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 LZheng0411/Lzheng-fitness --skill lzheng-strength-training-reviewgit clone --depth 1 https://github.com/LZheng0411/Lzheng-fitnessWrote 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/lzheng0411/lzheng-fitness/lzheng-strength-training-review)<a href="https://agentmods.dev/skills/lzheng0411/lzheng-fitness/lzheng-strength-training-review"><img src="https://agentmods.dev/badge/skills/lzheng0411/lzheng-fitness/lzheng-strength-training-review/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/lzheng0411/lzheng-fitness/lzheng-strength-training-review"><img src="https://agentmods.dev/badge/skills/lzheng0411/lzheng-fitness/lzheng-strength-training-review.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 | $0.00121 | $0.02304 |
| Opus 5 | $0.00060 | $0.01152 |
| Sonnet 5 | $0.00024 | $0.00461 |
| Haiku 4.5 | $0.00012 | $0.00230 |
Grade A, and why
lzheng-strength-training-review 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.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lzheng—力量训练复盘
把单次训练或一个训练周放回可验证的推进路径。先核对事实，再收集个人体感，最后形成下一次处方或周度决策；不得用客观数字替代用户的真实感受。
必读上下文
- 动态训练事实：用户直接提供的完整当次记录优先；用户授权且当前环境可访问外部训练记录时，再查询最近记录、恢复信息和体重。
- 当前处方：读取用户明确指定的当前计划；未指定时先读取
系统/lzheng-system.json的output_locations.plans，尚未建立系统配置时再检查LZHENG_FITNESS_HOME/plans/和当前工作目录的lzheng-fitness-output/plans/。不要把历史 HTML 或保留副本当作当前处方。 - 周期调整：需要审核或修改多周计划时，读取 Lzheng 周期调整规则。即使没有安装周期规划 Skill，本 Skill 也必须能够完成调整判断。
- 无周期推进：进入滚动或基准模式时，读取 无周期滚动复盘规则。
- 周训练阶段复盘：读取 周训练复盘规则 和 周训练复盘模板。
- 输出与沉淀：读取 复盘输出规范 和 本地记录规范。
- 证据边界：读取 证据基础。
- 专家知识：仅当营养、肌肥大、计划结构、力量瓶颈、反复中断或已获专业允许活动后的返场变量会改变复盘时，按 训练专家选择协议 读取最少必要模块。
专家模块不得覆盖当次训练事实、个人体感、当前计划或下一次处方。实际采用时先展示来源限定判断与保留，再由本 Skill 在 Lzheng健身系统总结 中结合当前记录给出最终复盘；未采用时不增加专家区块。
模式选择
只选择一个模式：
- 周训练阶段复盘
weekly：用户说“这周复盘 / 周总结 / 本周训练怎么样”，或明确要求汇总一个 Wn；汇总该周全部训练日、主项暴露、重复问题与关键决策。 - 周期单练复盘
cycle：存在当前有效计划，并能核验计划版本、Wn、训练日和动作职责。 - 滚动渐进
rolling：没有当前有效周期，但至少存在两次可比记录；先审核用户现有渐进方式。 - 基准训练
baseline：没有当前有效周期，且只有一次或没有可靠可比记录。
疑似存在周期但版本、周次或训练职责无法对应时，先提出最少量澄清问题；不得为了给出答案擅自切换到滚动模式。周复盘的周次同样以当前计划和复盘索引为准，不按自然日期猜测。
共同硬门槛：体感与含糊记录
个人体感先于正式沉淀
在给出正式结论、修改周期或写入“已复盘”前，先问用户本次训练 / 本周的个人体感。若用户已经提供足够明确的体感，不重复提问。
- 单练至少问：整体感觉、最顺/最别扭的动作、疼痛或异常疲劳、与上次可比训练的差异。
- 周复盘至少问：整体恢复与训练意愿、最顺与最消耗的一节、疼痛/动作失控/心理抗拒，以及外部记录未体现但会影响下周的感受。
- 用户未回复时，可以保存事实副本为
待补全；不得把推测写成正式结论，也不得据此修改计划。安全红旗除外，应立即停止相关高强度推进并说明原因。
外部记录含糊时做最小追问
只在含糊字段影响安全、可比性或处方时追问，并明确缺什么、为何重要：
- RPE/RIR、重量、组数、次数、器械或正式组不清；
- “酸、累、不舒服、有感觉”等描述的部位、性质、时点和持续时间不清；
- 实际与处方差异明显但未说明原因；
- 负重引体等自重负重动作缺当天体重或动作标准。
不得把“不清楚”自动解释为正常疲劳、动作错误或计划失效。
重复问题必须问原因
同一类问题在两次可比训练或连续两周出现时，不能只写“下次注意”。必须向用户追问：是选重、额外加组、休息、刻意追求力竭、技术、生活恢复，还是处方本身不合适；并询问是否愿意调整。用户未回答前，只能给保守执行限制，不得重写正式计划。
What ships with it
8 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.
- agents/openai.yaml 328 B
- references/evidence-base.md 1.6 KB
- references/local-review-record-spec.md 2.3 KB
- references/lzheng-cycle-adjustment-rules.md 6.7 KB
- references/review-output-spec.md 2.4 KB
- references/rolling-review-rules.md 3.2 KB
- references/weekly-review-rules.md 3.4 KB
- references/weekly-review-template.md 2.6 KB
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 · 102 lines · 121 tokens per session scan A 36e2d29c7d01
lzheng-strength-training-review is a skill published in the GitHub repository LZheng0411/Lzheng-fitness (65 stars, last pushed 2d ago), licensed MIT. It adds 121 tokens to every session and 2,304 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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