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-training-returngit 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-training-return)<a href="https://agentmods.dev/skills/lzheng0411/lzheng-fitness/lzheng-training-return"><img src="https://agentmods.dev/badge/skills/lzheng0411/lzheng-fitness/lzheng-training-return/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-training-return"><img src="https://agentmods.dev/badge/skills/lzheng0411/lzheng-fitness/lzheng-training-return.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 | $0.00152 | $0.01691 |
| Opus 5 | $0.00076 | $0.00846 |
| Sonnet 5 | $0.00030 | $0.00338 |
| Haiku 4.5 | $0.00015 | $0.00169 |
Grade A, and why
lzheng-training-return 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lzheng 训练中断恢复
目标
把“中断了,不知道怎么回来”变成一条安全、低阻力、能在 48 小时内启动的接回路径。先判断是否适合恢复,再依据当前事实调整原计划;不把恢复期做成惩罚性补课,也不重写与中断无关的长期目标。
使用边界
满足任一条件时使用本 Skill:
- 停训达到 7 天;
- 连续漏练 3 次;
- 生病后恢复,或伤病已经过适当评估并获准恢复;
- 训练场地、器械、时间或身体能力明显变化;
- 4 周内反复中断;
- 用户主动询问中断恢复或重新开始训练。
以下情况不在本 Skill 内直接安排普通训练:
- 胸痛、晕厥、异常气短等需要医疗评估的症状;
- 疼痛加重、麻木、明显功能受限或尚未明确能否恢复运动;
- 急性发热、全身症状仍在持续;
- 用户无法确认关键安全信息。
漏练 1 次不补课,按原顺序继续。漏练 2 次由主 Skill 生成 30 分钟或状态差版,完成后接回原计划。
必读资料
开始诊断前完整读取:
references/knowledge-routing.mdreferences/return-workflow.mdreferences/return-card-spec.mdreferences/evidence-base.md../lzheng-training-expert-library/references/expert-selection-contract.md(仅在独立专家变量会改变接回路径时)
再按知识路由读取与当前情况相关的内置资料;容易变化的恢复安全标准应联网核验官方来源。不得仅凭模型记忆作出处方。
目标含糊、反复中断或“回到基础”是关键变量时可读取 Dan John;只有用户已经获得合格专业人员评估并允许活动,且问题是功能进阶、返场验证或二级预防时才读取 Brukner 与 Khan。未评估疼痛、急性创伤、术后未获许可、疾病或红旗症状先走安全分流,不进入专家讨论。专家不能批准恢复训练,也不能覆盖医疗限制、当前事实和本 Skill 的最终接回决定。
工作流
1. 读取当前执行基准
优先收集:
- 最近一份带时间戳的状态快照;
- 中断前正在执行的计划及最近完成训练;
- 本次用户确认的动态事实;
- 用户授权且当前环境可访问时,读取外部训练记录服务中的当前记录、日记和恢复状态;
- 中断前后的器械、时间和生活条件变化。
找不到原计划时明确标记 unknown,不得虚构原重量、频率或动作。必要时输出保守的重新评估周,而不是假装“接回原计划”。
2. 做最小恢复问诊
至少确认:
- 最后一次训练日期和中断时长;
- 中断原因与过去 4 周的反复情况;
- 当前症状、疼痛、活动受限及医疗建议;
- 睡眠、压力、食欲、主观精力;
- 当前可训练时间、场地与器械;
- 中断前计划、主要动作及可复现重量;
- 用户现在最愿意完成的最小训练任务。
把信息分为 confirmed、inferred、unknown,生成新的不可覆盖时间快照。不得覆盖中断前快照。
3. 判定恢复权限
输出以下之一:
normal_return:无明显安全阻断,条件基本未变,可进入保守接回;degraded_return:无明显危险信号,但恢复或条件不足,需要降低训练压力;minimum_return:心理或时间阻力很大,只安排能维持连续性的最低任务;hold_and_refer:当前不宜生成普通恢复计划,建议先获得合适的专业评估。
4. 选择接回路径
遵循 references/return-workflow.md:
- 保留原计划主线和熟悉动作;
- 第一周以出勤、动作感觉、疼痛反应和恢复为主要判据;
- 不补课、不叠加错过的训练量、不测试极限;
- 根据中断长度、原因和当前状态回退训练压力;
- 给出正常、降级、最低三档任务;
- 设定 48 小时内能完成的下一步;
- 明确何时升级、维持、继续降级或暂停。
如果中断导致动作阶段、器械或能力明显变化,调用 lzheng-fitness-plan 重新评估相关维度;只有用户明确要求或确认单项周期时,才调用 lzheng-strength-cycle-planner。
5. 保存产物
生成并保存:
- 新的状态快照;
- 《Lzheng 个人训练接回卡》;
- 如用户需要,更新后的第一周训练安排。
用户指定输出目录时优先使用;否则先读取已初始化系统的 系统/lzheng-system.json,把状态快照和接回卡写入 output_locations.returns;只有尚未建立系统配置时,才写入 LZHENG_FITNESS_HOME/returns/ 或当前工作目录的 lzheng-fitness-output/returns/。客户模式只使用代号。真实姓名、联系方式、病史原文不写入公共知识库或示例。
What ships with it
5 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.
- 12d ago First seen · 131 lines · 152 tokens per session scan A fb085a3c5237
lzheng-training-return is a skill published in the GitHub repository LZheng0411/Lzheng-fitness (65 stars, last pushed 2d ago), licensed MIT. It adds 152 tokens to every session and 1,691 once invoked, about $0.0008 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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