health-mate（健康伴侣）

Health-Mate

Health-Mate is an executable OpenClaw skill, not a prompt-only package.

It reads structured Markdown logs from MEMORY_DIR, generates localized PDF reports, and can optionally deliver the final message to external services.

What It Does

• Parses meals, hydration, exercise, symptoms, medication, and custom monitoring sections
• Generates daily reports with scoring, detail sections, AI insight, risk alerts, and next-day actions
• Generates weekly reports with rings, paired trend charts, audience-specific insight modules, weekly review, and next-week plan
• Generates monthly reports with radar, branch-aware heatmaps, 30-day weight and BMR trend, specialty charts, follow-up reminders, and clinic suggestions
• Supports explicit report routing through report_preferences.population_branch, while the setup wizard still auto-suggests lifestyle vs disease mode from the primary goal
• Switches balanced / fat_loss reports into a lifestyle-review path with activity heatmaps, energy-balance, habit-progression, and lean-mass/fat-mass charts while skipping hospital lookup
• Supports multi-condition management in both LLM and local fallback paths
• Keeps core parsing, scoring, and PDF rendering local

Installation

pip install -r requirements.txt

Dependencies:

• reportlab
• pillow
• matplotlib

Required Runtime Setup

Required:

• MEMORY_DIR

Optional:

• NVM_DIR
• CRON_PATH
• OPENCLAW_BIN
• TAVILY_API_KEY
• DINGTALK_WEBHOOK
• FEISHU_WEBHOOK
• TELEGRAM_BOT_TOKEN
• TELEGRAM_CHAT_ID
• LOG_FILE
• REPORT_WEB_DIR
• REPORT_BASE_URL
• ALLOW_RUNTIME_FONT_DOWNLOAD

For ClawHub manual folder upload:

• config/.env.example may be missing from the uploaded package
• use the top-level env block inside config/user_config.example.json as the upload-safe reference template
• the setup wizard can create a commented project-local config/.env template if the file does not already exist
• runtime scripts still read project-local config/.env
• keep only the keys you intend this skill to read inside project-local config/.env

Upgrade And Backup Notice

Before upgrading or reinstalling this skill, back up:

• config/user_config.json
• config/.env
• any local font file you manually placed under assets/

Important:

• some platform upgrade or reinstall flows may overwrite, reset, or remove local configuration files
• after an upgrade, re-check MEMORY_DIR, report preferences, scoring modules, webhook settings, and Tavily settings before running scheduled jobs again
• if needed, also verify report_preferences.population_branch in config/user_config.json

Local And Network Behavior

Expected local file I/O:

• reads Markdown logs from MEMORY_DIR
• reads project-local config/.env when shell runners are used and the file exists
• the setup wizard may create a commented project-local config/.env template when the file is missing
• may rely on config/user_config.example.json as the upload-safe env reference during manual installation
• writes PDFs into reports/
• writes logs into logs/
• may create a temporary English memory mirror for rendering fallback

Expected network I/O:

• Tavily only when TAVILY_API_KEY is configured
• webhook delivery only when the matching delivery credentials are configured
• runtime font download only when ALLOW_RUNTIME_FONT_DOWNLOAD=true

Important:

• there is no implicit default MEMORY_DIR fallback in the shell runners
• the skill exits if MEMORY_DIR is missing
• shell runners use NVM_DIR and CRON_PATH from the environment or project-local config/.env, with built-in defaults when those keys are missing
• the Python local-LLM resolver uses OPENCLAW_BIN first and then tries common install paths without hardcoding a single fixed cron PATH

Commands

• /health

Daily report

• /health summary

Weekly report

• /health month

Monthly report

Memory Write Protocol

When writing into MEMORY_DIR, the model must act like a strict recorder.

Hard rules:

1. Never write commentary, advice, summaries, emoji, or chat filler into the file.
2. Meals, hydration, medication events, and exercise events must use level-3 headings with time markers.
3. Hydration blocks must remain minimal and structured.
4. Step totals must stay in one dedicated level-2 block.
5. Monitoring modules must use stable level-2 headings.
6. Use one language per block.

Core template:

# 2026-03-20 Health Log

## Meals
### Breakfast (around 08:30)
- Oatmeal 50g -> approx. 190kcal
- Skim milk 250ml -> approx. 87kcal

## Hydration
### Morning (around 09:45)
- Water intake: 300ml
- Cumulative: 300ml/2000ml

## Exercise
### Afternoon Cycling (around 17:17)
- Distance: 10km
- Duration: 47min
- Burn: approx. 300kcal

## Today Steps
- Total steps: 8500 steps

Chinese core template:

# 2026-03-20 健康记录

## 体重记录
- 晨起空腹：64.4kg

## 饮水记录
### 上午（约 08:45）
- 饮水量：300ml
- 累计：300ml/2000ml

## 饮食记录
### 早餐（约 08:50）
- 燕麦片 50g -> 约 190kcal
- 脱脂牛奶 250ml -> 约 87kcal

## 运动记录
### 下午骑行（约 17:10）
- 距离：10.2km
- 耗时：42min
- 消耗：约 290kcal

## 今日步数
- 总步数：8200 步

## 用药记录
- 胆舒胶囊：1 粒

Chinese monitoring-module constraints:

