Skill: sirsws/akm-fitness-planner (version 1.0.2)

--- name: "akm-fitness-planner" description: "AKM implementation for training decision workflows. Models goals, body limits, equipment context, time budget, and recovery before outputting a workout decision." --- # AKM Fitness AKM Fitness is a profile-first training decision skill for real-world constraints. It does not start with a workout template. It starts by modeling the operator: goals, body limits, equipment reality, time budget, recovery state, and adherence risk. ## Quick Reference | Item | Details | | --- | --- | | Primary outcome | a constraint-aware training decision | | Best use case | when generic workout advice fails because the user's actual constraints matter | | Public source | `https://github.com/sirsws/akm/tree/main/branches/fitness` | | Sample record | `https://github.com/sirsws/akm/blob/main/branches/fitness/skill/SAMPLE_RECORD.md` | | Research paper | `https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6231465` | | Install source | `https://github.com/sirsws/akm` | ## Public Links - GitHub branch: `https://github.com/sirsws/akm/tree/main/branches/fitness` - Skill files: `https://github.com/sirsws/akm/tree/main/branches/fitness/skill` - Sample record: `https://github.com/sirsws/akm/blob/main/branches/fitness/skill/SAMPLE_RECORD.md` - Branch paper: `https://github.com/sirsws/akm/tree/main/branches/fitness/paper` - SSRN paper: `https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6231465` ## Installation ClawHub / OpenClaw: - install from this listing Skills CLI: ```bash npx skills add https://github.com/sirsws/akm --skill akm-fitness-planner --full-depth ``` ## What This Skill Changes Most fitness agents answer too early. They assume the user has one clear goal, normal recovery, enough time, stable equipment, and no meaningful body limitation. That assumption is usually false. AKM Fitness changes the order of operations: profile first, decision second. ## When to Use Use this skill when: - injuries or physical limits materially affect training choices - equipment availability changes what is realistic - time budget is hard and cannot be ignored - recovery state should alter the decision - generic training plans keep failing in practice Do not use this skill when: - the user only wants casual fitness chat - a medical diagnosis is being requested - rehabilitation planning should be handled by a qualified clinician - critical inputs are missing and the user refuses to provide them ## Core Rule **No profile, no serious plan.** If the necessary state is unclear, the skill should surface `MissingInputs` instead of fabricating confidence. ## Workflow 1. `ELICITATION_PROMPT.md` 2. `RECORD_TEMPLATE.md` 3. `EXECUTION_PROMPT.md` 4. `SAMPLE_RECORD.md` ## Output Contract Outputs should include: - `StateJudgment` - `PrimaryDecision` - `DecisionConfidence` - `Plan` - `RiskNotes` - `NonNegotiables` - `MissingInputs` ## Operating Boundary AKM Fitness is a training decision aid. It is not a medical diagnosis tool, not a rehab replacement, and not a motivational wrapper for missing information. ## Files to Load Read these files before running the skill: - `ELICITATION_PROMPT.md` - `RECORD_TEMPLATE.md` - `EXECUTION_PROMPT.md` - `SAMPLE_RECORD.md`

path: agent-store-copy.md
content: <!--
文件：agent-store-copy.md
核心功能：作为 Fitness skill 的短版正式商店文案，供 agent store 类页面直接复用。
输入：Fitness 分支的 AKM 定位与核心价值。
输出：可直接上架的短版货架文案。
-->

# AKM Fitness Agent Store Copy

## Title

**AKM Fitness: Constraint-Aware Training Planner**

## Short Pitch

Most workout agents assume too much.
AKM Fitness models the user first, then decides what training is actually appropriate.

## Core Message

This is not a generic plan generator.
It builds a real training profile around goals, constraints, equipment, recovery, and time budget before producing output.

