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Agent Skill · 开发

habit-ai(习惯艾)

habit-ai 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

habit-ai 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 2,860。

总安装 14,059周安装 580GitHub Stars 公开资料未说明下载 4,594核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.2.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:habit-ai(习惯艾)
来源仓库:https://github.com/habitclaw/habit-ai
安装命令:
openclaw skills install habit-ai
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install habit-ai

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

habit-ai 通过免费 API 帮助用户跟踪营养、膳食、饮水、体重、步数、冥想和日记等日常习惯。适用于需要记录健康数据、分析生活习惯或制定改善计划的用户场景。调用方式简单,直接通过自然语言指令触发,无需复杂配置。使用前请确认权限范围,注意是否涉及联网或数据上传,并了解服务稳定性与隐私政策。

habit-ai 属于开发类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install habit-ai;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

habit-ai 怎么安装?

在终端执行 openclaw skills install habit-ai。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

habit-ai 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

habit-ai 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

habit-ai 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

habit-ai 用的人多吗?

公开数据是累计安装 14,059 次,近一周安装 580 次,下载 4,594 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

habit-ai 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
habit-ai

习惯艾 · 本页

14,059—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
habit-ai
description
Track nutrition, meals, water, weight, steps, meditation, and journal entries via the Habit AI API — a completely free service. Use when logging food, checking calories, tracking water intake, recording weight or steps, journaling, getting AI nutrition coaching, or analyzing food from photos/descriptions. Habit AI is free to use — just create an account at habitapp.ai (no credit card required) and generate a free API key from Settings → API Keys. Also available as a free iOS app.

Habit AI

Track health and nutrition through the Habit AI REST API.

Setup (100% Free)

Habit AI is a completely free service — no subscription, no credit card, no usage limits.

  1. Create a free account at https://habitapp.ai (or download the free iOS app)
  2. Go to Settings → API Keys → Create Key (free, up to 5 keys)
  3. Store key in environment: export HABITAI_API_KEY="hab_..."

All requests use:

  • Base URL: https://habitapp.ai/api/v1
  • Auth header: Authorization: Bearer $HABITAI_API_KEY
  • Content-Type: application/json

Quick Reference

ActionMethodEndpoint
Log a mealPOST/meals
Today's mealsGET/meals?date=YYYY-MM-DD
Daily nutritionGET/nutrition/daily?date=YYYY-MM-DD
Weekly nutritionGET/nutrition/weekly?date=YYYY-MM-DD
Log water (ml)POST/water
Log weight (kg)POST/weight
Log stepsPOST/steps
Log meditationPOST/meditation
Journal entryPOST/journal
AI eating coachPOST/coaches/eating
AI mindfulness coachPOST/coaches/mindfulness
AI meditation coachPOST/coaches/meditation
Get profileGET/profile
Update profilePUT/profile

For full endpoint details (request/response schemas, all parameters), see references/api.md.

Logging Meals — The Right Way

⚠️ CRITICAL: Use the AI model to analyze food, then POST /meals with the EXACT structure below

Do NOT call /analyze/food-image or /analyze/meal-description — instead, use your own vision/language capabilities to analyze the food, then construct the exact JSON structure below and POST it to /meals.

Step 0: Check user profile for allergens/diet

Before analyzing, call GET /profile to check foodSensitivities and diet fields. Factor these into:

  • healthScore — lower the score if the meal contains ingredients the user is sensitive to
  • healthScoreExplanation — mention the general nutritional pros/cons
  • healthSensitivityExplanation — if the meal contains any of the user's allergens/sensitivities, explain which ingredients are problematic and why. Leave empty string if no sensitivities match.

Step 1: Analyze the food yourself

For photos: Look at the image and identify each ingredient, estimate portions, and calculate nutrition using USDA data.

For descriptions: Parse the meal description and calculate nutrition the same way.

Step 2: POST /meals with the EXACT structure

Every field matters. iOS reads from nutritionalSummary (nested object) — if it's missing, meals show as 0 calories.

{
  "mealName": "Grilled Chicken Salad with Ranch",
  "calories": 520,
  "protein": 42,
  "carbs": 18,
  "fat": 32,
  "fiber": 4,
  "sodium": 890,
  "sugar": 6,
  "healthScore": 7,
  "healthScoreExplanation": "Lean protein from grilled chicken and fiber from greens, but ranch dressing adds significant fat and sodium.",
  "mealType": "lunch",
  "analysisConfidenceLevel": 8,
  "ingredients": [
    {
      "name": "grilled chicken breast",
      "calories": 280,
      "protein": 35,
      "carbs": 0,
      "fat": 14,
      "sugar": 0,
      "fiber": 0,
      "sodium": 400,
      "healthScore": 8,
      "measurementType": "grams",
      "measurementValue": 200
    },
    {
      "name": "mixed salad greens",
      "calories": 20,
      "protein": 2,
      "carbs": 4,
      "fat": 0,
      "sugar": 1,
      "fiber": 2,
      "sodium": 30,
      "healthScore": 9,
      "measurementType": "cups",
      "measurementValue": 2
    },
    {
      "name": "ranch dressing",
      "calories": 220,
      "protein": 5,
      "carbs": 14,
      "fat": 18,
      "sugar": 5,
      "fiber": 2,
      "sodium": 460,
      "healthScore": 3,
      "measurementType": "spoons",
      "measurementValue": 3
    }
  ]
}

