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/postpartum.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/postpartum)<a href="https://agentmods.dev/commands/huifer/wellally-health/postpartum"><img src="https://agentmods.dev/badge/commands/huifer/wellally-health/postpartum/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/postpartum"><img src="https://agentmods.dev/badge/commands/huifer/wellally-health/postpartum.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 | $0.00009 | $0.07569 |
| Opus 5 | $0.00005 | $0.03785 |
| Sonnet 5 | $0.00002 | $0.01514 |
| Haiku 4.5 | $0.00001 | $0.00757 |
Grade A, and why
postpartum 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 11d 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 — 1,080 lines — stays where its author put it; the contents beside it link to each section on GitHub.
产后护理管理
全面的产后恢复追踪和新生儿护理管理,从分娩到产后恢复期,提供专业的产后健康监测和指导。
⏱️ 可选追踪期:6周(42天)/6个月(180天)/1年(365天)
👶 宝宝护理:基础喂养、睡眠、体重、尿布记录
🧠 心理健康:EPDS抑郁筛查 + 红色警示系统
操作类型
1. 开始产后记录 - start
初始化产后恢复记录。
参数说明:
info: 分娩信息(必填)- 分娩日期:YYYY-MM-DD
- 分娩方式:vaginal(顺产)/c-section(剖宫产)
- 宝宝数量:1/2/3/4(可选,默认1)
- 追踪期:6weeks/6months/1year(可选,默认6months)
示例:
/postpartum start 2025-10-08 vaginal
/postpartum start 2025-10-08 c-section 6weeks
/postpartum start 2025-10-08 vaginal 2-babies 1year
执行步骤:
1. 解析分娩信息
提取信息:
- 分娩日期:YYYY-MM-DD格式
- 分娩方式:
- 顺产:vaginal, natural, 顺产, 阴道分娩
- 剖宫产:c-section, cesarean, 剖宫产, 剖腹产
- 宝宝数量:1, 2, 3, 4(默认1)
- 追踪期:
- 6weeks: 42天(标准)
- 6months: 180天(推荐)
- 1year: 365天(完整)
2. 验证输入
检查项:
- 分娩日期不能是未来日期
- 分娩日期应在过去2周内(避免过期数据)
- 宝宝数量应在合理范围(1-4)
3. 计算产后天数和阶段
产后阶段划分:
days_postpartum = today - delivery_date
if (days_postpartum <= 2) {
stage = "immediate" // 急性期(0-2天)
} else if (days_postpartum <= 14) {
stage = "early" // 早期(3-14天)
} else if (days_postpartum <= 42) {
stage = "subacute" // 亚急性期(15-42天)
} else {
stage = "late" // 恢复期(43天+)
}
4. 创建产后记录
数据结构:
{
"postpartum_id": "postpartum_20251008",
"delivery_date": "2025-10-08",
"delivery_type": "vaginal",
"baby_count": 1,
"tracking_period": "6months",
"tracking_end_date": "2026-04-06",
"current_status": {
"days_postpartum": 0,
"stage": "immediate",
"progress_percentage": 0
},
"recovery_tracking": {
"lochia": {
"stage": "rubra",
"amount": "moderate",
"last_updated": null
},
"perineal_care": {
"healing": "good",
"pain_level": 3,
"incision_type": null,
"notes": ""
},
"breastfeeding": {
"status": "establishing",
"challenges": [],
"last_updated": null
},
"pain": {
"uterine_contractions": {
"present": true,
"severity": "moderate"
},
"incision_pain": null,
"back_pain": null,
"headache": null
}
},
"mental_health": {
"epds": {
"last_screened": null,
"total_score": null,
"risk_level": "not_screened",
"q10_positive": false,
"last_updated": null
},
"mood_log": []
},
"physical_recovery": {
"pelvic_floor": {
"status": "recovering",
"exercises": "not_started",
"notes": ""
},
"diastasis_recti": {
"present": null,
"severity": null,
"assessed": false
},
"weight_tracking": [],
"sleep_tracking": []
},
"babies": [
{
"baby_id": "A",
"name": null,
"gender": null,
"birth_weight": null,
"current_weight": null,
"feeding": {
"method": "establishing",
"pattern": "on_demand",
"last_feed": null,
"feeds_log": []
},
"sleep": {
"pattern": "newborn",
"last_sleep": null,
"sleep_log": []
},
"diapers": {
"count": 0,
"last_change": null,
"diaper_log": []
},
"notes": ""
}
],
"red_flags": {
"active": [],
"resolved": [],
"last_assessment": null
},
"metadata": {
"created_at": "2025-10-08T00:00:00.000Z",
"last_updated": "2025-10-08T00:00:00.000Z"
}
}
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.
- 11d ago First seen · 1,080 lines · 9 tokens per session scan A 09370d5bc045
postpartum is a command published in the GitHub repository huifer/WellAlly-health (943 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 7,569 once invoked, about $0.0000 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.
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roster-doctor
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
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specify
Create or update the feature specification from a natural language feature description.