## Best Fit

Use it when you need:

- constraint-aware workout planning
- realistic programming under time limits
- less generic fitness output
- a system that can expose missing inputs instead of faking certainty

## One-line Close

**No profile, no serious plan.**

path: agent-store-copy.zh-CN.md
content: <!--
文件：agent-store-copy.zh-CN.md
核心功能：作为 Fitness skill 的中文短版商店文案，供 agent store 类页面直接复用。
输入：Fitness 分支的 AKM 定位与核心价值。
输出：可直接上架的中文短版货架文案。
-->

# AKM Fitness Agent Store Copy

## Title

**AKM Fitness：约束感知训练规划器**

## Short Pitch

多数训练智能体默认得太多。
AKM Fitness 会先建模用户，再判断今天到底该怎么练。

## Core Message

这不是泛泛的训练计划生成器。
它会先围绕目标、约束、器械、恢复和时间预算建立训练画像，然后才输出结果。

## Best Fit

适合这些场景：

- 需要考虑约束的训练规划
- 时间很紧但还想做现实可执行安排
- 不想再看泛化健身输出
- 希望系统在信息不够时能明确暴露缺口，而不是装作很确定

## One-line Close

**没有画像，就没有严肃训练决策。**


path: clawhub-copy.md
content: # AKM Fitness ClawHub Skill Page

## Skill Title

**AKM Fitness: Constraint-Aware Training Planner**

## One-line Description

Build the training profile first, then make workout decisions that respect body limits, equipment reality, recovery, and time budget.

## Install

```bash
npx skills add https://github.com/sirsws/akm --skill akm-fitness-planner --full-depth
```

## Public Sample

- GitHub sample record: `https://github.com/sirsws/akm/blob/main/branches/fitness/skill/SAMPLE_RECORD.md`

## What It Is

Most fitness agents answer too early.

They assume the user has one clear goal, enough time, normal recovery, and a stable gym environment.
That is usually false.

AKM Fitness fixes this by forcing the system to model the operator first:

- goal priority
- body constraints
- equipment context
- time budget
- recovery state
- adherence risks

Only then does it make a training decision.

## What Makes It Different

This is not just a workout prompt.
It is a three-stage method:

1. elicitation
2. structured record
3. execution decision

The output is not generic programming language.
It is a constraint-aware training judgment.

## Best Fit

Use this skill when:

- injuries or physical limits materially affect training
- equipment availability changes the decision
- the user needs realistic planning under hard time limits
- generic plans keep failing in practice

## Boundary

- not a medical diagnosis tool
- not a rehab replacement
- not a bodybuilding template generator
- does not pretend missing critical inputs are known

## Closing Line

**No profile, no serious plan.**


path: clawhub-copy.zh-CN.md
content: <!--
文件：clawhub-copy.zh-CN.md
核心功能：作为 Fitness skill 的中文长版发布页文案，供 ClawHub / OpenClaw 或同类技能市场直接复用。
输入：Fitness 分支的 AKM 定位、方法结构与使用边界。
输出：可直接上线的中文技能页文案。
-->

# AKM Fitness ClawHub Skill Page

## Skill Title

**AKM Fitness：约束感知训练规划器**

## One-line Description

先把训练画像建出来，再做尊重身体限制、器械现实、恢复状态和时间预算的训练决策。

## What It Is

多数健身智能体答得太早。

它们默认用户目标单一、时间充足、恢复正常、器械稳定。
现实通常不是这样。

AKM Fitness 的修正方式，是先把操作者模型建出来：

- 目标优先级
- 身体限制
- 器械环境
- 时间预算
- 恢复状态
- 依从性风险

只有在这些信息清楚之后，它才输出训练决策。

## What Makes It Different

这不是一个训练 prompt。
它是一套三段式方法：

1. elicitation
2. structured record
3. execution decision

输出也不是泛泛的编程语言。
它输出的是考虑约束的训练判断。

## Best Fit

适合这些情况：

- 旧伤或身体限制会实质影响训练
- 器械可用性会改变决策
- 用户需要在硬时间约束下做现实规划
- 泛化训练计划在实践中一直失效

## Boundary

- 不是医疗诊断工具
- 不替代康复建议
- 不是健美模板生成器
- 不会把关键缺失输入假装成已知

## Closing Line

**没有画像，就没有严肃训练决策。**


path: ELICITATION_PROMPT.md
content: <!--
文件：ELICITATION_PROMPT.md
核心功能：作为 Fitness 分支的英文前置挖掘提示词，在生成训练方案前主动挖掘目标、身体限制、器械环境与执行条件。
输入：用户关于健身目标、身体情况、器械环境、时间预算、恢复与执行情况的回答。
输出：供记录模板与执行提示词使用的结构化训练画像。
-->

# Fitness Elicitation Prompt

You are not here to prescribe training immediately.
Your first job is to **make the training profile explicit**.

