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apple-health-analystApple health analyst 搜索

Agent Skill

apple-health-analyst 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

745

周安装

32

GitHub Stars

46

下载量

261
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:apple-health-analyst(Apple health analyst 搜索)
来源仓库:https://github.com/ruochenlyu/apple-health-analyst
仓库路径:skills/apple-health-analyst
安装命令:
npx skills add https://github.com/ruochenlyu/apple-health-analyst --skill apple-health-analyst
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/ruochenlyu/apple-health-analyst --skill apple-health-analyst

简介

用于查找、检索和筛选 Apple Health 数据分析相关信息。

  • 适用于 Codex、Claude、Cursor、Gemini CLI,支持基于关键词的任务场景匹配。
  • 通过 npx skills add 命令安装,需结合来源仓库 README 确认具体使用方法。
  • 安装前建议核实是否会触发联网或数据处理操作,避免隐私泄露风险。
  • apple-health-analyst 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Apple Health Advisor

Use this skill when a user wants to analyze an Apple Health export ZIP. The ZIP is too large to fit directly into context, so the skill uses a local CLI pipeline to parse and structure the data first.

This is one skill that ships two complementary reports:

  • Default (recommended): generate BOTH reports — health report + training report, rendered into the same output/ folder and cross-linked via an in-page link in the topbar. prepare runs once, then render runs twice.
  • Explicit health-only: user says "只要健康报告" / "health report only" / similar → skip training render.
  • Explicit training-only: user says "只要运动报告" / "training report only" / names a sport exclusively → skip health render.

Language Detection

Detect the user's language from their message:

  • If the user writes in Chinese, use --lang zh
  • For all other languages, use --lang en

The narrative language must match the language declared in insights.json:

  • Health report: narrativeContext.language
  • Training report: training.narrativeContext.language

Intent Routing

Default: generate both health + training reports. Only drop one when the user is explicit.

  • Default (both) examples:

- Analyze my Apple Health export - 帮我分析 Apple Health 导出 - Generate a report from my Apple Health export

  • Health-only examples (skip training render):

- Only generate the health report - 只要健康报告 - 不用生成运动报告

  • Training-only examples (skip health render):

- Only generate the training report - 只要运动报告 - 重点分析拳击训练状态 (user is only asking about a specific sport) - 分析跑步和骑行训练趋势

Ambiguous-but-lean-training keywords still default to both reports (the training report alone is rarely enough context). The keywords below only matter as hints — they do NOT suppress the health report unless the user also says "只" / "only":

  • training, workout, 运动, 训练, 专项
  • named sports such as boxing, running, cycling, walking, hiking, strength training, 拳击, 跑步, 骑行, 力量训练

Your Role

Two roles share the same pipeline:

  1. Health management advisor:

- Integrate sleep, recovery, activity, and body metrics into an overall health view - Prioritize cross-metric reasoning over metric-by-metric reporting

  1. Training status advisor:

- Judge load, recovery support, consistency, and sport-specific trends - Give actionable training-management advice without pretending to be Garmin or a coach writing a periodized plan

Workflow

  1. Confirm the input is an official Apple Health export ZIP and that the main XML has HealthData as its root node.
  2. Run local prepare once with the correct --lang, producing summary.json and insights.json.
  3. Read summary.json, then insights.json.
  4. Decide which reports to produce (default = both unless the user is explicit; see Intent Routing).
  5. For each selected report, write the narrative JSON:

- health: report.llm.json - training: training.report.llm.json

  1. Run render for each selected report:

- health: default render - training: render --type training

  1. Both HTML reports share the same output/ folder. The topbar carries a cross-link between them, so the user can jump back and forth. File names are fixed (report.htmltraining.report.html) — do not rename.

insights.json Keys You Must Use

Shared

KeyWhat it contains
metadatatool, version, language, schemaVersion, generatedAt
historicalContextRecent 30d, baseline 90d, trailing 180d, all-time context
charts[]Health chart groups
crossMetricCross-metric health reasoning
riskFlags[]Health risks with evidence
notableChanges[]Significant changes
dataGaps[]Missing or sparse data warnings
sourceConfidence[]Device/source reliability signals

Health report

KeyWhat it contains
analysis.sleepSleep duration, stages, timing, regularity
analysis.recoveryRHR, HRV, blood oxygen, respiratory rate, VO2 max
analysis.activityActive energy, exercise minutes, stand hours, workouts
analysis.bodyCompositionWeight, body fat %
analysis.menstrualCycleCycle analysis if present
narrativeContextHealth-report audience, goal, schema version, boundaries

Training report

KeyWhat it contains
training.summaryTraining state, readiness, recent load, recovery support, primary sport
training.summary.trainingLoadCTL / ATL / TSB snapshot + 30-day & 90-day CTL deltas (null when < 28 days of data or < 6 workouts)
training.sports[]Top sports (dormant ones filtered, topSportCount configurable via --top-sports, default 5) with recent/baseline/trailing/all-time windows, recovery-after-workout, consistency, tags
training.charts[]training_load (CTL/ATL monthly curve), training_recovery, and sport_<slug>_trend charts
training.narrativeContextTraining-report audience, goal, schema version, boundaries

Commands

Default flow — prepare once, render twice so the folder contains both report sets. Pass --with-cross-link to both render calls so the topbar/footer cross-link lights up; omit it on single-report runs to avoid a dead link to a file that will not exist.

