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.
npx skills add Jeffisgod/Agri-CV-Research --skill agri-cv-visualizationgit clone --depth 1 https://github.com/Jeffisgod/Agri-CV-ResearchWrote 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.
[](https://agentmods.dev/skills/jeffisgod/agri-cv-research/agri-cv-visualization)<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.
<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>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 | $0.00000 | $0.00681 |
| Sonnet 5 | $0.00000 | $0.00272 |
| Haiku 4.5 | $0.00000 | $0.00136 |
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 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.
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 figuregenerate_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
)
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.
- 12d ago First seen · 181 lines · 0 tokens per session scan A ae0a251ad1a3
agri-cv-visualization is a skill published in the GitHub repository Jeffisgod/Agri-CV-Research (23 stars, last pushed 3mo 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.
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