## Task

Before producing any plan, actively elicit a reusable fitness profile so that downstream execution can work from real constraints.

## Elicitation Priorities

### 1. Primary Goal

Force the user to rank the current top priority:

- fat loss
- muscle gain
- conditioning
- physique
- combat / function
- post-injury rebuilding

If the user wants multiple goals, require a ranking. Do not accept “all of them”.

### 2. Body Constraints

You must confirm:

- current pain, old injuries, joint limits, spine limits
- medically prohibited items
- movements already known to be poor fits
- unusual recent recovery issues

If this part is vague, keep asking. Do not move into a full plan.

### 3. Equipment Context

You must confirm:

- training location
- available equipment list
- missing key equipment
- whether the location changes often

### 4. Time Budget

You must confirm:

- sessions per week
- hard upper limit per session
- periods that are completely unavailable

### 5. Execution and Recovery

You must confirm:

- whether the last 2-4 weeks were consistent
- whether food intake is tracked at all
- whether sleep and recovery are normal
- which part of execution fails most often

## Output Format

Produce a structured profile with at least:

- `PrimaryGoal`
- `SecondaryGoals`
- `BodyConstraints`
- `EquipmentContext`
- `TimeBudget`
- `RecoveryContext`
- `AdherenceRisks`
- `MissingInputs`

## Hard Rules

- if a critical input is missing, mark it in `MissingInputs`
- do not treat vague self-description as fact
- do not output a full training plan unless the profile is stable enough


path: ELICITATION_PROMPT.zh-CN.md
content: <!--
文件：ELICITATION_PROMPT.zh-CN.md
核心功能：作为 Fitness 分支的中文前置挖掘提示词，在生成训练方案前主动挖掘目标、身体限制、器械环境与执行条件。
输入：用户关于健身目标、身体情况、器械环境、时间预算、恢复与执行情况的回答。
输出：供记录模板与执行提示词使用的结构化训练画像。
-->

# Fitness Elicitation Prompt

你不是先开训练单的教练。
你先做一件事：**把训练画像挖清楚。**

## 任务

你的工作不是立即给方案，而是先通过追问建立一个可复用的健身画像，使下游训练规划模块能基于真实约束做决策。

## 追问优先级

### 1. 主线目标

先逼问用户当前最优先的一个目标：

- 减脂
- 增肌
- 体能
- 外形
- 格斗 / 功能性
- 康复后重建

如果用户同时想要多个目标，必须要求他排序，不接受“都要”。

### 2. 身体限制

必须确认：

- 当前疼痛、旧伤、关节限制、脊柱限制
- 医生明确禁止项
- 已知不适合自己的动作
- 近期恢复异常

如果用户在限制信息上模糊，直接继续追问，不得进入正式方案。

### 3. 器械环境

必须确认：

- 训练场地
- 可用器械清单
- 缺失的关键器械
- 是否会频繁换场地

### 4. 时间预算

必须确认：

- 每周可练几次
- 单次上限多少分钟
- 哪些时间完全不可训练

### 5. 执行与恢复

必须确认：

- 最近 2-4 周训练是否连续
- 饮食是否有记录
- 睡眠与恢复是否正常
- 用户最常失控的环节是什么

## 输出格式

输出为结构化训练画像，至少包含：

- `PrimaryGoal`
- `SecondaryGoals`
- `BodyConstraints`
- `EquipmentContext`
- `TimeBudget`
- `RecoveryContext`
- `AdherenceRisks`
- `MissingInputs`

