lzheng-strength-cycle-planner

lzheng-strength-cycle-planner is a skill for Codex from LZheng0411/Lzheng-fitness. It costs 121 tokens per session (3,879 once invoked), scanned A, original, MIT.

A Chinese-language guide for designing an 8–12 week training cycle for a main strength exercise such as the squat, bench press, deadlift, weighted pull-up, or overhead press. It turns recent performance, goals, recovery, equipment, and schedule into a plan with progression and adjustment rules.

In plain words
What is it for?
Use it to plan sets, reps, weights, effort targets, back-off work, deloads, testing, and missed-session adjustments, with a locally viewable HTML training plan.
Why use it?
It helps make training changes based on current evidence and feedback instead of treating a fixed weight table as mandatory when performance or recovery changes.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to plan sets, reps, weights, effort targets, back-off work, deloads, testing, and missed-session adjustments, with a locally viewable HTML training plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lzheng0411/lzheng-fitness/lzheng-strength-cycle-planner
Install

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.

Any agent
npx skills add LZheng0411/Lzheng-fitness --skill lzheng-strength-cycle-planner
Clone the repo
git clone --depth 1 https://github.com/LZheng0411/Lzheng-fitness

Made for: Codex.

Wrote 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.

agentmods badge for lzheng-strength-cycle-planner

README.md
[![agentmods](https://agentmods.dev/badge/skills/lzheng0411/lzheng-fitness/lzheng-strength-cycle-planner/github.svg)](https://agentmods.dev/skills/lzheng0411/lzheng-fitness/lzheng-strength-cycle-planner)
Your own site
<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.

agentmods 80×15 button for lzheng-strength-cycle-planner

Your own site · 80×15
<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>
Per session 121 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,879 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00121 $0.03879
Opus 5.5 $0.00048 $0.01552
Sonnet 5.5 $0.00024 $0.00776
Haiku 4.5 $0.00012 $0.00388

Measured 6d ago against content hash d963052d6e58, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 6d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/inline_html_image.py, scripts/render_strength_cycle_html.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/lzheng-strength-cycle-planner/SKILL.md · 168 lines

How it starts

The opening of the file, as written. The whole thing — 168 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。

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。

必读参考

生成或调整计划前读取:

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。

Read the full file on GitHub · 168 lines

Changes

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.

  1. 6d ago Changed · +18 lines d963052d6e58
  2. 2mo ago First seen · 150 lines · 121 tokens per session scan A 81a378811f90

Subscribe to this mod's changes

lzheng-strength-cycle-planner 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 3,879 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.

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