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health-qa

Answer health questions by routing to the most relevant specialist subagent(s) from the 10-agent roster, grounded in the user's own data.

معلومات المصدر

المستودع
compound-life-ai/Turri
آخر نشاط في المصدر
٥ أبريل ٢٠٢٦ في ١٦:٤٤
لغة SKILL.md المكتشفة
الإنجليزية
النجوم
٧
التفرعات
١

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

عرض SKILL.md

SKILL.md
تعليمات المصدر · معاينة للقراءة فقط
name
health-qa
description
Answer health questions by routing to the most relevant specialist subagent(s) from the 10-agent roster, grounded in the user's own data.
user-invocable
false
# Health Q&A Use this skill when: - the user asks a health-related question about their own data (e.g. "how's my HRV?", "am I eating enough protein?", "should I train today?") - the user asks for health advice that can be answered by one or more specialist agents - the user references sleep, recovery, strain, nutrition, supplements, experiments, or body metrics **This is the REQUIRED path for health data questions.** Do not skip this skill and answer directly by calling raw tools (health_profile, nutrition, etc.) yourself. The specialist agents have calibrated decision logic, flag thresholds, and domain expertise that the main agent does not replicate. Always route through this skill — fetch the data, spawn the specialists, return their answers. Do NOT use this skill when: - the user wants to log a meal (use `snap`) - the user wants to update their health profile (use `health`) - the user wants to start/manage an experiment (use `insights`) - the question is general knowledge with no connection to the user's data ## Step 1: Classify the question Map the user's question to 1-3 relevant specialist agents using this routing table: | Domain | Agent File | When to Route | |--------|-----------|---------------| | Overall priority / "what should I focus on?" | `imperial-physician.md` | General health questions, "how am I doing?", priority questions | | Nutrition, meals, macros, calories, diet | `diet-physician.md` | Food, eating, protein, calories, macros, meal timing | | Exercise, training, strain, workout | `movement-master.md` | Training advice, workout planning, strain, exercise | | HRV, heart rate, SpO2, recovery score, body metrics | `pulse-reader.md` | Vital signs, cardiovascular, Whoop metrics | | Cross-domain patterns, correlations | `formula-tester.md` | "Why is my X affecting Y?", pattern questions | | Supplements, micronutrients, vitamins | `herbalist.md` | Supplement questions, vitamin/mineral gaps | | Experiment status, check-ins, compliance | `trial-monitor.md` | "How's my experiment going?", check-in reminders | | Experiment design, "should I test X?" | `court-magistrate.md` | Trial design, hypothesis questions | | Safety, overtraining, warning signs | `medical-censor.md` | "Am I overtraining?", safety concerns, red flags | | Research, studies, news | `court-scribe.md` | "Any research on X?", literature questions | Routing rules: - Always include `imperial-physician.md` if the question is broad or ambiguous - Always include `medical-censor.md` if there's any safety concern in the question - For narrow questions (e.g. "how's my HRV?"), route to just 1 agent - Maximum 3 agents per question — pick the most relevant - When in doubt between 2 agents, include both ## Step 2: Gather data for the agents Call the raw tools to fetch the data each selected agent will need. Only fetch what's relevant: | Agent Needs | Tool Call | |-------------|-----------| | Nutrition data | `nutrition` with `command: "weekly_summary"` and/or `command: "lookup"` | | Health profile / Whoop metrics | `health_profile` with `command: "show"` | | Experiment status | `experiments` with `command: "status"` | | News / research | `news_digest` with `command: "show"` | Fetch in parallel when multiple tools are needed. ## Step 3: Spawn specialist agents For each selected agent: 1. Read the agent prompt: `read("{baseDir}/../../agents/{file}")` 2. Construct the task: ``` {contents of the agent .md file} --- IMPORTANT: You are answering a specific user question, not giving a daily briefing. - Answer the user's question directly using your domain expertise and the data below. - Your response MUST start with your role tag (e.g. [Pulse Reader 💓]) on its own line. - Keep it to 2-3 sentences, grounded in the data. USER'S QUESTION: {the user's original question} AVAILABLE DATA: {paste the relevant data fetched in Step 2} ``` 3. Spawn: `sessions_spawn(task=<constructed task>, label=<role name>)` Spawn all selected agents in parallel. ## Step 4: Collect and respond Wait for all spawned agents to complete. Present the specialist responses **verbatim** — do NOT rephrase, summarize, or strip the role tags. - Every response MUST keep the `[Role Emoji]` prefix (e.g. `[Pulse Reader 💓]`). This tells the user which specialist is speaking. - If 2-3 agents were spawned: present each response as a separate section. - Do NOT add your own commentary before or around the specialist responses. If the agents' advice needs to be reconciled, add a brief 1-sentence synthesis AFTER all specialist responses. ## Rules - Reply in the user's language if obvious from context. Otherwise English. - Each agent response should be 2-3 sentences as defined in their prompt files. - Recommendations must be conservative, lifestyle-only, grounded in the user's own data. - Do not overclaim from sparse data. Agents should say "insufficient data" when appropriate. - If no data is available for the question (e.g. no Whoop connected, no meals logged), skip agent dispatch and tell the user what data they need to provide first.
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