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/commands/mood.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/commands/huifer/wellally-health/mood)<a href="https://agentmods.dev/commands/huifer/wellally-health/mood"><img src="https://agentmods.dev/badge/commands/huifer/wellally-health/mood/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/commands/huifer/wellally-health/mood"><img src="https://agentmods.dev/badge/commands/huifer/wellally-health/mood.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00007 | $0.08765 |
| Opus 5.5 | $0.00003 | $0.03506 |
| Sonnet 5.5 | $0.00001 | $0.01753 |
| Haiku 4.5 | $0.00001 | $0.00877 |
Grade A, and why
mood 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 1mo 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 — 1,106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
心理健康与情绪追踪
全面的心理健康监测系统，支持情绪打卡、睡眠与压力记录、智能相关性分析和AI驱动的洞察。
操作类型
1. 记录情绪 - add
记录当前情绪状态，包括情绪评分、睡眠质量和压力水平。
参数说明：
description: 情绪描述（必填），用自然语言描述情绪状态、睡眠和压力date: 记录日期（可选），格式 YYYY-MM-DD，默认为今天
示例：
/mood add 今天感觉有点焦虑，晚上睡不好
/mood add 8分 昨晚睡了7小时
/mood add 情绪很低落，持续三天了，压力很大
/mood add 很开心！睡得很好
2. 查看历史 - history
查看情绪记录历史。
示例：
/mood history
/mood history week
/mood history recent 10
3. 统计分析 - status
查看情绪统计分析和趋势。
示例：
/mood status
4. 相关性分析 - correlations
分析情绪与其他健康指标的相关性。
示例：
/mood correlations
5. AI洞察 - insights
获取AI驱动的模式识别和个性化建议。
示例：
/mood insights
6. 危机资源 - crisis
获取心理健康危机资源（无需数据）。
示例：
/mood crisis
执行步骤
记录情绪 (add)
1. 解析用户描述
从自然语言描述中提取以下信息：
情绪信息（自动提取）：
- 情绪分数：1-10分的主观评分
- 主要情绪：24种情绪类型之一
- 次要情绪：最多2种附加情绪（混合状态）
- 情绪强度：1-10的强度评分
睡眠信息（识别）：
- 睡眠时长（小时）
- 睡眠质量评分（1-10）
- 入睡时间
- 夜醒次数
- 醒后感受
压力信息（识别）：
- 压力水平（1-10）
- 压力来源（工作、学习、家庭等）
触发因素（提取）：
- 工作压力
- 睡眠不足
- 人际关系
- 身体不适
- 环境因素
2. 情绪分类系统
24种情绪类型：
正面情绪（8种）：
- 开心(happy) - 快乐、愉悦
- 兴奋(excited) - 激动、振奋
- 满足(content) - 知足、满意
- 感激(grateful) - 感恩、感谢
- 充满希望(hopeful) - 乐观、期待
- 平静(peaceful) - 安宁、平和
- 自豪(proud) - 骄傲、成就感
- 精力充沛(energized) - 活力、精神饱满
负面情绪（10种）： 9. 悲伤(sad) - 难过、伤心 10. 焦虑(anxious) - 担心、不安、紧张 11. 抑郁(depressed) - 消沉、低落、无望 12. 压力大(stressed) - 紧张、压力大 13. 愤怒(angry) - 生气、恼火 14. 沮丧(frustrated) - 挫败、失落 15. 孤独(lonely) - 孤单、寂寞 16. 不堪重负(overwhelmed) - 崩溃、难以承受 17. 烦躁(irritable) - 易怒、烦躁 18. 恐惧(fearful) - 害怕、恐惧
中性/身体状态（6种）： 19. 平静(calm) - 冷静、平静 20. 疲惫(tired) - 累、疲倦 21. 乏力(fatigued) - 乏力、无精打采 22. 麻木(numb) - 麻木、无感 23. 困惑(confused) - 困惑、迷茫 24. 冷漠(indifferent) - 冷漠、无所谓
情绪分类：
- 积极情绪：正面情绪
- 消极情绪：负面情绪
- 中性状态：中性状态
- 身体感受：疲惫、乏力、精力充沛
3. 情绪分数计算
评分规则（1-10分）：
- 基础分：5分（中间状态）
- 正向词汇检测：+1分
- 负向词汇检测：-1分
- 强度修饰语调整：
- "非常"、"特别"、"极其"：×2倍
- "有点"、"稍微"、"轻微"：×0.5倍
- 最终分数限制在1-10范围
分数描述：
- 9-10分：非常好
- 7-8分：良好
- 5-6分：中等
- 3-4分：较差
- 1-2分：很差
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.
- 1mo ago First seen · 1,106 lines · 7 tokens per session scan A 5686cd147305
mood is a command published in the GitHub repository huifer/WellAlly-health (963 stars, last pushed 3mo ago), licensed MIT. It adds 7 tokens to every session and 8,765 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-08-30.
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