What is agri-cv-visualization?

A toolkit for creating charts and visual explanations for agricultural computer-vision research, such as models that analyse crop or field images.

Use it to create Grad-CAM heatmaps, confusion matrices, detection or segmentation result displays, training curves, and radar charts as 300-dpi PDF figures.

It removes the repetitive work of turning model results into consistent, paper-ready figures. It helps show errors, model attention, predictions, training progress, and model comparisons.

agri-cv-visualization — Skill for Claude Code and Codex by Jeffisgod

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 Jeffisgod/Agri-CV-Research --skill agri-cv-visualization

Clone the repo

git clone --depth 1 https://github.com/Jeffisgod/Agri-CV-Research

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/jeffisgod/agri-cv-research/agri-cv-visualization/github.svg)](https://agentmods.dev/skills/jeffisgod/agri-cv-research/agri-cv-visualization)

Your own site

<a href="https://agentmods.dev/skills/jeffisgod/agri-cv-research/agri-cv-visualization"><img src="https://agentmods.dev/badge/skills/jeffisgod/agri-cv-research/agri-cv-visualization/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/jeffisgod/agri-cv-research/agri-cv-visualization"><img src="https://agentmods.dev/badge/skills/jeffisgod/agri-cv-research/agri-cv-visualization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,362 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.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.01362
Opus 5.5	$0.00000	$0.00545
Sonnet 5.5	$0.00000	$0.00272
Haiku 4.5	$0.00000	$0.00136

Measured 2mo ago against content hash ae0a251ad1a3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agri-cv-visualization 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 2mo 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.

skills/agri-cv-visualization/SKILL.md · 181 lines

How it starts

The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agricultural CV Visualization

Description

Automatic generation of publication-quality figures commonly used in agricultural CV papers: Grad-CAM heatmaps, confusion matrices, detection result displays, training curves, and radar comparison charts.

When to Use

Need to generate paper-ready figures in PDF format at 300 dpi
Need to visualize classification errors via confusion matrices
Need to inspect model attention via Grad-CAM / Attention Map visualizations
Need to display detection/segmentation prediction results
Need to compare multiple models using radar charts
Need to summarize training dynamics with training/validation curves

Figure Standards

All generated figures follow the project conventions:

Output format: PDF
Resolution: 300 dpi
Font size: 12 by default
Suitable for direct insertion into papers

Core Components

GradCAMVisualizer(model, target_layer)
- generate(image, ...) → single heatmap figure
- generate_grid(dataset, num_per_class, classes) → multi-class grid figure
plot_confusion_matrix(y_true, y_pred, class_names, normalize, save_path)
plot_detection_results(images, predictions, gt, layout, save_path)
plot_training_curves(log_path, save_path)
plot_radar_comparison(models, metrics, save_path)
FigureGenerator(output_dir) for batch figure generation

Code Examples

Grad-CAM Visualization

from agri_cv_research.visualization import GradCAMVisualizer

viz = GradCAMVisualizer(model, target_layer="layer4")

fig = viz.generate(
    image_path="./test_images/tomato_blight.jpg",
    pred_class="Early Blight",
    confidence=0.94,
    overlay_alpha=0.5,
    save_path="./figures/gradcam_example.pdf"
)

fig_grid = viz.generate_grid(
    dataset=test_dataset,
    num_per_class=3,
    classes=["Early Blight", "Late Blight", "Healthy"],
    save_path="./figures/gradcam_grid.pdf"
)

Confusion Matrix

from agri_cv_research.visualization import plot_confusion_matrix

plot_confusion_matrix(
    y_true=labels,
    y_pred=predictions,
    class_names=dataset.class_names,
    normalize=True,
    figsize=(10, 8),
    cmap="Blues",
    font_size=8,
    save_path="./figures/confusion_matrix.pdf",
    dpi=300
)

Read the full file on GitHub · 181 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.

2mo ago First seen · 181 lines · 0 tokens per session scan A ae0a251ad1a3

Subscribe to this mod's changes

agri-cv-visualization is a skill published in the GitHub repository Jeffisgod/Agri-CV-Research (3 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,362 tokens. 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.

Analytics and metrics Scientific

agri-cv-visualization next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
agri-cv-visualizationJeffisgod/Agri-CV-Research	Skill	3	5mo ago	MIT	A	0	Claude Code, Codex
instrument-data-to-allotropeanthropics/knowledge-work-plugins	Skill	35k	3d ago	Apache-2.0	A	123	Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	83	Claude Code
matlabK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	44	Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	71	Claude Code, Codex
research-engineerdavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	43	Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmed	Skill	5.8k	yesterday	Apache-2.0	A	205	Claude Code