• ## 血压记录 / ## 血糖记录 / ## 体成分 / ## 生化记录 must stay as stable level-2 titles
• numeric monitoring blocks should use stable item labels such as - 血压：128/82 mmHg, - 血糖：7.1 mmol/L, - ALT：34 U/L
• do not mix commentary into monitoring blocks
• do not turn monitoring blocks into tables
• do not rename the same monitoring module on different days unless the user intentionally changed the module name

Recommended LLM instruction:

• paste the English or Chinese template above directly into your system prompt, soul.md, or memory-write policy so the model stays inside the parser-safe structure
• if custom modules are enabled in user_config.json, add their exact section titles to the LLM write protocol and keep those titles stable across days

Expandable monitoring modules:

## Blood Pressure
### Morning (around 08:00)
- Blood Pressure: 128/82 mmHg
- Heart Rate: 72 bpm

## Glucose Record
### After Breakfast (around 10:10)
- Glucose: 7.1 mmol/L
- Timing: 2h after breakfast

## Body Composition
- Weight: 64.4kg
- Body Fat: 18.6%

## Biochemistry
- ALT: 34 U/L
- AST: 28 U/L

Forbidden content:

• Assessment
• Status
• Summary
• motivational filler
• debug notes
• system logs
• tables inside daily memory files

Monthly Report Expectations

The monthly report now includes:

• macro adherence radar
• healthy-day donut chart based on the full calendar month
• symptom and medication heatmap
• 30-day weight and BMR trend
• condition-specific specialty charts
• lifestyle-mode specialty charts for balanced / fat_loss: energy balance, four-week habit progression, and lean-mass/fat-mass composition
• AI monthly review
• follow-up reminders or periodic screening suggestions, depending on the active monthly mode
• residence-aware hospital-and-doctor suggestions with grouped recommendations, grades, booking hints, and optional fee / schedule fields for disease-management modes

If the user manages multiple conditions, the monthly report should combine them instead of collapsing to a single narrow perspective.

Font Fallback

Preferred CJK font paths:

• assets/NotoSansSC-VF.ttf
• assets/NotoSansJP-VF.ttf

If one of them is missing:

• the skill may switch to an English-compatible rendering path
• the output adds a rendering notice
• users who need Chinese PDF output should place NotoSansSC-VF.ttf into assets/
• users who need Japanese PDF output should place NotoSansJP-VF.ttf into assets/

Changelog

v1.5.4 - 2026-04-01

• Enhanced PDF visual styling with refined layout and improved readability
• Fixed minor formatting issues in daily, weekly, and monthly report generators
• Optimized symbol rendering for better cross-platform compatibility

v1.5.3 - 2026-03-29

• Sanitized local-LLM stdout before AI commentary is embedded into push text or PDFs, preventing OpenClaw plugin-registration logs from leaking into reports
• Added a second daily-PDF commentary filter as a defensive fallback for unexpected plugin log fragments
• Aligned direct Python execution with the shell runners by auto-loading project-local config/.env when variables are not already exported

v1.5.2 - 2026-03-25

• Updated the upload-safe env example with OpenClaw default MEMORY_DIR, sanitized web-publish placeholders, and the shared LOG_FILE example
• Extended init_config.py so first-time setup can create a commented project-local config/.env template without overwriting an existing one
• Clarified SKILL metadata and runtime notes around project-local .env, cron PATH helpers, and optional-network behavior
• Verified that the setup wizard really creates the commented config/.env template when the file is missing
• Added a monthly lifestyle-review split for balanced / fat_loss, including new charts, renamed section titles, screening suggestions, and hospital-lookup bypass

v1.5.1 - 2026-03-24

• Optimized Cron environment configuration for reliable LLM invocation in scheduled tasks
• Embedded the upload-safe env reference into config/user_config.example.json
• Kept daily_health_report_pro.sh, weekly_health_report_pro.sh, and monthly_health_report_pro.sh loading environment variables from .env
• Changed all shell script comments to English for better internationalization
• Ensures scheduled daily/weekly/monthly reports can successfully call local LLM for AI insights

v1.5.0 - 2026-03-23

• Removed the legacy scripts/health_report_pro.py and scripts/pdf_generator.py wrappers
• Extended init_config.py to cover ja-JP
• Clarified env-first local LLM resolution through OPENCLAW_BIN and common-path auto-discovery
• Added explicit Japanese font fallback guidance and English-fallback notice behavior

v1.4.0 - 2026-03-21

• Added monthly reporting
• Added weekly and monthly symptom and medication heatmaps
• Added monthly weight and BMR trend output
• Added residence-aware monthly medical planning
• Refreshed README, README_ZH, SKILL metadata, and package metadata