daily-coach

# 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: 1. Call the `coaching_context` tool: ```json { } ``` 2. Call the `nutrition` tool for weekly summary: ```json { "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: 1. Read the agent prompt: `read("{baseDir}/../../agents/{file}")` 2. 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} ``` 3. 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.