Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JuneYaooo/mediwise-health-suite --skill health-monitorgit clone --depth 1 https://github.com/JuneYaooo/mediwise-health-suiteWrote 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/juneyaooo/mediwise-health-suite/health-monitor)<a href="https://agentmods.dev/skills/juneyaooo/mediwise-health-suite/health-monitor"><img src="https://agentmods.dev/badge/skills/juneyaooo/mediwise-health-suite/health-monitor/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/juneyaooo/mediwise-health-suite/health-monitor"><img src="https://agentmods.dev/badge/skills/juneyaooo/mediwise-health-suite/health-monitor.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 | $0.00136 | $0.01312 |
| Opus 5 | $0.00068 | $0.00656 |
| Sonnet 5 | $0.00027 | $0.00262 |
| Haiku 4.5 | $0.00014 | $0.00131 |
Grade A, and why
health-monitor 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 12d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MediWise · 健康记录提醒 Skill
按已配置阈值检查健康指标并生成多级记录提醒，支持全家 dashboard 一屏总览。所有级别都只表示规则优先级，不是病情严重程度或临床判断；本 Skill 不提供诊断、治疗或用药指导。
告警级别
| 级别 | 含义 | 处理方式 |
|---|---|---|
| info | 信息记录 | 仅记录 |
| warning | 普通提醒 | 创建普通优先级提醒记录 |
| urgent | 高优先级 | 创建高优先级提醒记录 |
| emergency | 最高优先级 | 创建最高优先级提醒记录 |
默认规则阈值
这些数值是可修改的内置提醒规则，不代表诊断标准或医疗建议。用户可根据自己的记录需求调整或关闭；如需医学阈值，应由专业医疗人员确定。
| 指标 | warning | urgent | emergency |
|---|---|---|---|
| 心率（高） | >100 bpm | >120 bpm | >150 bpm |
| 心率（低） | <55 bpm | <45 bpm | <35 bpm |
| 血氧（低） | <95% | <90% | <85% |
| 收缩压（高） | >140 mmHg | >160 mmHg | >180 mmHg |
| 舒张压（高） | >90 mmHg | >100 mmHg | >110 mmHg |
| 体温（高） | >37.3°C | >38.5°C | >39.5°C |
| 血糖空腹（高） | >6.1 mmol/L | >7.8 mmol/L | >11.1 mmol/L |
支持按年龄自动调整，支持用户自定义覆盖。
核心工作流
当前公开版本只用于个人本地档案。身份隔离由安装时的个人模式和 action 适配层处理，不向普通用户暴露或索取
owner_id。
0. 全家健康 Dashboard（首选入口）
用户说「看看全家健康」「今天家人状态怎样」「健康概览」时，优先调用此接口。 返回所有成员的风险级别、未解决告警数、最新关键指标和趋势警告。
# 全家健康一屏总览
python3 {baseDir}/scripts/dashboard.py show
1. 阈值管理
# 查看阈值配置（含默认+自定义）
python3 {baseDir}/scripts/threshold.py list --member-id <id>
# 自定义阈值
python3 {baseDir}/scripts/threshold.py set --member-id <id> --type heart_rate --level warning --direction above --value 110
# 恢复默认
python3 {baseDir}/scripts/threshold.py reset --member-id <id> --type heart_rate
2. 异常检测
# 检查单个成员
python3 {baseDir}/scripts/check.py run --member-id <id>
# 检查所有成员
python3 {baseDir}/scripts/check.py run-all
# 检查最近指定时间窗口
python3 {baseDir}/scripts/check.py run --member-id <id> --window 24h
3. 趋势分析
# 单指标趋势
python3 {baseDir}/scripts/trend.py analyze --member-id <id> --type heart_rate --days 7
# 全指标摘要
python3 {baseDir}/scripts/trend.py report --member-id <id>
4. 告警管理
# 查看未解决告警
python3 {baseDir}/scripts/alert.py list --member-id <id>
# 按级别筛选
python3 {baseDir}/scripts/alert.py list --member-id <id> --level urgent
# 标记已解决
python3 {baseDir}/scripts/alert.py resolve --alert-id <id>
# 告警历史
python3 {baseDir}/scripts/alert.py history --member-id <id> --limit 20
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 119 lines · 136 tokens per session scan A e297178d5234
health-monitor is a skill published in the GitHub repository JuneYaooo/mediwise-health-suite (29 stars, last pushed yesterday), licensed MIT. It adds 136 tokens to every session and 1,312 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
flux-analyzer
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
fba-simulator
Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
gsmm-validator
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
gsmm-builder
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
stat-result-validator
Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
statistical-experimental-evaluation
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.