body-diet

name	body-diet
description	Provide specialist nutrition evidence from Yazio when `body-data-qa` or `body-cadence-review` needs diet-domain depth, or when the user explicitly asks for a nutrition-only deep dive.
compatibility	[{"tool":"mcp__plugin_body_yazio__get_user_daily_summary"},{"tool":"mcp__plugin_body_yazio__get_user_consumed_items"},{"tool":"mcp__plugin_body_yazio__get_user_water_intake"}]

Body - Diet

Analyze nutrition data from Yazio and assess adherence to calorie, macro, and hydration goals. Use this as the diet specialist inside the body system. The default top-level entrypoint for normal questions is body-data-qa. This skill should mostly support the workflow skills unless the user explicitly wants diet-only depth.

MCP Server

yazio-mcp — pull user goals, daily summary, consumed items, water intake, exercises, and weight entries.

Goals

Use the same reasoning order as the workflow skills:

1. Read the guiding principles and strategy in 000 OS/.
2. Check the numerical targets in 3 Numerical Targets 2026.
3. Read the body area guidance in 300 Areas/Body/: protocols (0 Intro to body protocols), beliefs (Body beliefs), and maintenance systems (Body maintenance systems).
4. Then compare nutrition behavior against diet and body composition goals.

Analysis

• Pull today or the requested date from yazio-mcp.
• For direct diet-only trend questions, pull the last 7-14 days, or 30 days when the question is broader. For a cadence review spanning a full month, sample at least 8-10 dates, not 4-5 — a sparse sample has previously made intermittent-but-real logging look like a complete stop when a denser re-check showed otherwise.
• When invoked from body-cadence-review, use the exact review and comparison windows provided by the caller, such as this week vs last week or this month vs last month.
• Do not replace cadence-review comparisons with a rolling recent window unless that rolling context is explicitly labeled as supplemental.
• steps and activity_energy in get_user_daily_summary sync automatically from a connected health app, independent of food logging — a day with real steps can still have zero meals logged. Don't infer "day was tracked" from steps/activity_energy alone; check the meals block itself. get_user_exercises is typically empty (Garmin is the real exercise log, not Yazio) — don't expect a separate workout history here.
• get_user (profile) is worth a one-time pull for context: registration date explains any "zero data" period before the account existed, and self-reported activity_degree feeds the calorie-goal calculation shown in every daily summary.
• Calculate adherence against goals:
  • energy_kcal consumed vs target
  • protein_g, carb_g, fat_g consumed vs targets
  • water_ml consumed vs target
• Analyze meal distribution across breakfast, lunch, dinner, and snacks.
• Flag high day-to-day calorie variance and repeated macro under-target patterns.
• Track logging consistency using consumed-item presence per day.
• If an explicit streak is unavailable, derive a streak proxy from consecutive logged days.
• Mark inferred metrics with lower confidence than direct MCP fields.
• Prioritize protein and consistency adherence for recomposition-oriented goals.
• Cross-reference with body-recovery and body-composition when training or physique context is relevant.

Output Contract

If structured output is needed, keep the metric payload aligned with ../../schemas/diet.json. Present the findings in prose under this shape:

• Current intake snapshot - the latest daily totals against targets
• Trend - adherence and variance over the active window
• Behavior pattern - meal timing, logging consistency, or repeated misses
• Goal alignment - whether intake behavior supports the active body goal
• Caveats - inferred streaks, missing logging, or weak hydration coverage

Escalation Rules

Stay in body-diet when the user explicitly wants diet-only depth or when an upstream workflow already scoped the task to nutrition behavior and adherence.

Escalate to:

• body-composition when the core question is physique change rather than intake behavior
• body-recovery when the core decision is fueling versus recovery capacity
• body-data-qa for ad-hoc cross-domain comparisons
• body-cadence-review for weekly, monthly, quarterly, or yearly reviews

Resources

Only search 400 Resources/ when this specialist is being used directly for a nutrition-only deep dive. When invoked from body-data-qa or body-cadence-review, let the upstream workflow decide whether resource-backed recommendations are needed.

If a direct deep dive needs resource support:

1. Use find and grep to locate files related to the current query
2. Read .md and .txt files directly
3. For PDFs, extract text and scan for relevant sections
4. For Excel files, read and parse relevant sheets
5. Prioritize newer files over older ones
6. Cite which resource informed your recommendation

Search broadly — file names may not be descriptive. Look at actual content.

Tone

Quant analyst reviewing a dashboard. Numbers first, brief context, no fluff.

Schema

Reference ../../schemas/diet.json for field definitions.