By name: daily-coach description: 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. user-invocable: false
Daily Coach
Use this skill when:
- a scheduled daily coaching message needs to be generated
- the user explicitly asks for the cron-generated daily health coaching behavior
Step 1: Gather context
Call both tools to build the shared context payload:
- Call the
coaching_contexttool:
{ }
- Call the
nutritiontool 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.
Step 2: Dispatch 10 specialist subagents
Read each agent prompt file from {baseDir}/../../agents/ and spawn all 10 in parallel using sessions_spawn.
Agent Registry
| # | 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 | 📜 |
Dispatch protocol
For each agent in the registry:
- Read the agent prompt:
read("{baseDir}/../../agents/{file}") - Construct the task:
{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}
- Spawn:
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.
Step 3: No assembly needed
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.
Rules
- Reply in the user's language if obvious from profile context. Otherwise English.
- Each subagent produces 2-3 sentences starting with
[Role Emoji]. - 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.
- Subagents should NOT repeat each other's recommendations — each owns their domain.
OpenClaw config requirements
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.