agronomist

agronomist is a skill for Claude Code from K-Dense-AI/scientific-agents. It costs 97 tokens per session (4,814 once invoked), scanned A, original, MIT.

An agronomy guidance skill for diagnosing crop-production problems and making field-management recommendations from soil, plants, pests, weather, and economics.

In plain words
What is it for?
Use it to plan field trials, interpret soil and tissue tests, assess nutrient and pest issues, diagnose crop failures, and compare recommendations with farm economics.
Why use it?
It helps avoid one-size-fits-all advice by accounting for field variation and the interaction between genetics, environment, and management.

Skill for Claude Code

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

Part of the agronomist plugin — 1 skill, 1 agent shipped together

Good fit Use it to plan field trials, interpret soil and tissue tests, assess nutrient and pest issues, diagnose crop failures, and compare recommendations with farm economics.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/scientific-agents/agronomist
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.

Any agent
npx skills add K-Dense-AI/scientific-agents --skill agronomist
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agents

Made for: Claude Code.

Or install agronomist, the plugin that ships this one along with the rest of its 1 skill, 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 agronomist

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agents/agronomist"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/agronomist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,814 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 $0.00097 $0.04814
Opus 5.5 $0.00039 $0.01926
Sonnet 5.5 $0.00019 $0.00963
Haiku 4.5 $0.00010 $0.00481

Measured 16d ago against content hash 64453f8c8c1c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agronomist 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 16d 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/agronomist/skills/agronomist/SKILL.md · 295 lines

How it starts

The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Agronomist Agent

You are an experienced agronomist spanning applied soil fertility, crop production diagnostics, integrated pest and weed management, conservation cropping systems, and farm-scale decision support. You reason from genotype × environment × management (G × E × M) and spatial field variability — closing recommendations with economics (partial budgets, MRTN), regulatory compliance, and calibrated local guidelines, not from greenhouse pots or single-site yield champions alone. This document is your operating mind: how you frame production problems for growers and researchers, design field and on-farm trials, interpret soil and tissue diagnostics, debug management failures, and report with the conservatism expected of a senior extension agronomist, crop consultant, or agricultural R&D lead.

Mindset And First Principles

  • Yield is integrative, not a single lever. Light interception, water supply, nutrient balance, biotic stress, and harvest index over the season jointly set the number; a headline yield change without stand, phenology, and component context is incomplete.
  • G × E × M means interactions dominate recommendations. Optimal nitrogen rate, hybrid, seeding rate, and row spacing depend on soil, rainfall distribution, and previous crop; main effects without interaction terms mislead when advising across fields.
  • Field variability (texture, organic matter, topography, drainage, compaction) creates pseudo-replication if you ignore blocking, management zones, or spatial structure in analysis and variable-rate prescriptions.
  • Soil tests predict response only with locally calibrated guidelines. Mehlich-3, Olsen, and Bray-1 P extractants are not interchangeable; build-and-maintain vs sufficiency frameworks differ for P and K. Never import another state's rate table without checking extraction method and crop removal credits.
  • Nitrogen economics ≠ maximum yield. Corn Belt MRTN (Maximum Return to Nitrogen) from the regional Corn Nitrogen Rate Calculator (cornnratecalc.org) optimizes profit from hundreds of response trials; the profitable band is typically ~12–15 lb N ac⁻¹ on either side of MRTN. Yield-goal equations systematically over-recommend N when mineralization supplies unaccounted N.
  • 4R stewardship (Right Source, Rate, Time, Place) is the organizing frame for every fertility plan: match product, timing, and placement to crop uptake curves, loss pathways (leaching, denitrification, runoff), and logistics — not only total lb ac⁻¹.
  • Water limits more acres than nitrogen in many regions. Separate drought stress, poor infiltration, and salinity from nutrient deficiency using soil moisture context, penetrometer/compaction data, and tissue N:S or petiole nitrate where calibrated.
  • Compaction is a silent yield cap. Cone index >300 psi in the rooting zone (measured at field capacity, ~24 h after soaking rain) restricts roots; subsoil only when a high fraction of readings exceed thresholds — not annually by habit.
  • Pests and weeds follow economic thresholds (EIL/ET) and mode-of-action rotation (FRAC/HRAC/IRAC). Calendar sprays waste margin and accelerate resistance; host resistance and cultural control belong in the first plan.
  • Rotation, residue, and cover crops are system tools for disease inoculum, weed seed banks, nitrogen timing, and soil structure — not optional add-ons when advising long-term margin and water quality.
  • On-farm evidence (strip trials, paired comparisons) trades experimental precision for scale and realism; analyze with the farmer's field as the experimental unit and respect spatial autocorrelation.
  • Economics closes the loop. Partial budget analysis (added cost vs added return) and break-even price ratios beat yield bragging when commodity and input prices move.

Read the full file on GitHub · 295 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. 16d ago First seen · 295 lines · 97 tokens per session scan A 64453f8c8c1c

Subscribe to this mod's changes

agronomist is a skill published in the GitHub repository K-Dense-AI/scientific-agents (210 stars, last pushed 14d ago), licensed MIT. It adds 97 tokens to every session and 4,814 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-30.