## 硬规则

- 缺关键输入时，直接标 `MissingInputs`
- 不把模糊自述当事实
- 不给完整训练单，除非画像已足够稳定


path: EXECUTION_PROMPT.md
content: <!--
文件：EXECUTION_PROMPT.md
核心功能：作为 Fitness 分支的英文执行提示词，在画像与记录存在的前提下输出日级训练决策。
输入：FitnessProfile、近期训练状态、恢复与风险信息。
输出：显式暴露缺口与置信度的训练决策结构。
-->

# Fitness Execution Prompt

You are not here to output a motivational workout plan.
You are here to make a **constraint-aware training decision**.

## Decision Rule

Use the stored `FitnessProfile` and the latest available state.
If critical state variables are missing, do not pretend they are known.
Shrink confidence, expose `MissingInputs`, and reduce the decision scope.

## Required Output

- `StateJudgment`
- `PrimaryDecision`
- `DecisionConfidence`
- `Plan`
- `RiskNotes`
- `NonNegotiables`
- `MissingInputs`

## Hard Rules

- do not infer readiness from schedule alone
- do not overwrite pain, injury, or recovery uncertainty with split logic
- when critical inputs remain open, produce a conservative placeholder decision or pause
- explain why the decision is narrow if the state is narrow


path: EXECUTION_PROMPT.zh-CN.md
content: <!--
文件：EXECUTION_PROMPT.zh-CN.md
核心功能：作为 Fitness 分支的中文执行提示词，在画像与记录存在的前提下输出日级训练决策。
输入：FitnessProfile、近期训练状态、恢复与风险信息。
输出：显式暴露缺口与置信度的训练决策结构。
-->

# Fitness Execution Prompt

你不是来输出鸡血训练单的。
你是来做一个**考虑约束的训练决策**。

## 决策规则

使用已经记录的 `FitnessProfile` 和最新状态。
如果关键状态变量缺失，不得假装已知。
必须降低置信度、暴露 `MissingInputs`，并主动收缩决策范围。

## 必须输出

- `StateJudgment`
- `PrimaryDecision`
- `DecisionConfidence`
- `Plan`
- `RiskNotes`
- `NonNegotiables`
- `MissingInputs`

## 硬规则

- 不得仅凭日程推断准备状态
- 不得用训练分化模板覆盖疼痛、旧伤或恢复不确定性
- 关键输入没闭合时，只能给保守占位决策或暂停建议
- 如果输出变窄，必须解释为什么变窄


path: INPUT_TEMPLATE.md
content: <!--
文件：INPUT_TEMPLATE.md
核心功能：作为 Fitness 分支的英文最小输入模板，帮助用户提供首次建模所需的核心信息。
输入：用户初次提供的目标、限制、器械、时间与恢复情况。
输出：可直接交给前置挖掘模块使用的最小输入骨架。
-->

# Fitness Input Template

- `training goals`:
- `body limitations or injuries`:
- `available equipment`:
- `weekly frequency`:
- `session time budget`:
- `current recovery notes`:
- `recent execution problems`:


path: INPUT_TEMPLATE.zh-CN.md
content: <!--
文件：INPUT_TEMPLATE.zh-CN.md
核心功能：作为 Fitness 分支的中文最小输入模板，帮助用户提供首次建模所需的核心信息。
输入：用户初次提供的目标、限制、器械、时间与恢复情况。
输出：可直接交给前置挖掘模块使用的最小输入骨架。
-->

# Fitness Input Template

- `training goals`：
- `body limitations or injuries`：
- `available equipment`：
- `weekly frequency`：
- `session time budget`：
- `current recovery notes`：
- `recent execution problems`：


path: PUBLISHING_CHECKLIST.md
content: <!--
文件：PUBLISHING_CHECKLIST.md
核心功能：作为 Fitness 分支公开发布前的英文检查清单，避免私有数据泄露、口径错位和结构缺失。
输入：Fitness 分支当前 skill 包、论文稿与发布页文案。
输出：供发布前逐项核对的英文检查清单。
-->

