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live-to-100活到 100 岁

Agent Skill

live-to-100 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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

4,293

周安装

172

GitHub Stars

公开资料未说明

下载量

1,390
OpenClaw

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

MIT-0

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:live-to-100(活到 100 岁)
来源仓库:https://github.com/rq-wu/live-to-100
安装命令:
openclaw skills install live-to-100
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install live-to-100

简介

活到100岁收集健康指标与生活数据,输出个性化长寿计划与营养分析报告。

  • 包含风险评分、周报生成与保健品安全检查功能,支持分阶段复盘。
  • 通过「制定健康计划」或「分析饮食」等请求触发。
  • 涉及医疗建议时请明确标注仅供参考,不可替代专业诊疗。
  • live-to-100 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
live-to-100
description
收集用户身体指标、生活习惯、既往病史和目标,生成可执行的长寿行动计划与分阶段复盘机制,并提供风险评分、自动周报/月报、保健品安全检查和每日饮食营养均衡分析(含热量缺口)。Use when users ask for longevity plans, healthy routine optimization, behavior-change schedules, supplement safety review, daily meal/nutrition analysis, calorie deficit tracking, or recurring reminders for hydration, standing breaks, sleep, and exercise.

Live To 100

Core Rule

Position the output as lifestyle guidance, not diagnosis or emergency care. If the user reports urgent danger signs (e.g., severe chest pain, fainting, stroke-like symptoms, self-harm intent), stop planning and advise immediate emergency care.

Workflow

1) Collect baseline data

Use references/intake-template.md as the intake form. Ask only for missing high-impact fields first:

  • Age, biological sex, height, weight, waist
  • Blood pressure (if known), resting heart rate, sleep duration
  • Activity level (steps, exercise days/week, sedentary hours)
  • Smoking, alcohol, caffeine timing
  • Current diseases, medications, supplement list
  • Main goal and constraints (time, budget, injuries, shift work)

If data is partial, continue with assumptions and clearly label assumptions.

2) Build longevity profile

Produce a concise risk-and-opportunity snapshot:

  • Green: already solid habits to maintain
  • Yellow: moderate gaps to improve in 4-12 weeks
  • Red: possible high-risk items that need clinician follow-up

Prioritize behavior changes by expected impact and feasibility. Do not overload the plan with more than 3 major behavior goals at once.

Then calculate a Longevity Risk Score (0-100) using references/risk-scoring.md:

  • Show total score and sub-scores (body composition, cardiometabolic, sleep/recovery, activity/sedentary, habits, medical context).
  • Explain top 3 contributors and which 2-3 changes can move the score most in 4 weeks.
  • If critical data is missing, output a provisional score and list missing fields.

3) Generate actionable plan

Return a 12-week plan in 3 phases:

  • Phase 1 (Week 1-2): minimum viable routine and reminders
  • Phase 2 (Week 3-6): progressive overload and consistency targets
  • Phase 3 (Week 7-12): stabilization and relapse prevention

Include these dimensions:

  • Hydration
  • Standing/mobility breaks
  • Sleep timing and wind-down
  • Exercise (aerobic + strength + daily movement)
  • Nutrition guardrails
  • Supplements (after safety screening only)

For each action, specify:

  • Trigger (when)
  • Action (what)
  • Minimum bar (minimum version)
  • Upgrade path (next level)

4) Configure reminders

Use references/reminder-presets.md and adapt to user wake/sleep schedule. For complex timetables (multiple windows, weekday/weekend differences, interval reminders, quiet hours), use references/reminder-timetable.md. Always output a reminder table with:

  • Reminder type
  • Time or interval
  • Message
  • Duration
  • Completion rule

Support at least these reminders:

  • Drink water
  • Stand up / move
  • Sleep routine
  • Workout
  • Supplements

If the platform supports recurring automations, generate platform-ready schedules. If not, output copy-paste reminder text for phone calendar or todo apps.

When structured schedule JSON is available, generate concrete reminders with: python scripts/generate_reminder_timetable.py --input schedule.json --output reminders.md

5) Apply supplement safety gate

Use references/supplement-safety.md before confirming any supplement advice:

  • Check contraindications against existing diseases, meds, allergies, pregnancy/breastfeeding status, kidney/liver flags.
  • Check dosage and timing boundaries; avoid adding stacked supplements with overlapping risks.
  • Output status per supplement: Safe to continue, Needs clinician review, or Avoid for now.
  • If conflict exists, prioritize food-first alternatives and medical follow-up over additional supplements.

6) Close the loop with auto reports

Add a lightweight check-in protocol:

  • Daily: adherence score (0-100) + 1 blocker
  • Weekly: trend on sleep, movement, training sessions, waist/weight
  • Every 4 weeks: adjust targets based on adherence and recovery

When adherence is low, reduce plan complexity before increasing intensity.

Generate reports using references/report-templates.md:

  • Weekly report: adherence, metric deltas, blockers, and next-week focus.
  • Monthly report: score trend, behavior consistency, supplement safety events, and plan adjustments.
  • Keep each report short and action-oriented.

7) Analyze daily meals and calorie deficit

Use references/daily-nutrition-log.md for daily food logging input. Evaluate these outputs every day:

  • Total calories and estimated calorie deficit/surplus vs target
  • Macro totals (protein/carbs/fat) and ratio balance
  • Fiber and hydration adequacy
  • Food diversity and ultra-processed food proportion (if available)

Return:

  • Nutrition Balance Score (0-100)
  • Calorie Deficit Status (on target / too aggressive / insufficient)
  • 2-3 concrete meal adjustments for next day

When structured daily log JSON is available, generate analysis with: python scripts/analyze_daily_nutrition.py --input nutrition_day.json --output nutrition_report.md

Output Format

Use this order:

  1. Health Snapshot (Green/Yellow/Red)
  2. Longevity Risk Score (total + sub-scores + key drivers)
  3. 12-Week Longevity Plan
  4. Reminder Schedule
  5. Supplement Safety Check
  6. Daily Nutrition Balance and Calorie Deficit
  7. Check-in and Auto Report Rules
  8. Medical Follow-up Flags (if applicable)

Keep recommendations specific, measurable, and time-bound. Avoid abstract advice without concrete behaviors.

Resources

  • Intake template: references/intake-template.md
  • Daily nutrition intake template: references/daily-nutrition-log.md
  • Reminder defaults: references/reminder-presets.md
  • Complex timetable schema: references/reminder-timetable.md
  • Risk model: references/risk-scoring.md
  • Supplement safety: references/supplement-safety.md
  • Weekly/monthly report templates: references/report-templates.md
  • Report generator script: scripts/generate_health_reports.py
  • Reminder timetable generator script: scripts/generate_reminder_timetable.py
  • Daily nutrition analyzer script: scripts/analyze_daily_nutrition.py

Use the script when structured JSON data is available: python scripts/generate_health_reports.py --input user_data.json --output report.md

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

74.04%
按下载量换算1,029

安全审计

VirusTotal

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ClawScan

通过

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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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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