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Grok Bot+18 agentsFood Photo Nutrition Coach
Turn meal images into practical nutrition guidance with visible-food identification, uncertainty checks and goal-aware improvements.
$9.99
Food Photo Nutrition Coach
Example session with this skill installed
Fictional example: An adult describes a photo of rice, chicken, mixed vegetables and sauce after a workout. Portion weights and sauce ingredients are unknown; goal is balanced recovery. Produce a complete first-pass deliverable, separate facts from assumptions, and give practical next steps. Do not take external actions.
- Read your context and instructions
- Compiled the food photo nutrition
Food Photo Nutrition Coach: worked example
Scope: the fictional brief below, using only supplied information.
An adult describes a photo of rice, chicken, mixed vegetables and sauce after a workout. Portion weights and sauce ingredients are unknown; goal is balanced recovery.
Deliverable
| Workstream | Finding / decision | Recommended action |
|---|---|---|
| Meal structure | Rice, chicken and vegetables are described, not independently image-verified | Treat rice as a likely carbohydrate source, chicken as protein and vegetables as produce; ask for the actual image. |
| Precision | Weights, cooking oil and sauce composition are unknown | Avoid calorie or macro totals; request weights or label information before numerical estimates. |
| Practical improvement | The described plate already includes multiple food roles | Keep the basic structure; use appetite and training context to adjust portions and verify sauce allergens separately. |
Decision and next steps
Obtain the image and confirm foods before a visual assessment. Ask about portion size and dietary restrictions; offer one manageable adjustment instead of judging a single meal.
Evidence boundaries
These conclusions apply to the supplied scenario. Missing facts remain unverified; proposed changes are recommendations, not completed actions. Validate source data and approvals before implementation. Track the result against the stated objective and adjust only when new evidence supports a change.
Connects securely to your tools. The creator never sees your data.
About this skill
Food Photo Nutrition Coach
Turn meal images into practical nutrition guidance with visible-food identification, uncertainty checks and goal-aware improvements.
What the workflow covers
- Treat rice as a likely carbohydrate source, chicken as protein and vegetables as produce; ask for the actual image.
- Avoid calorie or macro totals; request weights or label information before numerical estimates.
- Keep the basic structure; use appetite and training context to adjust portions and verify sauce allergens separately.
How to use it
Provide your goal, relevant source material, current constraints and the details needed for food photo nutrition coach. Include dates, context and any existing decisions that must be preserved.
What you receive
A structured deliverable with supplied facts, clear assumptions, prioritized actions, open questions and next steps. The depth adapts to your goal and available evidence. The example below is a fictional demonstration.
Images cannot establish exact calories, ingredients, allergens, food safety or diagnoses. Adult educational nutrition guidance; does not replace individualized clinical care. Requires image-capable host for photo interpretation.
This package contains reusable agent instructions. Tool access depends on your AI host. External actions require supported tools and explicit authorization.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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