# Fitness Publishing Checklist

## Framing

- [ ] title clearly identifies this as the Fitness branch of AKM rather than a standalone parent concept
- [ ] claims do not drift into medical system, rehab system, or broad efficacy validation language
- [ ] paper framing is consistent with a method paper under real constraints

## Privacy

- [ ] no raw training log is exposed in full
- [ ] no complete personal body-history record is exposed
- [ ] identity-specific details are removed or generalized
- [ ] body limitations are rewritten as reusable input fields rather than named self-description

## Structure

- [ ] `ELICITATION_PROMPT.md` is readable on its own
- [ ] `RECORD_TEMPLATE.md` is reusable on its own
- [ ] `EXECUTION_PROMPT.md` does not conflict with the other two
- [ ] `SKILL.md` makes it explicit that the method is not a single prompt
- [ ] store copy and skill copy use the same framing


path: PUBLISHING_CHECKLIST.zh-CN.md
content: <!--
文件：PUBLISHING_CHECKLIST.zh-CN.md
核心功能：作为 Fitness 分支公开发布前的中文检查清单，避免私有数据泄露、口径错位和结构缺失。
输入：Fitness 分支当前 skill 包、论文稿与发布页文案。
输出：供发布前逐项核对的中文检查清单。
-->

# Fitness Publishing Checklist

## 口径

- [ ] 标题明确写成 AKM 的 Fitness 分支，而非独立母概念
- [ ] 不漂移成医疗系统、康复系统或广义疗效验证语言
- [ ] 论文口径与“真实约束下的方法论文”保持一致

## 隐私

- [ ] 不直接公开完整训练日志
- [ ] 不直接公开完整私人身体历史记录
- [ ] 仅对邵先生成立的身份细节已脱敏或泛化
- [ ] 身体限制被改写为可复用输入字段，而不是实名自述

## 结构

- [ ] `ELICITATION_PROMPT.md` 可独立阅读
- [ ] `RECORD_TEMPLATE.md` 可独立复用
- [ ] `EXECUTION_PROMPT.md` 与前两者无冲突
- [ ] `SKILL.md` 已明确方法不是单 Prompt
- [ ] 商店文案与 skill 文案使用同一口径


path: README.md
content: <!--
文件：README.md
核心功能：作为 Fitness skill 的英文入口页，说明用途、方法分层、双语文件、公开样本与输出方向。
输入：Fitness 分支的挖掘逻辑、记录模板、执行提示词、公开样本与 skill 设计。
输出：供 GitHub 或技能页直接引用的英文 README。
-->

# Fitness Skill

<p align="center">
  <a href="./README.md">English</a> | <a href="./README.zh-CN.md">简体中文</a>
</p>

## Purpose

The Fitness skill is the operational entry point for AKM in training decision workflows.
It is organized as a three-stage method rather than a single prompt.

## Method Layers

1. elicitation
2. structured record
3. execution decision

## Bilingual Files

- [SKILL.md](./SKILL.md)
- [SKILL.zh-CN.md](./SKILL.zh-CN.md)
- [ELICITATION_PROMPT.md](./ELICITATION_PROMPT.md)
- [ELICITATION_PROMPT.zh-CN.md](./ELICITATION_PROMPT.zh-CN.md)
- [RECORD_TEMPLATE.md](./RECORD_TEMPLATE.md)
- [RECORD_TEMPLATE.zh-CN.md](./RECORD_TEMPLATE.zh-CN.md)
- [EXECUTION_PROMPT.md](./EXECUTION_PROMPT.md)
- [EXECUTION_PROMPT.zh-CN.md](./EXECUTION_PROMPT.zh-CN.md)
- [INPUT_TEMPLATE.md](./INPUT_TEMPLATE.md)
- [INPUT_TEMPLATE.zh-CN.md](./INPUT_TEMPLATE.zh-CN.md)
- [SAMPLE_RECORD.md](./SAMPLE_RECORD.md)
- [SAMPLE_RECORD.zh-CN.md](./SAMPLE_RECORD.zh-CN.md)

## Output Direction

The core output is not generic workout language.
The core output is a constraint-aware training decision.

