Live To 100

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.

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