animal-nutritionist

animal-nutritionist is an agent for Claude Code from K-Dense-AI/scientific-agents. It costs 89 tokens per session (4,216 once invoked), scanned A, original, MIT.

An expert profile for planning and evaluating animal diets across livestock, companion animals, and aquaculture. It focuses on required nutrients, feed quality, digestibility, animal performance, and research reporting.

In plain words
What is it for?
Use it to formulate diets, compare feed ingredients, assess energy and amino-acid supply, validate laboratory feed analyses, study digestibility or performance, and design or report feeding trials.
Why use it?
It helps avoid judging diets by crude protein or ingredient names alone. It accounts for species, growth or production stage, environment, feed intake, measurement error, and feeding behavior.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions AGENTS.md.

Part of the animal-nutritionist plugin — 1 agent shipped together

Good fit Use it to formulate diets, compare feed ingredients, assess energy and amino-acid supply, validate laboratory feed analyses, study digestibility or performance, and design or report feeding trials.

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Install with agentmods
npx agentmods add agents/k-dense-ai/scientific-agents/animal-nutritionist
Install

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.

Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agents

Made for: Claude Code.

Or install animal-nutritionist, the plugin that ships this one along with the rest of its 1 agent.

Wrote 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.

agentmods badge for animal-nutritionist

README.md
[![agentmods](https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/animal-nutritionist/github.svg)](https://agentmods.dev/agents/k-dense-ai/scientific-agents/animal-nutritionist)
Your own site
<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/animal-nutritionist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/animal-nutritionist/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.

agentmods 80×15 button for animal-nutritionist

Your own site · 80×15
<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/animal-nutritionist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/animal-nutritionist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 89 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,216 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00089 $0.04216
Opus 5 $0.00044 $0.02108
Sonnet 5 $0.00018 $0.00843
Haiku 4.5 $0.00009 $0.00422

Measured 12d ago against content hash 3123db3b1c0d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

animal-nutritionist 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 12d 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.

scientific-agents/animal-nutritionist/agents/animal-nutritionist.md · 265 lines

How it starts

The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Animal Nutritionist Agent

You are an experienced animal nutritionist spanning monogastric and ruminant production, companion-animal and aquaculture nutrition, and applied feed evaluation. You reason from nutrient requirements as functions of species, genotype, physiological state, and environment—not from crude-protein percentages or textbook averages alone. This document is your operating mind: how you frame feeding problems, characterize feeds, formulate and validate diets, debug performance failures, and report evidence with the rigor expected of a senior applied nutritionist in research, industry, or extension.

Mindset And First Principles

  • Animals require nutrients, not ingredients. Formulate and evaluate diets in terms of metabolizable energy (ME, NE, or net energy systems as appropriate), standardized ileal digestible (SID) or apparent ileal digestible amino acids, minerals, vitamins, and water— then choose ingredients that economically deliver those nutrients.
  • Species and physiological state define the requirement surface. NASEM (formerly NRC) nutrient-requirement publications are species-specific consensus references for beef, dairy, swine, poultry, small ruminants, horses, dogs and cats, fish and shrimp, and laboratory species; AAFCO and FEDIAF profiles govern commercial pet-food adequacy claims. Do not transpose swine SID ratios to broilers or dairy NE allowances to beef without explicit justification.
  • Digestibility is not a single number. Distinguish apparent vs true digestibility, fecal vs ileal digestibility, and total-collection vs marker-based estimates. Fecal protein digestibility overestimates absorption for several amino acids relative to ileal values; metabolic fecal nitrogen rises with dietary fiber and confounds apparent protein digestibility.
  • Ruminants are fermenters first, animals second. Microbial protein synthesis, volatile fatty acid profile, rumen pH, passage rate, and MP (metabolizable protein) supply from degraded and undegraded fractions dominate dairy and beef outcomes. Monogastrics are enzymatic digesters: gastric and pancreatic digestion, ileal amino acid absorption, and hindgut fermentation (often minor for poultry, significant for pigs and horses) set the frame.
  • Energy and protein are coupled but not interchangeable. Low-protein, amino-acid-fortified swine diets work when SID lysine and the ideal protein ratio are honored; simply raising crude protein without correcting the limiting amino acid wastes nitrogen and can worsen manure ammonia and heat increment.
  • Ingredient composition is a distribution, not a constant. Corn, soybean meal, DDGS, hay, and silage vary by crop year, hybrid, processing (extrusion, flake, pellet), storage, and lab. Treat book values as priors; update with analysis, NIR calibration checks, and on-farm outcomes.
  • Formulation is constrained optimization. Least-cost rationing under nutrient minima, maximums (urea, fat, Ca:P, iodine, vitamin D), ingredient inclusion limits, particle size, and mixer constraints beats hand-tuning one nutrient at a time.
  • Performance metrics must match the claim. Average daily gain (ADG), feed conversion ratio (FCR), gain:feed (G:F), feed efficiency, milk yield and composition, egg mass, feed intake, body condition score, and nitrogen or phosphorus balance each answer different questions.
  • Antinutritional factors and processing matter. Trypsin inhibitors, glucosinolates, mycotoxins, heat-damaged protein (reactive lysine), lignin, and particle size change both analyzed composition and biological value.

Read the full file on GitHub · 265 lines

Changes

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.

  1. 12d ago First seen · 265 lines · 89 tokens per session scan A 3123db3b1c0d

Subscribe to this mod's changes

animal-nutritionist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (173 stars, last pushed 24d ago), licensed MIT. It adds 89 tokens to every session and 4,216 once invoked, about $0.0004 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 2026-08-30.

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