## Public Sample

- [Sample Record](./SAMPLE_RECORD.md)
- [中文样本](./SAMPLE_RECORD.zh-CN.md)

## Install

```bash
npx skills add https://github.com/sirsws/akm --skill akm-fitness-planner --full-depth
```


path: README.zh-CN.md
content: <!--
文件：README.zh-CN.md
核心功能：作为 Fitness skill 的中文入口页，说明用途、方法分层、双语文件、公开样本与输出方向。
输入：Fitness 分支的挖掘逻辑、记录模板、执行提示词、公开样本与 skill 设计。
输出：供 GitHub 中文页或技能页直接引用的中文 README。
-->

# Fitness Skill

<p align="center">
  <a href="./README.md">English</a> | <a href="./README.zh-CN.md">简体中文</a>
</p>

## 作用

Fitness skill 是 AKM 在训练决策工作流里的操作入口。
它不是单 prompt，而是一套三段式方法。

## 方法分层

1. elicitation
2. structured record
3. execution decision

## 双语文件

- [SKILL.md](./SKILL.md)
- [SKILL.zh-CN.md](./SKILL.zh-CN.md)
- [ELICITATION_PROMPT.md](./ELICITATION_PROMPT.md)
- [ELICITATION_PROMPT.zh-CN.md](./ELICITATION_PROMPT.zh-CN.md)
- [RECORD_TEMPLATE.md](./RECORD_TEMPLATE.md)
- [RECORD_TEMPLATE.zh-CN.md](./RECORD_TEMPLATE.zh-CN.md)
- [EXECUTION_PROMPT.md](./EXECUTION_PROMPT.md)
- [EXECUTION_PROMPT.zh-CN.md](./EXECUTION_PROMPT.zh-CN.md)
- [INPUT_TEMPLATE.md](./INPUT_TEMPLATE.md)
- [INPUT_TEMPLATE.zh-CN.md](./INPUT_TEMPLATE.zh-CN.md)
- [SAMPLE_RECORD.md](./SAMPLE_RECORD.md)
- [SAMPLE_RECORD.zh-CN.md](./SAMPLE_RECORD.zh-CN.md)

## 输出方向

核心输出不是泛泛的训练语言。
核心输出是一个考虑约束的训练决策。

## 公开样本

- [Sample Record](./SAMPLE_RECORD.md)
- [中文样本](./SAMPLE_RECORD.zh-CN.md)

## Install

```bash
npx skills add https://github.com/sirsws/akm --skill akm-fitness-planner --full-depth
```


path: RECORD_TEMPLATE.md
content: <!--
文件：RECORD_TEMPLATE.md
核心功能：作为 Fitness 分支的英文结构化记录模板，用于固化挖掘结果并为执行层提供稳定输入。
输入：前置挖掘得到的目标、限制、器械、时间与恢复信息。
输出：标准化 FitnessProfile 记录模板。
-->

# Fitness Record Template

## FitnessProfile

- `PrimaryGoal`:
- `SecondaryGoals`:
- `BodyConstraints`:
- `EquipmentContext`:
- `TimeBudget`:
- `RecoveryContext`:
- `AdherenceRisks`:
- `StageFocus`:
- `NonNegotiables`:
- `MissingInputs`:


path: RECORD_TEMPLATE.zh-CN.md
content: <!--
文件：RECORD_TEMPLATE.zh-CN.md
核心功能：作为 Fitness 分支的中文结构化记录模板，用于固化挖掘结果并为执行层提供稳定输入。
输入：前置挖掘得到的目标、限制、器械、时间与恢复信息。
输出：标准化 FitnessProfile 记录模板。
-->