Field Reference

FieldTypeRequiredDescription
mealNamestringYesDisplay name (e.g. "Chicken Caesar Salad"). Without this, the meal has no name in the app.
caloriesnumberYesTotal calories (kcal). Must be > 0.
proteinnumberYesTotal protein in grams
carbsnumberYesTotal carbohydrates in grams
fatnumberYesTotal fat in grams
fibernumberYesTotal fiber in grams
sodiumnumberYesTotal sodium in milligrams
sugarnumberYesTotal sugar in grams
healthScoreintegerYes1-10. How healthy is this meal overall? (1=very unhealthy, 10=very healthy)
mealTypestringYesOne of: breakfast, lunch, dinner, snack
analysisConfidenceLevelintegerYes1-10. How confident are you in the nutrition estimates? (1=wild guess, 10=exact data from packaging). For photo analysis use 6-8, for descriptions use 5-7.
healthScoreExplanationstringYes1-2 sentence explanation of the nutritional pros/cons (e.g. "Good protein from chicken but high sodium from the sausage and dressing.")
healthSensitivityExplanationstringYesIf the meal contains any of the user's allergens/food sensitivities (from profile), explain which ingredients are problematic. Empty string "" if no sensitivities match or user has none set.
ingredientsarrayYesArray of ingredient objects (see below)
imageUrlstringNoURL of the food photo. Get this from POST /meals/upload-image first (see below).
dateScannedstringNoISO 8601 timestamp. Defaults to now if omitted.
servingnumberNoServing multiplier (defaults to 1.0)

Ingredient Object

Each ingredient in the ingredients array must have:

FieldTypeDescription
namestringIngredient name (e.g. "grilled chicken breast")
caloriesnumberCalories for this ingredient's portion (kcal)
proteinnumberProtein in grams
carbsnumberCarbs in grams
fatnumberFat in grams
sugarnumberSugar in grams
fibernumberFiber in grams
sodiumnumberSodium in milligrams
healthScoreinteger1-10 health score for this specific ingredient
measurementTypestringMust be one of: grams, ounces, cups, spoons, servings. Use servings for pieces/slices/bowls/items. Use spoons for tablespoons/teaspoons.
measurementValuenumberAmount in the specified unit

Important Rules

  1. All nutrition values must be numbers, not strings. "calories": 520 not "calories": "520"
  2. Ingredient calories should sum to the total calories (approximately — within 5%)
  3. mealName is mandatory — without it, the meal is invisible on iOS
  4. healthScore is 1-10 integer — use your judgment (fast food = 2-4, home-cooked balanced = 6-8, raw salad = 9-10)
  5. analysisConfidenceLevel is 1-10 integer — be honest about uncertainty
  6. Sodium is in milligrams, everything else is in grams (except calories in kcal)

Uploading a meal photo (thumbnail)

If you have a food photo, upload it first to get a URL:

curl -X POST https://habitapp.ai/api/v1/meals/upload-image \
  -H "Authorization: Bearer $HABITAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"imageBase64": "<base64-encoded-image>"}'

Returns: {"success": true, "imageUrl": "https://firebasestorage.googleapis.com/..."}

Then pass imageUrl in your POST /meals call. You can also attach to an existing meal:

{"imageBase64": "<base64>", "mealId": "<existing-meal-id>"}

Full flow with photo:

  1. POST /meals/upload-image with base64 photo → get imageUrl
  2. POST /meals with nutrition data + imageUrl

Other Workflows

Check remaining calories

  1. GET /nutrition/daily for today's totals
  2. GET /profile for calorie goal
  3. Subtract: caloriesGoal - totalCalories

Quick water log

curl -X POST https://habitapp.ai/api/v1/water \
  -H "Authorization: Bearer $HABITAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"amount": 500}'

Amount is in milliliters. 1 cup ≈ 237ml, 1 glass ≈ 250ml.

Notes

  • Dates default to today if omitted (uses user's timezone from profile)
  • Water amount is in milliliters
  • Weight is in kilograms (1 lb ≈ 0.4536 kg)
  • Steps auto-calculate calories burned if profile has height/weight/gender
  • Max 5 API keys per account

平台分布

OpenClaw

82.42%
按下载量换算3,786

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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