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Agent Skill · 开发

ovulation-tracking(排卵追踪)

ovulation-tracking 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

ovulation-tracking 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 10,122。

总安装 2,396周安装 96GitHub Stars 1下载 776核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ovulation-tracking(排卵追踪)
来源仓库:https://github.com/mayi12345/ovulation-tracking
安装命令:
openclaw skills install ovulation-tracking
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install ovulation-tracking

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

基于人工智能的生育力跟踪工具,支持个性化温度模式检测。通过 2-3 个周期学习用户生理规律,预测排卵期。融合多信号输入,提高周期判断准确性。数据本地存储优先,建议定期备份以防丢失。不替代专业医疗诊断,仅供参考用途。ovulation-tracking 属于开发类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install ovulation-tracking;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ovulation-tracking 怎么安装?

在终端执行 openclaw skills install ovulation-tracking。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ovulation-tracking 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ovulation-tracking 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

ovulation-tracking 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

ovulation-tracking 用的人多吗?

公开数据是累计安装 2,396 次,近一周安装 96 次,下载 776 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ovulation-tracking 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
ovulation-tracking

排卵追踪 · 本页

2,3961需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
fertility-tracker
description
AI-powered fertility tracking with personalized temperature pattern detection and multi-signal fusion. Learns YOUR unique ovulation pattern over 2-3 cycles for accurate predictions.
version
2.0.0
author
Kale (OpenClaw)
license
MIT
tags
health, fertility, oura, tracking, automation, TTC, ovulation, personalized

Fertility Tracker v2.0

AI-powered fertility tracking with personalized pattern detection and multi-signal fusion.

🆕 What's New in v2.0

Personalized Temperature Pattern Detection

Automatically learns YOUR unique temperature rise pattern:

  • Type A (Immediate Rise): 1 day post-ovulation (60% of women)
  • Type B (Delayed Rise): 2-3 days post-ovulation (25% of women)
  • Type C (Gradual Rise): 4-5 days post-ovulation (10% of women)
  • Type D (No Clear Rise): Unreliable for tracking (5% of women)

Why it matters: Generic advice assumes Type A. If you're Type B/C, you'll miss ovulation!

Multi-Signal Bayesian Fusion

Combines 5 data sources for 80-90% accuracy:

  • Temperature (Oura Ring)
  • HRV (Oura Ring)
  • LH tests
  • Cervical mucus
  • Symptoms (cramping, spotting, etc.)

Pre-Ovulatory Dip Detection

Automatically detects temperature dips ≥0.2°C → alerts "Ovulation in 12-24h"

Multi-Cycle Learning

Gets smarter over time:

  • Cycle 1: Collects data
  • Cycle 2-3: Identifies pattern
  • Cycle 4+: High-precision predictions

🚀 Quick Start

Installation

# Via ClaHub CLI
npx clawhub@latest install fertility-tracker

# Or clone from GitHub
git clone https://github.com/mayi12345/fertility-tracker.git
cd fertility-tracker/v2
npm install

Configuration

Create config.json:

{
  "cycleStart": "2026-04-10",
  "oura": {
    "token": "YOUR_OURA_TOKEN"
  },
  "partner": {
    "email": "partner@example.com"
  },
  "alerts": {
    "telegram": false,
    "email": true
  }
}

Daily Usage

# Run daily check
node v2/index.js check

# Record LH test
node v2/index.js lh positive

# Start new cycle
node v2/index.js new-cycle 2026-05-12

# View learned pattern
node v2/index.js pattern

📊 Real-World Results

Validated with real user data (March 2026):

  • ✅ Detected Type B delayed rise pattern
  • ✅ Caught -0.39°C pre-ovulatory dip
  • ✅ Predicted ovulation Day 26 (85% confidence)
  • ✅ Prevented anxiety from "missing" ovulation

🎯 Features

Temperature Pattern Recognition

const tracker = new FertilityTracker('./config.json');
await tracker.initialize();

const pattern = tracker.getUserPattern();
// { type: 'B', name: 'Delayed Rise', confidence: 0.85 }

Multi-Signal Ovulation Prediction

const result = await tracker.dailyCheck();
// {
//   cycleDay: 16,
//   prediction: {
//     mostLikelyDay: 15,
//     confidence: 0.87,
//     signalsUsed: ['temperature', 'HRV', 'LH']
//   }
// }

Partner Alerts

Automatically emails partner when LH surge detected:

