# Weight Gain Strategy

> Detect and respond to upward weight trends after weigh-ins or when the user asks why their weight is increasing. Use for: (1) consecutive weight increases detected by post-weigh-in deviation checks, (2) explicit weight-gain questions like 'why am I gaining weight' or '体重怎么涨了'. Provides graduated support from reassurance to cause analysis to temporary adjustment strategies. This skill handles only…

- **Type:** Skill
- **Install:** `agentstack add skill-nanorhino-weight-loss-skill-weight-gain-strategy`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [NanoRhino](https://agentstack.voostack.com/s/nanorhino)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [NanoRhino](https://github.com/NanoRhino)
- **Source:** https://github.com/NanoRhino/weight-loss-skill/tree/main/weight-gain-strategy
- **Website:** https://nanorhino.com/

## Install

```sh
agentstack add skill-nanorhino-weight-loss-skill-weight-gain-strategy
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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% 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.
> - `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-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 |

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [NanoRhino](https://github.com/NanoRhino)
- **Source:** [NanoRhino/weight-loss-skill](https://github.com/NanoRhino/weight-loss-skill)
- **License:** MIT
- **Homepage:** https://nanorhino.com/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-nanorhino-weight-loss-skill-weight-gain-strategy
- Seller: https://agentstack.voostack.com/s/nanorhino
- Browse the marketplace: https://agentstack.voostack.com/browse

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