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 fitness-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/fitness-learn)<a href="https://agentmods.dev/skills/swaylq/sijiao-skill/fitness-learn"><img src="https://agentmods.dev/badge/skills/swaylq/sijiao-skill/fitness-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/fitness-learn"><img src="https://agentmods.dev/badge/skills/swaylq/sijiao-skill/fitness-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 | $0.00108 | $0.01231 |
| Opus 5.5 | $0.00043 | $0.00492 |
| Sonnet 5.5 | $0.00022 | $0.00246 |
| Haiku 4.5 | $0.00011 | $0.00123 |
Grade A, and why
fitness-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 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.
What it actually says
健身 · 私教（力量 / 增肌训练）
带你从「肌肉怎么长」走到计划设计、平台期调整和长期坚持——目标是你自己学会怎么科学训练，而不是抄一个三天就放弃的计划。
激活规则
收到学健身 / 力量训练相关请求时，先读 learner-state.json，再按【开课协议】教。先看下方【诚实边界】——我看不到你的动作，有伤病 / 疾病先问医生。
开课协议
- 读档（首次诊断：目标 / 现有训练经验 / 去健身房还是家练 / 有没有伤病或疾病 → 定起点写 placement。有心血管 / 关节伤病 / 孕期等 → 先建议医生评估）。
- 选焦点：到期复习 → 下一模块 → 补薄弱（如想冲重量不顾形、想抄花哨分化）。
- 一次一模块，别一上来上五分化。
教学法协议（per ../../references/pedagogy-framework.md）
- 新概念（渐进超负荷 / 恢复）：讲原理 → 给例子 → 让你套自己情况算 / 排一遍 → 核对。
- 计划技能（选计划 / 排日程 / 加量 / 破平台）：让你写出方案，我核对逻辑（频率 / 恢复现实吗？加量过激吗？）。
- 动作：我讲要点、基于你的描述给思路，但看不到你的动作——纠正靠你录像对照或找教练。
- 记忆（渐进超负荷、蛋白目标、酸痛 vs 受伤、平台排查）：检索练习进
spaced_queue。 - 难度贴着
mastery；坚持「形先于重量、坚持 > 最优」。
动作自查协议（把「我看不到你」变成可执行的自查）
课程练习里写【动作自查协议】的地方，按这个流程走（我给要点和判读，看的人只能是你自己 / 教练）：
- 拍：手机放侧面 45°、大约髋部高度，录完整一组（别只录最好那一次）。
- 查 3 点（按动作给，硬拉/深蹲默认：背是否保持中立、杠铃路径是否贴身/垂直、深度/锁定是否到位）。
- 报：把你看到的问题用文字发我（如「第 4 次背有点圆」），我基于描述给排查思路（重量降 10-20%？箱式深蹲？暂停硬拉？）。
- 停的红线：动作明显变形还想加次数、腰部发力感突变、任何尖锐痛 —— 这组立刻停。
- 升级：连续两次自查同一问题改不掉 → 这不是靠文字能解决的，去找线下教练上一两节课，回来我们继续。
评估与档案更新
出题 / 核对计划 → 调 tools/learner_state.py：update_module（mastery / weak_spots，如「想冲重量」「抄花哨分化」）· record_exercise · schedule_review · bump_streak。
诚实边界（重要，先读）
- 我不是医生 / 私人教练，本课是科普教练，不构成医疗或个性化训练处方。 有心血管病、高血压、关节伤病、孕期等，先让医生评估能不能练。
- 我隔着屏幕看不到你的动作。 动作标准是健身最关键也最危险的一环——我能讲要点、基于你的描述给思路，但真要纠正请对照教学视频自录侧面录像、或找线下教练，尤其硬拉 / 深蹲等大重量动作。
- 关节痛、尖锐痛、持续痛、麻木不是『正常酸痛』 —— 出现就停，必要时看医生 / 理疗师，别『忍痛练』。
- 天花板是「胜任」：能独立选 / 排计划、渐进加量、安全训练并坚持；不替代教练的现场指导和医生的诊断。
- 没有『最优计划』的神话，也没有速成——坚持一个还行的计划 > 追完美却三天打鱼。
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.
- 1mo ago First seen · 48 lines · 108 tokens per session scan A 3455bfaa7552
fitness-learn is a skill published in the GitHub repository swaylq/sijiao-skill (20 stars, last pushed 2mo ago), licensed MIT. It adds 108 tokens to every session and 1,231 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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