Food Science

Community

beita6969

food-science

Analyzes food science topics including nutritional composition, food chemistry, food safety hazard analysis, sensory evaluation design, and food processing optimization; trigger when users discuss nutrients, food additives, HACCP, shelf life, or food product development.

Overview

Publisher	beita6969
Repository	`ScienceClaw`
Skill name	`food-science`
Stars	950
Forks	106
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by beita6969 on GitHub. Read the source before you install it.

Installation

Install the Food Science AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/beita6969/ScienceClaw/tree/main/skills/food-science
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/food-science .claude/skills/food-science

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Food Science in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Food Science on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Food Science is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

When to Trigger

Activate this skill when the user mentions:

Nutritional analysis, macronutrients, micronutrients, dietary reference intakes
Food chemistry, Maillard reaction, emulsification, gelation
Food safety, HACCP, critical control points, pathogen analysis
Sensory evaluation, taste panels, hedonic scales
Food processing, pasteurization, fermentation, preservation
Shelf life, water activity, food packaging
Dietary assessment, food frequency questionnaire, 24-hour recall

Step-by-Step Methodology

Define the food science question - Specify the food matrix (raw ingredient, processed product, meal). Identify whether the question is about composition, safety, processing, sensory properties, or health effects.
Nutritional analysis - Query food composition databases (USDA FoodData Central, EFSA). Report per serving and per 100g. Compare against DRIs (Dietary Reference Intakes) or RDAs. Account for bioavailability and cooking losses.
Food chemistry analysis - Identify key chemical reactions (Maillard browning, lipid oxidation, enzymatic browning, starch gelatinization). Characterize relevant physical chemistry (pH, water activity, emulsion stability, rheology). Relate to quality attributes (color, texture, flavor).
Food safety assessment - Identify hazards: biological (pathogens: Salmonella, Listeria, E. coli O157:H7), chemical (pesticides, mycotoxins, heavy metals, allergens), physical (foreign objects). Apply HACCP principles: hazard analysis, critical control points, critical limits, monitoring, corrective actions.
Process optimization - Define processing parameters (temperature, time, pH, pressure). Model thermal processing (D-value, z-value, F0 calculations for sterilization). Optimize for safety while minimizing quality loss. Consider novel technologies (HPP, PEF, UV).
Sensory evaluation - Design appropriate test: discrimination (triangle, duo-trio), descriptive (QDA, CATA), or affective (hedonic, preference). Determine panel size (trained vs. consumer), number of replicates, and serving conditions. Apply appropriate statistical analysis.
Shelf life estimation - Monitor quality indicators over time (microbial counts, chemical markers, sensory scores). Model degradation kinetics (zero or first order). Apply accelerated shelf life testing (ASLT) with Arrhenius equation for temperature-dependent reactions.

Key Databases and Tools

USDA FoodData Central - US food composition database
EFSA / Codex Alimentarius - Food safety standards
FDA Food Safety - US food regulations
CompTox (EPA) - Chemical toxicity data
Mintel / Innova Market Insights - Food product trends

Output Format

Nutritional composition tables: nutrient, amount per serving, % DRI.
HACCP plan as a table: hazard, CCP, critical limit, monitoring, corrective action.
Thermal processing: D-values, z-values, F0 calculation with target organism.
Sensory results: panel demographics, test statistics, significance levels.
Shelf life: degradation curves, estimated shelf life with confidence intervals.

Quality Checklist

Food composition data source and version specified
Serving sizes clearly defined and consistent
HACCP analysis covers all three hazard categories (biological, chemical, physical)
Thermal processing targets the most resistant pathogen of concern
Sensory evaluation design includes proper controls and blinding
Regulatory framework identified (FDA, EFSA, Codex)
Allergen considerations addressed
Shelf life conditions (temperature, humidity, packaging) specified

Frequently asked questions

What does the Food Science AI skill do?

Why use Food Science on TypingMind?

Because you install it once and use it with any model. Food Science is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Food Science in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/food-science. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Food Science?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Food Science?

As many as you like. As long as a model supports skills, you can use Food Science with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Food Science AI skill free?

Yes. It is published on GitHub by beita6969 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Trigger

Step-by-Step Methodology

Key Databases and Tools

Output Format

Quality Checklist

Frequently asked questions

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