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ovulation-tracking排卵追踪

Agent Skill

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。

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2,396

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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。

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openclaw skills install ovulation-tracking

简介

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。

SKILL.md

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

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能力 5

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