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Nutrition Score Optimizer

Move your front-of-pack nutrition score without losing the taste.

What is the Nutrition Score Optimizer?

The Nutrition Score Optimizer is a free AI skill that plans how to improve a product's front-of-pack nutrition rating — Nutri-Score, Health Star Rating, or traffic-light labels — for food and beverage R&D and innovation teams. You give it the product, its current nutrition panel, and the score you are targeting; it returns the levers most likely to move the rating across sugar, sodium, saturated fat, fiber, protein, and fruit or vegetable content, a ranking of each lever by taste and texture risk, the claims and merchandising opportunities that unlock at each threshold, and a stepwise reformulation path that sequences safe moves before risky ones. It is built for teams whose retailers, regulators, or brand leads are pushing for a better score. Because scoring algorithms are threshold-based, the skill focuses effort on changes that actually cross a line rather than diffuse healthier tinkering. Pairing it with live food and beverage demand data shows which health cues consumers in your category actually reward.

Who it's for

  • R&D teams tasked with hitting a Nutri-Score target
  • Innovation managers planning better-for-you renovation
  • Brand teams needing claim unlocks for a redesign
  • Nutrition and regulatory leads sequencing reformulation steps

What you get

  • A score diagnosis: what is holding the rating down
  • Score levers ranked by likely impact on your rating
  • A taste and texture risk rating per lever
  • Claim and merchandising unlocks at the target score
  • A stepwise reformulation path with re-score checkpoints
  • A verify-before-acting list for regulatory and nutrition teams

How to use it

  1. 1Copy the prompt into ChatGPT, Claude or Google Gemini.
  2. 2Fill the bracketed inputs (product, nutrition panel, scoring system, target).
  3. 3Answer its clarifying questions about non-negotiables and constraints.
  4. 4Iterate: ask for a version that prioritizes zero taste risk.

See it in action

Example input

Product: honey-oat breakfast cereal from fictional 'Morningful', currently Nutri-Score C in France and Belgium. Target: B without losing the honey taste that drives repeat. Panel pressure points: sugar and fiber. Brand policy: no artificial sweeteners.

Example output

Score diagnosis for Morningful: sugar content is the anchor holding the score at C; fiber is the under-used favorable lever. Verify against the current Nutri-Score algorithm with your regulatory team before acting.

Levers ranked by score impact:
1. Sugar reduction — the dominant lever; stepwise cuts matter most near threshold points.
2. Fiber increase — raising whole-oat share and adding oat bran lifts the favorable side.
3. Sodium trim — small contribution, low effort.
4. Saturated fat — already low; little to gain.

Taste-risk ranking:
- Low risk: oat bran addition (slight color deepening), sodium trim.
- Medium risk: a staged sugar cut in two moves, holding honey aroma constant.
- High risk: sweetener substitution — excluded by brand policy anyway.

Unlocks at B: stronger signaling at retailers that badge Nutri-Score, eligibility for better-for-you range reviews, and school and workplace channel conversations.

Stepwise path: 1) fiber lift plus sodium trim, re-score; 2) sugar cut step one, bench panel; 3) sugar cut step two, re-score and lock the B before touching anything else.

Want me to draft the bench panel plan for the sugar steps?

The prompt

Here's the start of the prompt. Download the free bundle for the full, ready-to-paste version — plus the installable Claude Skill and Custom-GPT instructions.

# Role
You are a senior nutrition and reformulation strategist for food and beverage companies who knows how threshold-based front-of-pack systems behave — Nutri-Score, Health Star Rating, UK traffic lights. You focus effort on changes that cross a scoring line, and you never trade away the taste that drives repeat purchase.

# Context I'll provide
- Product and category: [PRODUCT]
- Scoring system and market: [SCORING SYSTEM e.g. Nutri-Score in France, HSR in Australia]
- Current nutrition panel: [NUTRITION PANEL — per 100g or 100ml values you have]
- Current and target score: [CURRENT SCORE] to [TARGET SCORE]
- Taste and brand non-negotiables: [NON-NEGOTIABLES e.g. the honey taste, no artificial sweeteners]
- Constraints (optional): [CONSTRAINTS e.g. cost ceiling, ingredient policy, timeline]

# Your task

Frequently asked questions

What is a front-of-pack nutrition score?
Front-of-pack nutrition scores — Nutri-Score in much of Europe, Health Star Rating in Australia and New Zealand, traffic-light labels in the UK — grade a product's overall nutrition on the pack face using an algorithm over nutrients like sugar, sodium, saturated fat, fiber, and protein. Because they are threshold-based, small formulation changes near a cutoff can move the visible grade.
Does it calculate my exact Nutri-Score?
No, and treat any tool that promises to as a starting point only. The skill identifies which levers plausibly move your score and sequences them — but official algorithms are updated over time, so every projected change must be verified against the current published algorithm by your regulatory or nutrition team before you commit to reformulation or on-pack changes.
Which scoring systems does it cover?
Nutri-Score, Health Star Rating, and UK-style traffic lights are handled natively, and the approach adapts to other threshold-based systems if you describe the scheme and market in the prompt. Name the market explicitly — the same recipe can score differently across systems, and the claim rules attached to each score differ by jurisdiction.
Which AI chat tools support this skill?
All the major ones — the prompt runs identically in ChatGPT, Claude, and Google Gemini, and it is model-agnostic by design. Teams working across a portfolio often save it as a Custom GPT or a Claude Skill and run each SKU through it, building a consistent renovation queue ranked by score impact and taste risk.

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