Health Mate

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name: health-mate display_name: Health-Mate version: 1.5.4 type: python/app description: "Executable OpenClaw health-report skill with Chinese, English, and Japanese report flows. It reads Markdown logs only from an explicitly configured MEMORY_DIR, writes reports and logs locally, can create a commented project-local config/.env template during setup, sanitizes local-LLM stdout before AI commentary is embedded into reports, separates monthly disease mode from balanced/fat-loss lifestyle mode, and only performs Tavily, webhook, or font-download network activity when the corresponding runtime options are configured." install: pip install -r requirements.txt capabilities: - file_read - file_write - pdf_generation - http_request metadata: openclaw: emoji: "🏥" homepage: "https://github.com/tankeito/Health-Mate" clawdbot: requires: env: - MEMORY_DIR env: MEMORY_DIR: Required. Explicitly set this to the Markdown health-memory directory that the skill may read. NVM_DIR: Optional. Node.js NVM directory used by the shell runners for scheduled execution. CRON_PATH: Optional. Cron-safe PATH value used by the shell runners so openclaw and system commands can be found. OPENCLAW_BIN: Optional. Absolute path to the local openclaw binary used by the Python local-LLM resolver. HEALTH_MATE_LANG: Optional. Force the output locale such as zh-CN, en-US, or ja-JP. TAVILY_API_KEY: Optional. Enables Tavily-assisted fallback guidance and monthly clinic lookup hints. DINGTALK_WEBHOOK: Optional. Enables DingTalk delivery. FEISHU_WEBHOOK: Optional. Enables Feishu delivery. TELEGRAM_BOT_TOKEN: Optional. Enables Telegram delivery when paired with TELEGRAM_CHAT_ID. TELEGRAM_CHAT_ID: Optional. Required only for Telegram delivery. LOG_FILE: Optional. Overrides the shell-runner log file path; if omitted, each runner uses its own file under logs/. REPORT_WEB_DIR: Optional. Local directory used when generated PDFs should be copied for public download. REPORT_BASE_URL: Optional. Public base URL used for generated PDF links. ALLOW_RUNTIME_FONT_DOWNLOAD: Optional. Disabled by default. Set to true only if runtime fallback download of NotoSansSC-VF.ttf is explicitly allowed.

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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