Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add beita6969/ScienceClaw --skill food-sciencegit clone --depth 1 https://github.com/beita6969/ScienceClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beita6969/scienceclaw/food-science)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/food-science"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/food-science/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beita6969/scienceclaw/food-science"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/food-science.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.00837 |
| Opus 5 | $0.00025 | $0.00418 |
| Sonnet 5 | $0.00010 | $0.00167 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade A, and why
food-science scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 53 lines · 50 tokens per session scan A 812b742ebf61
food-science is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 837 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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