rb209-nutrient-planning

rb209-nutrient-planning is a skill for Claude Code, Codex from charles-gentry/rb209-mcp. It costs 91 tokens per session (6,422 once invoked), scanned A, original, MIT.

A workflow for calculating AHDB RB209 nutrient recommendations for a UK field or crop. AHDB RB209 is a UK guide for deciding fertiliser and related nutrient applications.

In plain words
What is it for?
Use it to gather the crop, soil, location, previous crop, manure, and soil-test information needed for recommendations covering nutrients such as nitrogen, phosphate, potash, magnesium, sulphur, sodium, and lime.
Why use it?
It prevents recommendations from being based on guessed field details or incorrect lookup identifiers.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to gather the crop, soil, location, previous crop, manure, and soil-test information needed for recommendations covering nutrients such as nitrogen, phosphate, potash, magnesium, sulphur, sodium, and lime.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/charles-gentry/rb209-mcp/rb209-nutrient-planning
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 charles-gentry/rb209-mcp --skill rb209-nutrient-planning
Clone the repo
git clone --depth 1 https://github.com/charles-gentry/rb209-mcp

Made for: Claude Code, Codex.

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 rb209-nutrient-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/charles-gentry/rb209-mcp/rb209-nutrient-planning/github.svg)](https://agentmods.dev/skills/charles-gentry/rb209-mcp/rb209-nutrient-planning)
Your own site
<a href="https://agentmods.dev/skills/charles-gentry/rb209-mcp/rb209-nutrient-planning"><img src="https://agentmods.dev/badge/skills/charles-gentry/rb209-mcp/rb209-nutrient-planning/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 rb209-nutrient-planning

Your own site · 80×15
<a href="https://agentmods.dev/skills/charles-gentry/rb209-mcp/rb209-nutrient-planning"><img src="https://agentmods.dev/badge/skills/charles-gentry/rb209-mcp/rb209-nutrient-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,422 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 $0.00091 $0.06422
Opus 5 $0.00046 $0.03211
Sonnet 5 $0.00018 $0.01284
Haiku 4.5 $0.00009 $0.00642

Measured 11d ago against content hash 0374d97c2220, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

rb209-nutrient-planning 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 11d 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.

skill/rb209-nutrient-planning/SKILL.md · 412 lines

How it starts

The opening of the file, as written. The whole thing — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RB209 Nutrient Planning

Produce a fertiliser recommendation for a UK field with the rb209_* MCP tools. The one calculation tool is rb209_recommendation_recommendations (a POST taking { body: <DataInput> }). Everything else is lookups that give you the integer IDs the DataInput needs.

Golden rules — read first

  1. Interview the user before calling anything. Do not invent field details. A recommendation depends on real facts about a real field (crop, soil, location, previous crop, manures, soil-test results). If you don't have one of these, ask the user a short, specific question — never guess a crop, a yield, or a soil type. See Phase 1.
  2. Resolve every ID from a lookup tool. IDs like cropTypeId, soilTypeId, swardTypeId are API-specific integers. Look them up; never hard-code them from memory. Show the user the options when a choice is theirs to make.
  3. Copy the shape of a known-good request. This repo has two real, accepted DataInput/response pairs — mirror the one matching your field type and only swap in the values you gathered:
    • Arable: test/fixtures/RecommendationsSampleInput.json (+ …Sample.json) — winter barley, England & Wales.
    • Grass: test/fixtures/RecommendationsGrassInput.json (+ …GrassSample.json) — first-cut silage plus grazing, England & Wales.
  4. If the API returns a validation error, read it — the field is named. The error tells you exactly what to fix. See Troubleshooting for the common ones and their fixes.
  5. Be efficient — a whole recommendation is usually ~6–10 tool calls, not 30. Avoid these common wasted calls:
    • One recommendation call returns all nutrients. Set every nutrient you want to true in the single nutrients object and call rb209_recommendation_recommendations once. Do not call it per nutrient or re-call it to "check" — N, P₂O₅, K₂O, MgO, SO₃ and lime all come back together.
    • Don't look fieldType up — it's fixed: 1 = Arable & Horticulture, 2 = Grassland, 3 = Both.
    • No soil analysis? Send "soilAnalyses": [] and skip all soil methodology/index lookups. The engine applies RB209 default indices; you do not need rb209_soil_methodologies…, rb209_soil_nutrient_index…, or any index tool. (Those T04 "not found" errors mean you're fetching indexes you don't need.)
    • Don't use rb209_recommendation_calculate_nutrient_offtake or nutrient target-index tools for a standard field recommendation — the main recommendation call already does that maths.

Read the full file on GitHub · 412 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. 11d ago First seen · 412 lines · 91 tokens per session scan A 0374d97c2220

Subscribe to this mod's changes

rb209-nutrient-planning is a skill published in the GitHub repository charles-gentry/rb209-mcp (2 stars, last pushed 22d ago), licensed MIT. It adds 91 tokens to every session and 6,422 once invoked, about $0.0005 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 2026-08-31.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens