# Health Mate > 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. - Skill: `tankeito/health-mate` (Agent Skill, multi-file: 8 files) - Install (CLI): `npx skillmds@latest add tankeito/health-mate` - Raw SKILL.md: https://api.skillmd.com/api/skills/tankeito/health-mate/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tankeito (https://skillmd.com/u/tankeito) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/tankeito/health-mate --- # 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 ```bash 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: ```markdown # 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: ```markdown # 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: ```markdown ## 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