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Live To 100

作者 RQ-Wu · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install live-to-100
功能描述
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, be...
使用说明 (SKILL.md)

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

安全使用建议
This skill appears to do what it says: it builds longevity plans, scores risk, produces reminder schedules, and runs local Python scripts to analyze nutrition and generate reports. Before installing or running: (1) Review the three scripts (analyze_daily_nutrition.py, generate_health_reports.py, generate_reminder_timetable.py) to confirm they will only process local JSON files you provide — they do not contain network calls. (2) Be aware you will be entering sensitive health data (vitals, meds, pregnancy status, labs). Only supply such data if you are comfortable with local processing and the platform's privacy policy. (3) The skill is advisory only and explicitly warns to seek emergency/clinical care when appropriate; do not treat outputs as medical diagnoses. (4) If you will run the scripts on a shared machine, consider isolating them (container/VM) and confirming no extra dependencies are installed. If you want higher assurance, ask the author for a provenance statement or run the scripts in a sandbox and inspect their I/O behavior.
能力评估
Purpose & Capability
Name and description match the included files: risk scoring, reminder timetable, nutrition analysis, and report generation are implemented by the provided reference docs and three Python scripts. No extraneous credentials, binaries, or unrelated capabilities are requested.
Instruction Scope
SKILL.md stays within the coaching scope: it collects health inputs, applies the risk model, builds plans, and uses local scripts when structured JSON is available. It explicitly avoids diagnosis and instructs to escalate emergencies. Note: the skill processes sensitive personal health data (vitals, meds, pregnancy/breastfeeding, labs); users should understand this privacy implication before supplying data.
Install Mechanism
No install spec — the skill is instruction-first and includes small, readable Python scripts that run locally. There are no network downloads, package install steps, or external binaries referenced in the manifest.
Credentials
The skill requests no environment variables, credentials, or config paths. The inputs it needs are personal health data provided by the user (via forms or JSON), which is appropriate for its function but sensitive in nature.
Persistence & Privilege
always is false, no special privileges are requested, and the skill does not modify other skills or system-wide agent settings. It runs as-needed and can be invoked by the user or agent normally.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install live-to-100
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /live-to-100 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release
元数据
Slug live-to-100
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Live To 100 是什么?

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, be... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 131 次。

如何安装 Live To 100?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install live-to-100」即可一键安装,无需额外配置。

Live To 100 是免费的吗?

是的,Live To 100 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Live To 100 支持哪些平台?

Live To 100 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Live To 100?

由 RQ-Wu(@rq-wu)开发并维护,当前版本 v1.0.0。

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