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 | $0.02960 |
| Opus 5.5 | $0.00048 | $0.01184 |
| Sonnet 5.5 | $0.00024 | $0.00592 |
| Haiku 4.5 | $0.00012 | $0.00296 |
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 6d 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 — 120 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。
主动同步个人工作台（每轮必须检查）
用户提供或更正个人信息、确认训练计划、补充实际训练或体感后，同轮将已知内容写入其对应主源，并主动运行工作台 refresh-workbench，检查正式 HTML 成功后再交付。不要等待用户再次说“刷新”，不要只写计划/交接文件就结束。没有待处理交接不代表页面已更新；必要时直接运行刷新闭环。
个人资料、训练计划与动作负荷校准分别判断。已经提供真实计划时立即展示，不能因体重、训练经验、部分重量或其他非阻断字段未知而把整套系统继续显示为“待建档”。未知字段分别标“待填写”“待校准”或留空，已知信息与处方照常展示；缺失且会影响训练执行的信息只限制对应执行建议，不隐藏其他已知资料。过程中每次取得新事实继续增量刷新，不补造数据、不自动改正式计划、不部署网站、不建立后台监听。刷新失败则说明具体缺口并继续修复，不能报告已更新。
共同训练判断协议
处理训练数据、RPE、下一次调整、计划渐进或失败反馈时，先读 训练数据判断与渐进协议。它统一力量、增肌、减脂保肌三类目标，允许有理由的多变量联合调整；优先于旧参考中的固定单变量和机械重复规则，不覆盖本 Skill 的数据读取、正式记录及计划写入权限。未知 RPE、体感或休息不抹掉已完成组；先给能确定的分析，影响具体决定的未知再轻量核实，不循环追问用户已表示不记得的内容。
维护内置 AI 或训练 Skill 的共同决策规则时，执行该协议末尾的联动维护检查，不仅更新一端。
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。
What ships with it
9 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 7.1 KB
- references/review-output-spec.md 2.6 KB
- references/rolling-review-rules.md 3.8 KB
- references/training-progression-protocol.md 20 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.
- 6d ago Changed · +18 lines e0c3f1c34a6d
- 2mo 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 (140 stars, last pushed 6d ago), licensed MIT. It adds 121 tokens to every session and 2,960 once invoked, about $0.0005 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.
lzheng-strength-training-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| lzheng-strength-training-reviewLZheng0411/Lzheng-fitness | Skill | 140 | 6d ago | MIT | A | 121 | Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |