Reward Engine

Reward Engine — Calorie Target Badge

> ⚠️ SILENT OPERATION: Never narrate internal actions or tool calls.

Philosophy

1. Celebrate accumulation, never punish gaps. Count goes up only — never down.
2. Reward the behavior (eating on target), not the outcome (weight loss).
3. One celebration per level-up, then move on. Don't revisit past badges.
4. Personalize the image. User name + their data on the badge.

Qualification Rules

A day counts as "qualified" when ALL THREE conditions are met:

1. Complete meals logged: Number of main meals (breakfast/lunch/dinner) logged today ≥ expected meal count

• Default: 3 meals
• Intermittent fasting (16:8 or explicit "两餐" in PLAN.md): 2 meals
• snack does NOT count as a main meal

1. Calories in target range: Total daily calories within the Daily Calorie Range from PLAN.md (e.g., 1,430 - 1,630)

• Falls back to target ± 10% if only a single target value exists

1. Above safety floor: Total daily calories ≥ BMR × 0.8

• Prevents rewarding dangerous under-eating

Script

python3 {baseDir}/scripts/badge-calc.py check \
  --workspace-dir {workspaceDir} \
  --tz-offset {tz_offset}

Output

{
  "qualified_today": true,
  "already_counted": false,
  "current_count": 14,
  "current_level": 3,
  "level_up": true,
  "new_badge": {
    "level": 3,
    "name": "⭐⭐⭐ 自由支配者",
    "message": "两周的精准分配。你不是在\"控制饮食\"，你是真的会吃了。",
    "milk_tea_cups": 28,
    "progress_bar": "━━━━━━━━━━░░░░ 14/21 → 下一级：稳定输出选手"
  },
  "badge_image": "{workspaceDir}/data/badges/badge_level_3.png"
}

• qualified_today: whether today meets all 3 conditions
• already_counted: true if today was already counted (prevents double-counting)
• level_up: true only when a NEW level is reached this check
• new_badge: null if no level-up
• badge_image: path to generated badge image (null if no level-up)

Level Definitions

| Level | Days | Name | Message | |-------|------|------|---------| | 1 | 3 | ⭐ 稳稳吃饭的人 | 有3天热量刚好落在目标区间。这不是运气——是你对"一顿饭多少热量"有感觉了。 | | 2 | 7 | ⭐⭐ 热量掌控达人 | 7天达标。你已经知道什么时候该多吃、什么时候该收着。这种判断力比任何食谱都管用。 | | 3 | 14 | ⭐⭐⭐ 自由支配者 | 两周的精准分配。你不是在"控制饮食"，你是真的会吃了。 | | 4 | 21 | ⭐⭐⭐⭐ 稳定输出选手 | 三周的节奏，已经不是"在坚持"了，是你的正常状态。 | | 5 | 30 | ⭐⭐⭐⭐⭐ 精准生活家 | 一个月。你对热量的直觉已经内化了——不看数字也不会差太远。 | | 6 | 45 | ⭐×6 持续进化者 | 45天。一个半月的稳定输出，这不是阶段性的表现，是你的新常态。 | | 7 | 60 | ⭐×7 长期主义玩家 | 60天。不需要鸡汤，数据说明一切。 | | 8 | 90 | ⭐×8 不可撼动 | 90天。多数人的计划活不过两周，你走完了全程。这不是关于减肥了，这是你证明了自己能长期做好一件事。 |

Badge Image Generation

When level_up == true, generate a personalized badge image:

• Template: from {baseDir}/assets/ (operator provides base template)
• Dynamic text overlay:
• User's name (from USER.md)
• Badge level + name
• Cumulative days count
• Milk tea cups equivalent
• Progress bar to next level
• Output: saved to {workspaceDir}/data/badges/badge_level_{N}.png
• Format: PNG, dimensions TBD (pending operator's style reference)

> ⚠️ Image template assets pending — operator will provide reference design. > Until then, badge_image will be null and celebration is text-only.

Data Storage

File: {workspaceDir}/data/badges.json

{
  "calorie_target": {
    "current_level": 3,
    "current_count": 14,
    "next_level_target": 21,
    "qualified_dates": ["2026-05-01", "2026-05-03", "2026-05-04"],
    "unlocked_at": {
      "1": "2026-04-15",
      "2": "2026-04-28",
      "3": "2026-05-10"
    },
    "daily_deficit": 385,
    "last_calculated": "2026-05-19"
  }
}

Integration Point

Called by diet-tracking-analysis after meal_checkin returns with action: "create" or action: "append":

# After composing the normal meal reply (①②③):
1. Run badge-calc.py check
2. If level_up == true AND badge_image exists:
   → Append badge celebration text after normal reply
   → Send badge image via MEDIA: directive
3. If level_up == true AND badge_image is null/empty:
   → Say nothing (silent skip — do NOT fall back to text)
4. If qualified_today == true but no level_up:
   → Say nothing (silent accumulation)
5. If qualified_today == false:
   → Say nothing about badges

Source & license

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

• Author: NanoRhino
• Source: NanoRhino/weight-loss-skill
• License: MIT
• Homepage: https://nanorhino.com/

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