Borrowing it
Nothing to install: this file belongs to huifer/WellAlly-health. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/huifer/WellAlly-health/main/.claude/skills/food-database-query/SKILL.mdgit clone --depth 1 https://github.com/huifer/WellAlly-healthWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/huifer/wellally-health/food-database-query)<a href="https://agentmods.dev/skills/huifer/wellally-health/food-database-query"><img src="https://agentmods.dev/badge/skills/huifer/wellally-health/food-database-query/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/huifer/wellally-health/food-database-query"><img src="https://agentmods.dev/badge/skills/huifer/wellally-health/food-database-query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Agent Snooping · line 769 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.05655 |
| Opus 5 | $0.00000 | $0.02828 |
| Sonnet 5 | $0.00000 | $0.01131 |
| Haiku 4.5 | $0.00000 | $0.00566 |
Grade A, and why
food-database-query scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 781 lines — stays where its author put it; the contents beside it link to each section on GitHub.
食物数据库查询技能
技能名称: Food Database Query 技能类型: 数据查询与分析 创建日期: 2026-01-06 版本: v1.0
技能概述
本技能提供全面的营养食物数据库查询功能,支持食物营养信息查询、比较、推荐和自动营养计算。
核心功能:
- ✅ 食物营养信息查询
- ✅ 食物比较分析
- ✅ 智能食物推荐
- ✅ 自动营养计算
- ✅ 分类浏览和搜索
- ✅ 份量转换和估算
数据源
主数据库
- 文件:
data/food-database.json - 内容: 50种常见食物的详细营养数据
- 结构: 每种食物包含30+营养素指标
分类体系
- 文件:
data/food-categories.json - 分类: 10大类,30+子类
- 支持: 按分类浏览和筛选
功能模块
1. 食物查询 (Food Query)
1.1 精确查询
用途: 根据食物名称查询营养信息
支持输入:
- 中文名称: "燕麦", "西兰花", "三文鱼"
- 英文名称: "Oats", "Broccoli", "Salmon"
- 别名: "燕麦片", "broccoli", "三文鱼肉"
查询流程:
- 接收食物名称
- 在数据库中搜索匹配项
- 支持模糊匹配和别名匹配
- 返回完整营养信息
返回信息:
- 基本信息 (名称、分类、标准份量)
- 宏量营养素 (卡路里、蛋白质、碳水、脂肪、纤维)
- 微量营养素 (维生素、矿物质)
- 特殊营养素 (Omega-3/6、胆碱等)
- 升糖指数数据
- 健康标签和适用人群
- 常见份量
- 营养优势说明
示例:
# 用户输入: "燕麦"
# 返回:
{
"name": "燕麦",
"name_en": "Oats",
"category": "谷物类",
"nutrition_per_100g": {
"calories": 389,
"protein_g": 16.9,
"carbs_g": 66.3,
"fat_g": 6.9,
"fiber_g": 10.6,
# ... 更多营养素
},
"health_tags": ["高纤维", "低GI"],
"glycemic_index": {"value": 55, "level": "低"}
}
1.2 模糊搜索
用途: 根据营养特征搜索食物
搜索条件:
- 营养素含量: "高蛋白", "高纤维", "低GI"
- 营养素组合: "高蛋白 低卡路里", "高纤维 低GI"
- 分类筛选: "谷物类", "蔬菜", "蛋白质"
- 适用人群: "素食友好", "高血压", "糖尿病"
搜索逻辑:
# 示例: 搜索"高蛋白 低卡路里"
def search_foods(criteria):
results = []
for food in database:
protein = food.nutrition_per_100g.protein_g
calories = food.nutrition_per_100g.calories
# 定义阈值
high_protein = protein >= 15 # 每100g≥15g蛋白质
low_calorie = calories <= 150 # 每100g≤150卡
if high_protein and low_calorie:
results.append(food)
return sorted(results, key=lambda x: x.protein_g, reverse=True)
返回格式:
- 按匹配度排序
- 显示关键营养素
- 标注匹配标签
1.3 分类浏览
用途: 按食物分类浏览所有食物
分类层级:
蛋白质来源
├── 肉类
├── 禽类
├── 鱼虾贝类
├── 蛋类
├── 豆类
├── 坚果种子
└── 乳制品
浏览模式:
- 列出某分类下所有食物
- 按营养素排序
- 按GI值排序
- 按健康标签筛选
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 781 lines · 0 tokens per session scan A cabf9ca7e81e
food-database-query is a skill published in the GitHub repository huifer/WellAlly-health (947 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,655 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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