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food-channel食品频道

Agent Skill

food-channel 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。

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下载量

1,006
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:food-channel(食品频道)
来源仓库:https://github.com/cdmichaelb/food-channel
安装命令:
openclaw skills install food-channel
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 food-channel

简介

处理食物摄入事件的确定性路由与消息传递。

  • 专为食品跟踪系统设计,维护数据流一致性。
  • 适用于健康管理与饮食日志应用场景。food-channel 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 需集成现有食品追踪渠道才能正常工作。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 建议检查消息队列配置与错误处理机制。

SKILL.md

name
food-channel
version
1.0.1
description
Handle messages in a food-tracking channel by routing food intake events through a deterministic food tracker. Use when working in a dedicated food channel, especially for barcode lookups, photo estimates, and daily summaries.
env
WORKSPACE
description
OpenClaw workspace root path
required
false
FOOD_LOG
description
Override path for food log CSV
required
false
FOOD_CHANNEL_ID
description
Channel ID for food tracking
required
true
FOOD_PROFILE_PATH
description
Path to user profile JSON with daily limits
required
false

Food Channel Skill

Handle messages in a food-tracking channel by logging food intake events.

Configuration

Set the following environment variables or replace placeholders before use:

  • FOOD_CHANNEL_ID — Channel ID for food tracking (env var)
  • FOOD_PROFILE_PATH — Path to user profile JSON (default: $WORKSPACE/data/food_profile.json)
  • Tracker script is at scripts/tracker.py (included in bundle)

Message Types

1. Barcode Lookup

User sends a barcode (numeric string, 8-14 digits). Optional servings specified as "2x", "2 servings", "x2", etc. Defaults to 1.

Flow:

  1. Parse the barcode and optional servings from the message
  2. Run: python3 scripts/tracker.py lookup <barcode> [servings]
  3. If successful, reply with a formatted summary of the food item and nutrition logged
  4. If failed, reply with the error

2. Photo Estimate

User sends an image attachment.

Flow:

  1. Copy the image to the workspace data dir
  2. Resize it to max 1024px on longest side: convert src.jpg -resize '1024x1024>' -quality 80 dst.jpg (or use python3 -c "from PIL import Image; ..." if imagemagick unavailable)
  3. Use the image tool on the resized image with a prompt asking to estimate: food items, portion sizes, and approximate nutrition per serving
  4. Parse the vision response into structured nutrition data
  5. Run: echo '<json>' | python3 scripts/tracker.py estimate
  6. Reply with formatted summary noting it's an estimate (≈)

3. Summary Request

User asks for today's summary or a daily summary.

Flow:

  1. Run: python3 scripts/tracker.py summary [YYYY-MM-DD]
  2. Reply with formatted daily totals

4. Post-Log Check (after every barcode or estimate log)

After logging an entry, check running daily totals against limits:

  1. Run: python3 scripts/tracker.py summary
  2. Load profile from FOOD_PROFILE_PATH (default: data/food_profile.json)
  3. Compare totals against daily_limits. Flag any that are over (or under for fiber_g_min):

- calories > limit → ⚠️ Over calorie budget ({total}/{limit} kcal) - sodium_mg > limit → ⚠️ High sodium ({total}/{limit} mg) - sugar_g > limit → ⚠️ Over sugar limit ({total}/{limit} g) - fiber_g < limit → ℹ️ Low fiber ({total}/{limit} g)

  1. Append any warnings to the log reply. If nothing is over, skip the check section entirely.

Reply Format

For logged items, use this format:

📝 **Item Name** (Brand)
Source: barcode | Servings: 2
Per serving: 150g
---
🔥 300 kcal | 🥩 12g protein | 🍞 45g carbs | 🧈 8g fat
🥬 3g fiber | 🍬 18g sugar | 🧂 420mg sodium
[Vitamins/minerals if present]

For photo estimates, prefix with: 📸 Estimate —

Parsing Servings

From message text, look for patterns like:

  • 2x, x2, ×2
  • 2 servings, 2 serving
  • two, three (common number words)
  • If none found, default to 1

Data Access

This skill reads and writes persistent files in the workspace:

  • Writes $WORKSPACE/data/food_log.csv — one row per food entry (item, nutrition, timestamps)
  • Writes resized images to $WORKSPACE/data/ during photo estimates (cleaned up after processing)
  • Reads $WORKSPACE/data/food_profile.json (or FOOD_PROFILE_PATH) — daily nutritional limits
  • Network barcode lookups hit https://world.openfoodfacts.org/api/v2/ (public API, no key needed)
  • External API photo estimates use the platform image tool which may send images to a remote vision model

Required Files

  • scripts/tracker.py — the food tracker script (included in bundle)
  • data/food_profile.json — user profile with daily limits (user-created, not included)

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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敏感数据

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安装前确认

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来源信息

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