freddy

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---
name: connect-a-source
description: Use when the user wants to connect a wearable, ring, CGM, training platform, or gym log to freddy, or asks why a newly connected source shows no data yet. Not for disconnecting or for querying data.
---

# Connect a source

This skill uses the freddy app's MCP tools (get_profile, list_metrics, query_metrics, connect_source, sync_source). Follow the workflow below.

Help me connect a new data source to freddy.

1. Call get_profile to see what is already connected.
2. If I haven't named a source, ask which one I want to connect.
3. Call connect_source with that provider. OAuth sources return a URL for me to open in a browser; API-key sources and phone sources (Apple Health, Health Connect) return instructions to finish on the freddy dashboard or phone app — relay those instructions to me clearly and never ask me to paste credentials into this chat.
4. Once I confirm I have authorized, data starts syncing automatically and the first backfill can take a few minutes. If nothing is showing after a couple of minutes, call sync_source to force a pull. If query_metrics says data is still hydrating, tell me it is on the way rather than concluding there is no data.

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---
name: long-term-trends
description: Use when the user asks how a metric recorded in their freddy account changed over months or years — history charts, progress since a date, seasonal patterns, year-over-year comparisons. Not for a single week (use weekly-health-review).
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# Long-term trends

This skill uses the freddy app's MCP tools (get_profile, list_metrics, query_metrics, connect_source, sync_source) to chart the user's own recorded wellness data over time. It is a factual presentation of historical numbers, not medical advice, diagnosis, or a clinical assessment.

Show how my freddy data changed over time.

1. Call list_metrics to find the exact metric names — never guess generic English names. Prefer daily-summary variants (names containing avg/min/max) over per-reading metrics.
2. For multi-month or multi-year windows, call query_metrics with granularity: "month" (or "week" for finer detail) and explicit start / end dates (YYYY-MM-DD). This returns avg/min/max/count buckets so years of data fit in one response.
3. If a response reports truncation or omitted buckets, follow its continuation hint — it names the exact start and end to pass next.
4. Present the numbers factually: the overall direction, notable changes with their dates, seasonal patterns if visible, and — when enough history exists — the most recent 3 months side by side with the same period a year earlier. State which aggregation level the numbers come from. Do not characterize the data as a health status and do not draw medical conclusions from it.

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---
name: weekly-health-review
description: Use when the user asks for a weekly summary or check-in of the sleep, heart, training, or body data recorded in their freddy account. Not for multi-month or multi-year questions (use long-term-trends) or single-metric lookups.
---

# Weekly health review

This skill uses the freddy app's MCP tools (get_profile, list_metrics, query_metrics, connect_source, sync_source) to present the user's own recorded wellness data back to them. It is a factual data summary, not medical advice, diagnosis, or a clinical assessment.

Summarize my week from my freddy data.

1. Call get_profile to see which sources are connected and whether a sync is running, then list_metrics once to learn the exact metric names available for my account.
2. Fetch the most recent 7 full days with explicit start / end dates (YYYY-MM-DD; end = yesterday, start = 6 days before that) for the areas that exist in my data: sleep (duration, stages), heart data (HRV, resting heart rate), training (workouts, load), and body measurements (weight) if present.
3. Fetch the 7 full days immediately before that window, again with explicit start / end dates, as the comparison baseline — two equal-length, non-overlapping weeks.
4. Present a factual side-by-side summary: how this week's numbers compare with the previous week, with the actual values. Call out gaps — days without data or a source that stopped syncing. Do not assess health status, offer diagnoses, or prescribe changes; if the user wants guidance beyond what their numbers show, suggest they consult a qualified professional.

Stay grounded in the data returned — no invented values, and no conclusions from metrics that were not fetched.