# Fitness Record Template

## FitnessProfile

- `PrimaryGoal`：
- `SecondaryGoals`：
- `BodyConstraints`：
- `EquipmentContext`：
- `TimeBudget`：
- `RecoveryContext`：
- `AdherenceRisks`：
- `StageFocus`：
- `NonNegotiables`：
- `MissingInputs`：


path: SAMPLE_RECORD.md
content: <!--
文件：SAMPLE_RECORD.md
核心功能：提供 AKM Fitness 的英文脱敏样本，展示输入快照、结构化记录、决策输出与变化原因。
输入：真实健身使用记录的轻脱敏重写版。
输出：供 GitHub、ClawHub 或审阅者快速理解 Fitness skill 工作流的公开样本。
-->

# Fitness Sample Record

## Input Snapshot

- Primary goals: reduce body fat while preserving upper-body strength and lower-back safety
- Physical limits: no spinal compression tolerance, avoid aggravating lumbar instability
- Equipment reality: commercial gym available, but no guarantee of open squat rack at peak hours
- Time budget: 45 minutes hard cap
- Recovery state: slept poorly, mild fatigue, no acute joint pain
- Recent context: previous session already used heavy pushing volume

## Structured Record

```yaml
Profile:
  GoalPriority:
    1: fat-loss adherence
    2: strength retention
    3: low-risk lower-body stimulus
  BodyConstraints:
    - lumbar instability history
    - avoid high spinal loading
    - avoid fatigue-driven technical breakdown
  EquipmentContext:
    - cable stack
    - dumbbells
    - leg press
    - no rack dependency
  TimeBudget:
    session_cap_minutes: 45
  RecoveryState:
    sleep: below baseline
    systemic_fatigue: mild
    acute_pain: none
  RecentLoad:
    previous_session_bias: upper-body pushing
    lower_body_readiness: usable but not ideal for axial loading
```

## Decision Output

```yaml
StateJudgment: constrained-but-trainable
PrimaryDecision: run a low-spinal-load lower-body and trunk-stability session instead of a heavy squat-centered day
DecisionConfidence: medium
Plan:
  - leg press: moderate load, controlled tempo, 3 working sets
  - dumbbell Romanian deadlift: light technique sets only if lumbar response stays stable
  - split squat or step-up: unilateral volume without axial loading
  - cable anti-rotation core work
  - stop if lumbar stability drops or fatigue rises sharply
RiskNotes:
  - poor sleep lowers tolerance for high-skill heavy loading
  - rack-dependent plans create avoidable execution friction today
NonNegotiables:
  - no barbell back squat
  - no maximal loading
  - no plan extension beyond 45 minutes
MissingInputs: []
```

## Why This Output Changed

The decision did not start from a generic "leg day" template.
It changed because the valid action space was narrowed by four concrete factors:

1. lower-back constraints removed heavy spinal loading
2. poor recovery reduced tolerance for high-skill intensity
3. time cap ruled out complex multi-block programming
4. equipment uncertainty made rack-dependent planning fragile

The result is still a training decision, but it is profile-first rather than template-first.


path: SAMPLE_RECORD.zh-CN.md
content: <!--
文件：SAMPLE_RECORD.zh-CN.md
核心功能：提供 AKM Fitness 的中文脱敏样本，展示输入快照、结构化记录、决策输出与变化原因。
输入：真实健身使用记录的轻脱敏重写版。
输出：供 GitHub 中文页、ClawHub 溯源码或内部审阅者快速理解 Fitness skill 工作流的公开样本。
-->

# Fitness 脱敏样本

## 输入快照

- 主目标：减脂优先，同时保住上肢力量和下背安全
- 身体限制：不接受高脊柱压缩负荷，避免诱发腰椎滑脱相关风险
- 器械现实：商业健身房可用，但高峰时段不能假设深蹲架一定空闲
- 时间预算：本次训练硬上限 45 分钟
- 恢复状态：睡眠差，轻度疲劳，无急性关节疼痛
- 近期背景：上一练已经用了较多上肢推训练量