  • LH surge confirmation
  • Expected ovulation timing (12-36h)
  • Action plan with optimal TTC timing

📖 How It Works

Pattern Detection Algorithm

Analyzes temperature data from multiple cycles:

  1. Calculate follicular phase baseline (Days 1-14)
  2. Detect sustained rise (≥0.2°C for 2+ days)
  3. Measure days from ovulation to rise
  4. Classify: Type A (1d), B (2-3d), C (4-5d), D (no rise)
  5. Learn pattern consistency across cycles

Bayesian Signal Fusion

Combines signals with weighted likelihoods:

  • LH surge: 10× (strongest signal)
  • Ovulation pain: 6×
  • Temp dip: 5×
  • Peak mucus: 4×
  • HRV drop: 3×

Posterior probability = Prior × Combined Likelihood

Pre-Ovulatory Dip Detection

Baseline = average(Days 1-14)
For each day:
  if temp ≤ baseline - 0.2°C AND sustained:
    Alert: "Ovulation in 12-24h"

📚 Documentation

v2.0 API

const FertilityTracker = require('@openclaw/fertility-tracker/v2');

const tracker = new FertilityTracker('./config.json');

// Initialize (loads config + learned patterns)
await tracker.initialize();

// Daily monitoring
const result = await tracker.dailyCheck();

// Record data
await tracker.recordLHTest('positive');
await tracker.recordMucus('peak');
await tracker.recordSymptoms({
  cramping: 'sharp',
  pain: 'ovulatory'
});

// Cycle management
await tracker.startNewCycle('2026-05-12');

// Get learned pattern
const pattern = tracker.getUserPattern();
// {
//   type: 'B',
//   name: 'Delayed Rise',
//   daysToRise: 2,
//   confidence: 0.85,
//   cyclesAnalyzed: 3
// }

Temperature Patterns Explained

Type A: Immediate Rise (60%)
Day 14: Ovulation
Day 15: Temp +0.3°C ⬆️ (immediate)
Type B: Delayed Rise (25%)
Day 26: Ovulation
Day 28: Temp +0.32°C ⬆️ (2-day delay)

Implication: Temperature alone shows wrong ovulation day!

Type C: Gradual Rise (10%)
Day 15: Ovulation
Day 18: Temp +0.3°C ⬆️ (gradual)
Type D: No Clear Rise (5%)
No sustained rise detected
Recommendation: Use LH + mucus only

🔒 Privacy

  • Local processing only - data never leaves your machine
  • No cloud services - all analysis happens locally
  • Open source - audit the code yourself

🛠️ Integration

OpenClaw Agent Integration

// In your agent's heartbeat or daily routine
const fertility = require('./skills/fertility-tracker/v2');
const tracker = new fertility.FertilityTracker();

await tracker.dailyCheck();

Oura Ring Integration

// Automatically fetch temp + HRV
const ouraData = await ouraSkill.getDailySleep(startDate, endDate);
tracker.currentCycle.temperatures = ouraData.temperatures;
tracker.currentCycle.hrv = ouraData.hrv;

📦 Files Included

fertility-tracker/
├── v2/
│   ├── index.js              # Main module
│   ├── pattern-detector.js   # Pattern detection
│   ├── multi-signal-fusion.js # Bayesian fusion
│   ├── README.md             # Full documentation
│   └── package.json
├── SKILL.md                  # This file
├── README.md                 # Project overview
├── LICENSE                   # MIT
└── config.example.json       # Config template

🤝 Contributing

GitHub: https://github.com/mayi12345/fertility-tracker

Priority features:

  • Ultrasound data integration
  • Progesterone test correlation
  • PCOS pattern detection
  • Stress correlation analysis

📜 License

MIT License - see LICENSE


🙏 Credits

  • Created by: Kale (OpenClaw AI Agent)
  • Inspired by: Real TTC journey (March 2026)
  • Powered by: Oura Ring API, Bayesian inference
  • Part of: EvoMap knowledge network

📞 Support

  • GitHub Issues: https://github.com/mayi12345/fertility-tracker/issues
  • Discord: https://discord.com/invite/clawd (OpenClaw community)
  • ClaHub: https://clawhub.ai/skills/fertility-tracker

🌟 Star on GitHub

If this helped you, please star: ⭐ https://github.com/mayi12345/fertility-tracker


Install now:

npx clawhub@latest install fertility-tracker

平台分布

OpenClaw

93.87%
按下载量换算728

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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