weight-gain-strategy

name	weight-gain-strategy
version	1.0.0
description	Deep intervention for sustained upward weight trends. Trigger: consecutive increases flagged by post-weigh-in checks, or explicit questions ('体重怎么涨了', 'why am I gaining weight'). Graduated support: reassurance → cause analysis → temporary adjustments. Recording weigh-ins and routine trend judgment → weight-tracking. NOT when emotional distress needs priority → emotional-support; NOT when weight-focus should be avoided (history_of_ed / avoid_weight_focus flags).
metadata	{"openclaw":{"emoji":"mag","homepage":"https://github.com/NanoRhino/weight-loss-skill"}}

Weight Gain Strategy

Detect upward weight trends and respond with graduated support — from reassurance on the first increase, to guided cause discovery, to full diagnosis with adjustment strategies — matching the response depth to how persistent the trend is.

Routing Gate

Entry paths:

• Auto (post-weigh-in): weight-tracking 记完体重后自主判断是否需要干预 → 需要时读 references/cause-check-flow.md 进入诊断流程。
• Manual: User asks about weight gain ("why am I gaining weight", "体重怎么涨了") → check Skip conditions first → run analyze directly → Interactive Flow Step 1.

Skip — do NOT enter this skill if:

• No PLAN.md exists (no plan to deviate from)
• USER.md > Health Flags contains avoid_weight_focus or history_of_ed
• User shows emotional distress about weight → defer to emotional-support (P1 priority)

Principles

1. Normalize first. Lead with reassurance, then dig into data.
2. Data + habits before opinions. Every diagnosis must cite actual numbers or observable behavioral patterns. Never speculate without evidence.
3. Escalate gradually. Response depth follows the streak. Never skip levels or jump to strategy on a first increase.
4. Collaborate, don't force. The user can opt in or out at every step. Playful challenges are fine; pushing past a "no" is not.
5. Keep it light. Witty friend, not stern doctor. Data rigorous, delivery fun.

---

Diagnosis Dimensions

The analyze command outputs raw statistics only — no detected: true/false judgments. The AI interprets these numbers in context (user history, lifestyle, chat context) to determine causes.

Output fields

Field	What it contains	AI uses it for
calorie_stats	avg/min/max/std_dev, days over target, days under 60%, daily breakdown	Surplus, volatility, binge/restrict patterns
protein_stats	avg daily g, recommended g (weight×1.2), days below 70%	Protein deficit detection
exercise_stats	This week vs last week sessions & minutes	Exercise decline
logging_stats	Coverage %, single-meal days, unlogged days	Data reliability
weight_pattern	Largest daily jump + dates	Sudden spike (water retention)
food_list	Raw food names (dedupe, up to 50)	Food quality, variety, processed patterns
data_confidence	sufficient flag, issues list	Whether to analyze or ask for more data first
active_strategy	Current strategy type/dates if active	Whether to suppress new interventions
suggested_actions	Concrete script-driven actions (not AI judgment)	Strict mode, set calorie target, suppress strategy

⚠️ suggested_actions are deterministic rules, not AI opinions:

• strict_mode: coverage < 50% or >50% single-meal days → enter strict mode (see references/strict-mode.md). Do NOT create new meal reminder crons — they already exist. Strict mode makes existing reminders more insistent.
• set_calorie_target: no calorie target set → cannot do surplus analysis
• suppress_new_strategy: active strategy hasn't expired → don't start a new cause-check

🎯 AI creates targeted habits based on analysis — NOT generic meal reminders: After analyzing the raw data, the AI identifies the specific problem and creates a habit that addresses it. Examples:

• Protein low → habit: "每餐加一份蛋白质（鸡蛋/鸡胸/豆腐）"
• Calorie volatility (binge/restrict) → habit: "每天吃到{目标}附近，不跳餐"
• Late-night eating pattern → habit: "8点前吃完晚饭"
• Weekend overeating → habit: "周末拍照打卡，不多不少"
• Food quality issues → habit: specific swap based on actual foods (e.g. "方便面换成挂面煮蛋")
• Snacking excess → habit: specific swap (e.g. "下午零食换成酸奶/坚果")

NEVER create habits for: meal logging reminders, weight check-ins, or anything that already has a cron job.

⚠️ AI-driven analysis: The script provides numbers; the AI decides what they mean. A std_dev of 967 kcal might be binge/restrict — or a user transitioning diets. The AI considers context.

---

Analysis Script

Script path: python3 {baseDir}/scripts/analyze-weight-trend.py

Commands: analyze, save-strategy, check-strategy. See references/script-api.md for full usage, parameters, and return schemas.

---

Safety Rules

• Calorie floor: Never suggest intake below max(BMR, 1000 kcal/day).
• Exercise safety: For sedentary users or those with health conditions, start with walking only.
• No shame, no blame. Frame adjustments as experiments, not corrections.

---

References

File	Contents
references/cause-check-flow.md	Full cause-check flow (Steps A→D), habit creation, cron rules
references/script-api.md	Script commands, parameters, return schemas
references/strict-mode.md	Strict mode: trigger, behavior rules, duration, failure escalation
references/data-schemas.md	Data sources, strategy JSON schema, skill integration, edge cases