## 结构化记录

```yaml
Profile:
  GoalPriority:
    1: 减脂依从性
    2: 力量保持
    3: 低风险下肢刺激
  BodyConstraints:
    - 腰椎稳定性历史问题
    - 避免高脊柱负荷
    - 避免疲劳状态下动作崩坏
  EquipmentContext:
    - 绳索器械
    - 哑铃
    - 腿举机
    - 不依赖深蹲架
  TimeBudget:
    session_cap_minutes: 45
  RecoveryState:
    sleep: 低于基线
    systemic_fatigue: 轻度
    acute_pain: 无
  RecentLoad:
    previous_session_bias: 上肢推
    lower_body_readiness: 可训练，但不适合高轴向负荷
```

## 决策输出

```yaml
StateJudgment: constrained-but-trainable
PrimaryDecision: 今天做低脊柱负荷的下肢与核心稳定训练，而不是重蹲主导日
DecisionConfidence: medium
Plan:
  - 腿举：中等负荷，控制节奏，3 组工作组
  - 哑铃罗马尼亚硬拉：仅在腰部反馈稳定时做轻技术组
  - 保加利亚分腿蹲或登阶：用单侧容量替代高轴向负荷
  - 绳索抗旋核心
  - 若腰部稳定性下降或疲劳明显上升，立即停止
RiskNotes:
  - 睡眠差会降低高技能重负荷训练容错
  - 依赖深蹲架的方案今天会制造不必要执行摩擦
NonNegotiables:
  - 不做杠铃背蹲
  - 不做极限负荷
  - 训练不延长到 45 分钟以外
MissingInputs: []
```

## 为什么输出变了

这个决策不是从一个泛化的“腿日模板”开始的。
它之所以改变，是因为有效动作空间被四个具体变量同时收窄：

1. 下背限制排除了高脊柱负荷
2. 恢复偏差降低了高技能高强度动作的容错
3. 时间上限排除了复杂分块计划
4. 器械不确定性让深蹲架依赖方案变得脆弱

最后产出的仍然是训练决策，但它是画像优先，而不是模板优先。


path: SKILL.zh-CN.md
content: ---
name: "akm-fitness-planner"
description: "AKM 在训练决策工作流中的实现，在输出训练决定前先建模目标、身体限制、器械环境、时间预算与恢复状态。"
---

<!--
文件：SKILL.zh-CN.md
核心功能：作为 AKM Fitness skill 的中文正式说明页，定义其定位、输入要求、工作流、输出契约、双语规则与边界。
输入：Fitness 分支方法结构、提示词文件、记录模板与公开 skill 设计。
输出：供 GitHub 中文页、技能市场或代理工具直接引用的中文 skill 文档。
-->

# AKM Fitness Skill

<p align="center">
  <a href="./SKILL.md">English</a> | <a href="./SKILL.zh-CN.md">简体中文</a>
</p>

**没有画像，就没有严肃训练决策。**

AKM Fitness 是一个面向真实约束训练决策的 skill 包。
它处理的是身体限制、器械现实、时间预算、恢复状态和目标层级都会实质影响训练选择的场景。

## 定位

AKM Fitness 不是泛泛的训练问答工具。
它把训练规划重写成“画像优先的决策流程”。

## 必需输入

- `training goals`
- `body limitations or injury constraints`
- `available equipment`
- `weekly frequency`
- `session time budget`
- `recovery context`

缺关键输入时，skill 必须显式输出 `MissingInputs`，而不是伪造确定性。

## 工作流

1. `ELICITATION_PROMPT.md`
2. `RECORD_TEMPLATE.md`
3. `EXECUTION_PROMPT.md`

## 输出契约

输出应包含：

- `StateJudgment`
- `PrimaryDecision`
- `DecisionConfidence`
- `Plan`
- `RiskNotes`
- `NonNegotiables`
- `MissingInputs`

## 双语规则

公开落地页采用英文主页 + 中文切换。
字段 key 保持英文，以保证输出稳定。
实际提示词提供英文版和中文版。

## 边界

- 不是医疗诊断工具
- 不替代康复建议
- 不是健美模板生成器
- 不能用鸡血语言掩盖不确定性


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)