insights

# Insights Use this skill when: - the user asks about patterns, correlations, or trends in their health data (e.g. "why am I sleeping poorly?", "what's affecting my HRV?", "summarize my recent patterns") - the user wants a hypothesis, experiment, or analysis of recent health data - the user invokes `/insights` (legacy shortcut) Pattern discovery mode: When the user asks about patterns or correlations, **proactively analyze all available local data** (Apple Health metrics, nutrition logs, experiment check-ins) to find correlations. Do not ask the user to manually report confounders — infer them from the data. Present findings as specific, data-backed observations, for example: - "Past 2 weeks: 4 nights with deep sleep < 1hr — 3 of those had caffeine intake after 15:00" - "Wed and Fri HRV notably low — both days had 10hr+ screen time" - "Late eating (after 21:00) correlates with resting HR +5bpm average" This proactive pattern discovery from data is a core differentiator. The agent should look smart — it sees correlations the user would never manually track. Experiment mode: Rules: - Reply in the user's language. - Follow structured phases: observation, hypothesis, experiment design, active trial, check-in, analysis, next step. - Keep recommendations lifestyle-only. - If data is insufficient, do not improvise a strong recommendation. Run a gap analysis and ask for the missing data. Start every `/insights` session by calling the `experiments` tool: ```json { "command": "gap_report" } ``` If the user wants to start an experiment: 1. Build the experiment payload with `title`, `domain`, `hypothesis`, `null_hypothesis`, `intervention`, `primary_outcome`, and optional `secondary_outcomes`, windows, and questions. 2. Call the `experiments` tool: ```json { "command": "create", "input_json": { "title": "...", "domain": "...", "hypothesis": "...", "null_hypothesis": "...", "intervention": "...", "primary_outcome": "..." } } ``` For a daily check-in: 1. Capture compliance, 1 to 2 primary outcome scores, confounders, and a short note. 2. Call the `experiments` tool: ```json { "command": "checkin", "input_json": { "experiment_id": "<id>", "compliance": 0.9, "primary_outcome_scores": { "metric": 7 }, "confounders": [], "note": "..." } } ``` For experiment review, call the `experiments` tool: ```json { "command": "analyze", "experiment_id": "<id>" } ``` When the script says more data is needed: - explain exactly what is missing - ask the user to collect that data - tell them to return to `/insights` after enough data exists