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/nutrition-analyzer/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/nutrition-analyzer)<a href="https://agentmods.dev/skills/huifer/wellally-health/nutrition-analyzer"><img src="https://agentmods.dev/badge/skills/huifer/wellally-health/nutrition-analyzer/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/nutrition-analyzer"><img src="https://agentmods.dev/badge/skills/huifer/wellally-health/nutrition-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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.
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 | $0.00045 | $0.07325 |
| Opus 5 | $0.00023 | $0.03662 |
| Sonnet 5 | $0.00009 | $0.01465 |
| Haiku 4.5 | $0.00005 | $0.00732 |
Grade A, and why
nutrition-analyzer 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 9d 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 — 776 lines — stays where its author put it; the contents beside it link to each section on GitHub.
营养分析器技能
分析饮食和营养数据，识别营养模式，评估营养状况，并提供个性化营养改善建议。
功能
1. 营养趋势分析
分析营养素摄入的变化趋势，识别改善或需要关注的方面。
分析维度：
- 宏量营养素趋势（蛋白质、碳水、脂肪、纤维、卡路里）
- 微量营养素趋势（维生素、矿物质）
- 热量来源分布变化
- 餐食模式（饮食时间、频率）
- 食物类别偏好
输出：
- 趋势方向（改善/稳定/下降）
- 变化幅度和百分比
- 趋势显著性
- 改进建议
2. 营养素摄入评估
评估营养素摄入是否达到推荐标准（RDA/AI）。
评估内容：
-
宏量营养素评估：
- 蛋白质摄入量和质量
- 碳水化合物类型分布（精制 vs 复杂碳水）
- 脂肪类型分布（饱和/单不饱和/多不饱和/反式脂肪）
- 膳食纤维摄入量
-
维生素评估：
- 维生素A、C、D、E、K
- 维生素B族（B1、B2、B3、B6、B12、叶酸、泛酸、生物素）
- 与RDA对比
- 缺乏风险评估
-
矿物质评估：
- 常量矿物质：钙、磷、镁、钠、钾、氯、硫
- 微量矿物质：铁、锌、铜、锰、碘、硒、铬、钼
- 与RDA对比
- 缺乏风险评估
-
特殊营养素评估：
- Omega-3脂肪酸（EPA、DHA、ALA）
- 胆碱
- 辅酶Q10
- 植物化学物（类黄酮、类胡萝卜素等）
输出：
- 每种营养素的达成率
- 缺乏/不足/充足/过量分级
- 缺乏风险识别
- 优先改善建议
3. 营养状况评估
综合评估用户的营养状况。
评估内容：
-
整体营养质量评分：
- 营养密度评分
- 食物多样性评分
- 均衡饮食评分
-
营养模式识别：
- 饮食模式类型（地中海式、DASH、素食等）
- 饮食时间模式（进食频率、进食窗口）
- 零食和加餐模式
-
营养风险识别：
- 营养缺乏风险（如维生素D缺乏、铁缺乏）
- 营养过量风险（如维生素A过量、钠过量）
- 不健康饮食习惯（高糖、高脂、高钠）
输出：
- 营养状况等级（优秀/良好/一般/较差）
- 主要营养问题识别
- 风险因素列表
- 改善优先级
4. 相关性分析
分析营养与其他健康指标的相关性。
支持的相关性分析：
-
营养 ↔ 体重：
- 卡路里摄入与体重变化的关系
- 宏量营养素比例与体重管理
- 进食时间与代谢关系
-
营养 ↔ 运动：
- 营养摄入对运动表现的影响
- 运动日vs休息日的营养需求
- 蛋白质摄入与肌肉恢复
-
营养 ↔ 睡眠：
- 咖啡因摄入与睡眠质量
- 晚餐时间与入睡时间
- 特定营养素（如镁、色氨酸）与睡眠
-
营养 ↔ 血压：
- 钠摄入与血压
- 钾/钠比值与血压
- DASH饮食依从性与血压控制
-
营养 ↔ 血糖：
- 碳水化合物类型与血糖波动
- 膳食纤维与血糖控制
- 进食时间与血糖曲线
输出：
- 相关系数（-1到1）
- 相关性强度（弱/中/强）
- 统计显著性
- 因果关系推断
- 实践建议
5. 个性化建议生成
基于用户数据生成个性化营养改善建议。
建议类型：
-
营养素调整建议：
- 增加缺乏的营养素
- 减少过量的营养素
- 优化营养素比例
-
食物选择建议：
- 推荐特定食物类别
- 食物替换建议（更健康的选择）
- 食物搭配建议（促进吸收）
-
饮食习惯建议：
- 进食时间调整
- 餐食频率调整
- 烹饪方式建议
-
补充剂建议（仅供参考）：
- 基于缺乏风险的补充剂建议
- 补充剂剂量和时机
- 相互作用警示
建议依据：
- DRIs/RDA标准
- 用户营养历史数据
- 用户健康状况和目标
- 循证营养学证据
使用说明
触发条件
当用户请求以下内容时触发本技能：
- 营养趋势分析
- 营养素摄入评估
- 营养状况评估
- 营养改善建议
- 营养与其他健康指标的关联分析
执行步骤
步骤 1: 确定分析范围
明确用户请求的分析类型和时间范围：
- 分析类型：趋势/评估/相关性/建议
- 时间范围：周/月/季度/自定义
- 分析深度：宏量营养素/微量营养素/全面分析
步骤 2: 读取数据
主要数据源：
data-example/nutrition-tracker.json- 营养追踪主数据data-example/nutrition-logs/YYYY-MM/YYYY-MM-DD.json- 每日饮食记录
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.
- 9d ago First seen · 776 lines · 45 tokens per session scan A 58a317dbd74e
nutrition-analyzer is a skill published in the GitHub repository huifer/WellAlly-health (948 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 7,325 once invoked, about $0.0002 per session on Opus 5. 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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