# 1. Prepare once (shared by both reports)
#    Optional: --top-sports N to cap the training-report sport list (default 5)
npx apple-health-analyst prepare /path/to/export.zip --lang en --out ./output

# 2. Health render (fixed file name: report.html)
npx apple-health-analyst render \
  --insights ./output/insights.json \
  --narrative ./output/report.llm.json \
  --with-cross-link \
  --out ./output

# 3. Training render (fixed file name: training.report.html)
npx apple-health-analyst render \
  --type training \
  --insights ./output/insights.json \
  --narrative ./output/training.report.llm.json \
  --with-cross-link \
  --out ./output

The two HTML files auto-link to each other via the topbar and footer only when --with-cross-link is set on both renders. Always write both into the same --out directory to keep the cross-links working.

Single-report mode: if the user is explicit about only wanting the health or the training report (see Intent Routing), run render once without --with-cross-link — otherwise the lone HTML will point at a companion file that never gets generated.

Health Narrative Framework

Use the existing health schema in references/report-llm-json.md.

Prioritize:

  1. crossMetric.compositeAssessment
  2. crossMetric.sleepRecoveryLink
  3. crossMetric.sleepConsistency
  4. crossMetric.activityRecoveryBalance
  5. crossMetric.recoveryCoherence
  6. crossMetric.patterns
  7. riskFlags and notableChanges

Health writing rules:

  • Every conclusion must cite concrete values or dates from summary.json or insights.json
  • key_findings must be cross-metric, not single-metric trivia
  • actions_next_2_weeks must specify time, frequency, or numeric targets
  • questions_for_doctor must be data-driven and specific

Training Narrative Framework

Use the training schema in references/training-report-llm-json.md.

Prioritize:

  1. training.summary.trainingState and training.summary.readiness
  2. training.summary.trainingLoad — the CTL / ATL / TSB snapshot (primary load signal)
  3. training.summary.loadTrend and training.summary.recoverySupport (legacy 30d-vs-90d views, use as corroboration)
  4. training.sports[] in descending importance
  5. training.charts[]
  6. dataGaps[] and missing metric coverage

Training writing rules:

  • Use neutral wording inspired by public training-status concepts (CTL / ATL / TSB), not branded Garmin claims
  • When training.summary.trainingLoad is non-null, lead with CTL direction + TSB value; cite ctlDelta30dPct and ctlDelta90dPct rather than the legacy 30-day-vs-90-day numbers
  • If trainingLoad is null, fall back to loadTrend and say so explicitly (e.g. "数据覆盖不足 28 天,暂以 30 天对比为准")
  • Sport sections must focus on the actual top sports in training.sports[]
  • Only discuss heart rate or distance when the structured data includes those metrics
  • Recommendations are for training management and health monitoring, not race plans or diagnosis

Required Reading Before Writing Narrative

Constraints

  • Only reference facts from summary.json and insights.json
  • Do not fabricate sport metrics, chart IDs, or medical risks
  • Provide health management and training adjustment advice, not diagnoses or treatment plans
  • If a module is insufficient_data, say so plainly
  • Do not generate final HTML directly; write the narrative JSON first, then run render

Error Handling

  • ZIP format error: if prepare cannot find the HealthData XML, verify the user provided the official Apple Health export ZIP. The main XML filename is not fixed and may be localized (for example 导出.xml) or appear as mojibake. export_cda.xml / ClinicalDocument is auxiliary only and should not be used as the main analysis input.
  • Out of memory: large ZIPs may need --from and --to
  • Health narrative validation failure: verify report.llm.json matches schema v2
  • Training narrative validation failure: verify training.report.llm.json matches schema v1 and only references existing sport/chart IDs
  • npm cache EPERM: use npm_config_cache=./.npm-cache
  • Sandbox/policy rejection: do not chain destructive commands with prepare / render; create directories separately if needed

Output Files

Always produced by prepare:

  • summary.json
  • insights.json

Health render (file names are fixed; do not rename):

  • report.llm.json
  • report.md
  • report.html

Training render (file names are fixed; do not rename):

  • training.report.llm.json
  • training.report.md
  • training.report.html

The two HTML reports cross-link via the topbar and footer using relative paths (./report.html./training.report.html). Keep both in the same output/ directory for the links to work.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.83%
按下载量换算88

Claude

30.76%
按下载量换算80

Cursor

19.06%
按下载量换算50

Gemini CLI

9.68%
按下载量换算25

安全审计

Gen Agent Trust Hub

可疑

Socket

可疑

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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