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-cycle-plannergit 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-cycle-planner)<a href="https://agentmods.dev/skills/lzheng0411/lzheng-fitness/lzheng-strength-cycle-planner"><img src="https://agentmods.dev/badge/skills/lzheng0411/lzheng-fitness/lzheng-strength-cycle-planner/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-cycle-planner"><img src="https://agentmods.dev/badge/skills/lzheng0411/lzheng-fitness/lzheng-strength-cycle-planner.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 | $0.03246 |
| Opus 5 | $0.00060 | $0.01623 |
| Sonnet 5 | $0.00024 | $0.00649 |
| Haiku 4.5 | $0.00012 | $0.00325 |
Grade A, and why
lzheng-strength-cycle-planner 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lzheng—力量训练周期规划
为一个明确的力量动作生成可执行、可反馈、可调整的 8—12 周周期。把计划视为待验证的训练假设，不把固定重量表当作必须完成的命令。
必读参考
生成或调整计划前读取：
- 周期设计规则：底层模型、阶段选择、动作差异和调整规则。
- 问诊与输出规范：最少输入、完整表格和未完成分支格式。
- 训练计划网页规范：数据核验、HTML 交付、视觉和响应式要求。
- 周期 HTML 与曲线契约：固定页面框架、图表口径、JSON 数据结构和验收要求。
- 证据基础：说明周期适用边界、来源和最新指南核验规则。
- 训练专家选择协议：在计划结构、力量瓶颈或训练阶段变量会改变周期时读取最少必要模块。
专家库只提供约束与判断：一般结构优先 Eric Helms，力量停滞、专项性、变量实验或峰值使用 Greg Nuckols + Eric Helms，目标与现实脱节或是否回到基础时才使用 Dan John。当前表现、周期重量、最终处方和写入权始终归本 Skill；没有真实交叉变量时不制造多人讨论。
任务边界
- 一次优先规划一个主项；需要多个主项时，先分别建立动作周期，再检查它们在同一周中的疲劳冲突。
- 生成动作周期，不擅自重写用户整套训练计划；必须读取或询问会影响该动作的现有训练安排。
- 当前训练记录、体重和完成数据属于动态信息。用户授权且当前环境能访问训练记录服务时优先查询最近记录和当前周期；否则使用用户本次提供的数据。旧计划、历史 PR、旧身体数据不能冒充当前事实。
- 只在用户明确要求时写回外部记录服务；但制定或重做训练计划时，默认交付本地 HTML 页面，除非用户明确说不要文件或网页。
- 不提供医学诊断。出现锐痛、麻木、放射痛或持续加重的关节疼痛时，停止高强度推进并建议线下专业评估。
执行流程
1. 核验基本信息和训练事实
先建立“已核验 / 用户直接提供 / 缺失或冲突”的事实表。用户已给出的内容不要重复询问；缺少会改变计划结构的信息时，用一轮紧凑问题补齐，不要边猜边生成精确重量表。
至少确认：
- 使用者、当前日期和计划适用周期；不要复用其他人的数据。
- 目标动作、动作标准和目标成绩；
- 近期真实成绩：重量、组数、次数、RPE/RIR和动作质量；
- 当前训练频率、相关训练日和辅助/变式动作；
- 训练年限及该动作的熟练度；
- 可用器械、最小加重单位和单次时长；
- 近期完成度、恢复、疼痛或技术限制；
- 是否有测试日期，以及希望采用 8、10 或 12 周。
负重自重动作还要确认体重，并使用“体重 + 附加重量”判断总系统负荷。
出现冲突时，优先级为：用户当次明确确认的近期完整记录 > 已授权且可核验的当前训练记录 > 用户明确标注为历史的成绩 > 估算。记录日期、动作名称和重量单位；器械重量、总系统负荷和附加重量不得混写。
2. 判断是否适合多周周期
- 新动作、技术不稳定或仍能逐次线性加重：采用动作学习或简单线性周期，但仍可按用户要求整理为 8 周表格；不要伪装成复杂高级周期。
- 单次加重已经不稳定、训练记录可靠：使用多周周期。
- 主项暴露频率很低：按“有效暴露次数”规划，不按自然周强行加重。
3. 确定周期长度和阶段
- 8 周：目标单一、动作频率较高、只需要一次积累与一次转化。
- 10 周：一般力量发展，兼顾积累、强度与验证。
- 12 周：训练频率低、需要更慢推进，或需要完整积累、强度、实现与减量。
根据目标分配积累期、强度期、实现期和减量/验证期。阶段长度不是固定生理定律；以有效暴露次数、表现趋势和恢复反馈为准。
4. 安排每次训练的职责
明确每次暴露只承担一个主要任务：强度、训练量、技术、变式或恢复。
- 顶组用于校准当天状态、练习较高负荷和估算当前能力。
- 回退组是顶组后的计划内正式训练，用较低重量积累主要训练量。
- 未完成分支是计划没有按目标完成时替代原安排的条件分支，不是额外追加的训练组。
不要混淆“回退组”和“未完成/退行分支”。
5. 生成重量和组次
What ships with it
10 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 353 B
- assets/header-lineart.png 1441 KB
- assets/strength-cycle-template.html 6.0 KB
- references/cycle-design-rules.md 6.7 KB
- references/cycle-html-contract.md 4.6 KB
- references/evidence-base.md 1.6 KB
- references/output-spec.md 5.5 KB
- references/training-plan-website-spec.md 6.2 KB
- scripts/inline_html_image.py 1.3 KB runs code
- scripts/render_strength_cycle_html.py 15 KB runs code
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 · 150 lines · 121 tokens per session scan A 81a378811f90
lzheng-strength-cycle-planner 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 3,246 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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