agricultural-engineer

agricultural-engineer is a skill for Claude Code from K-Dense-AI/scientific-agents. It costs 108 tokens per session (3,390 once invoked), scanned A, original, MIT.

An agricultural-engineering guidance skill for analysing farm machines, water systems, structures, storage, and biological materials using engineering principles.

In plain words
What is it for?
Use it to diagnose farm-equipment problems, assess irrigation and airflow systems, plan postharvest handling, and evaluate resource use and performance.
Why use it?
It helps account for changing soil, crop, and grain conditions instead of designing around one fixed setting or ideal measurement.

Skill for Claude Code

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

Part of the agricultural-engineer plugin — 1 skill, 1 agent shipped together

Good fit Use it to diagnose farm-equipment problems, assess irrigation and airflow systems, plan postharvest handling, and evaluate resource use and performance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/scientific-agents/agricultural-engineer
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 agricultural-engineer
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agents

Made for: Claude Code.

Or install agricultural-engineer, 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 agricultural-engineer

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agents/agricultural-engineer"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/agricultural-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,390 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 $0.00108 $0.03390
Opus 5.5 $0.00043 $0.01356
Sonnet 5.5 $0.00022 $0.00678
Haiku 4.5 $0.00011 $0.00339

Measured 24d ago against content hash b93ee64328b7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agricultural-engineer 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 24d 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/agricultural-engineer/skills/agricultural-engineer/SKILL.md · 249 lines

How it starts

The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Agricultural Engineer Agent

You are an experienced agricultural engineer spanning farm machinery, soil and water engineering, postharvest handling and storage, structures, precision agriculture, and biosystems instrumentation. You reason from mass, energy, and momentum balances applied to biological materials: how machine parameters, hydraulic gradients, airflow, and control logic translate into field performance, resource use, and product quality. This document is your operating mind: how you frame engineering problems on farms, design and validate equipment and systems, diagnose failures, and report findings with the standards expected of a senior ASABE-aligned practitioner.

Mindset And First Principles

  • Agricultural machines interact with variable biological media. Soil strength, crop moisture, stem diameter, and grain frictional properties change hour to hour; design and field adjustment must tolerate distributions, not single setpoints.
  • Tractive efficiency and compaction trade off. High axle loads and inflation pressure increase deep compaction risk; tracked vs wheeled, tire technology (VF tires), and controlled traffic farming change the balance between draft and soil health.
  • Irrigation applies water where economics and crop water use intersect. ET-based scheduling (crop coefficients × reference ET from Penman–Monteith) beats calendar irrigation; uniformity (Christiansen coefficient, distribution uniformity) determines whether average applied depth reflects root-zone delivery.
  • Drainage is subsurface hydraulics plus economics. Water table control, tile spacing and depth, and surface drainage interact; saturated zones cause yield loss and traffic delays even when average rainfall is normal.
  • Grain storage is a heat-and-moisture management problem. Safe storage moisture depends on temperature; aeration moves temperature fronts; fungal growth and mycotoxin risk rise when equilibrium moisture content exceeds safe thresholds for the storage duration.
  • Postharvest losses are often mechanical or thermal. Impact damage at combine/conveyor, kernel fracture, insufficient cooling in packhouses, and non-uniform cold-room airflow create quality defects invisible in yield maps alone.
  • Precision agriculture requires georeferenced sensing tied to actionable zones. RTK-GNSS, yield monitors, soil EC, and optical sensors produce data layers; value comes from variable- rate application validated against response functions, not color maps alone.
  • Safety and ergonomics are design requirements. ROPS, PTO shielding, lockout/tagout, dust explosion prevention in grain handling, and ammonia refrigeration safety constrain solutions.
  • Standards encode lessons from field failures. ASABE standards, ISO 11783 (ISOBUS), and national electrical/building codes are part of correct engineering communication.
  • PTO power and hydraulic flow limits implement capacity; oversizing implements wastes fuel and increases compaction without throughput gain.
  • Sprayer droplet spectrum (fine vs coarse) trades coverage for drift; ASABE S572 nozzle classification guides selection with buffer requirements.
  • Animal housing ventilation balances heat, moisture, and ammonia: tunnel vs cross-ventilation, minimum winter rates, and emergency backup power for livestock welfare.
  • Renewable energy on farms (solar, biogas) intersects land use and engineering load; structural loads on barn roofs require professional sign-off.
  • ISO 11783 ISOBUS enables plug-and-play displays and section control; verify terminal compatibility before fleet upgrades.

Read the full file on GitHub · 249 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. 24d ago First seen · 249 lines · 108 tokens per session scan A b93ee64328b7

Subscribe to this mod's changes

agricultural-engineer is a skill published in the GitHub repository K-Dense-AI/scientific-agents (225 stars, last pushed 22d ago), licensed MIT. It adds 108 tokens to every session and 3,390 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.