Generate a personalized daily health coaching message by dispatching 10 specialist subagents that each review the user's data from their domain expertise. Each subagent delivers its own message as a separate Telegram bubble.
Use this skill when:
Call both tools to build the shared context payload:
coaching_context tool:{ }
nutrition tool for weekly summary:{
"command": "weekly_summary",
"end_date": "YYYY-MM-DD"
}
If insufficient_data is true: skip subagent dispatch. Instead say what is missing and what to log next.
Read each agent prompt file from and spawn all 10 in parallel using .
{baseDir}/../../agents/sessions_spawn| # | File | Role | Emoji |
|---|---|---|---|
| 1 | imperial-physician.md | Orchestrator — #1 priority for today | 🏥 |
| 2 | diet-physician.md | Nutrition — meals, macros, micros | 🍚 |
| 3 | movement-master.md | Exercise — strain, training load | 🏃 |
| 4 | pulse-reader.md | Body metrics — RHR, HRV, SpO2 | 💓 |
| 5 | formula-tester.md | Cross-domain pattern detection | 🧪 |
| 6 | herbalist.md | Supplement considerations | 🌿 |
| 7 | trial-monitor.md | Experiment status + compliance | 📋 |
| 8 | court-magistrate.md | Trial design candidates | ⚖️ |
| 9 | medical-censor.md | Safety flags + warnings | 🛡️ |
| 10 | court-scribe.md | Relevant news + literature | 📜 |
For each agent in the registry:
read("{baseDir}/../../agents/{file}"){contents of the agent .md file}
---
TODAY'S CONTEXT:
{paste the full JSON context payload from Step 1}
WEEKLY NUTRITION:
{paste the weekly summary JSON from Step 1}
sessions_spawn(task=<constructed task>, label=<role name>)Spawn ALL 10 in parallel. Each subagent runs independently and announces its result back as a separate message.
Each subagent announces directly to the chat channel as a separate Telegram bubble. They arrive as each finishes. The main agent does NOT need to collect or reformat the results.
After all 10 have announced, if checkin_needed is true, send one final message prompting the user to log their experiment check-in.
[Role Emoji].The install script (step 6) configures agents.defaults.subagents.maxChildrenPerAgent and maxConcurrent to 10 in ~/.openclaw/openclaw.json. Without this, only 5 of the 10 specialists will spawn.