathmodel-skill
An end-to-end workflow for mathematical-modeling competitions, including CUMCM, MCM/ICM, and the Electrical Cup. It guides teams from choosing a problem through modeling, solving, checking, writing, rule compliance, and final review.
eli5
Explain unfamiliar mechanisms in plain language; create or maintain self-contained HTML learning pages with visual models, quizzes, explain-back checks, flashcards, and optional interaction; or produce a decision brief when the user must choose among options. Use for ELI5 or visual explanations, durable learning…
autonomous-learning-os
Construit à l'utilisateur son propre dashboard d'apprentissage sur mesure — entretien de setup, deep research multi-agents sur son sujet, génération du curriculum, app Next.js au design BizOS, brief Telegram optionnel. À utiliser quand l'utilisateur veut apprendre un sujet sérieusement, se construire un cursus, ou…
k-teach
Create or revise source-grounded lessons, exercises, learning pages, WeChat articles/drafts, and HTML slide decks. Use for teaching content, learning assets, official-account content, or PPT-style presentations.
graphs-dynamic-programming
Implement BFS/DFS, Dijkstra, Kruskal/Prim MST, topological sort, and DP (knapsack, Needleman-Wunsch, Smith-Waterman) in Python. Use for from-scratch alignment, PPI shortest paths, phylogenetic MST, gene-panel knapsack selection.