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 CHENyiru3/AI-Skills-Collections --skill vitesscegit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote 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/chenyiru3/ai-skills-collections/vitessce)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/vitessce"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/vitessce/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/chenyiru3/ai-skills-collections/vitessce"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/vitessce.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 | $0.00032 | $0.00537 |
| Opus 5.5 | $0.00013 | $0.00215 |
| Sonnet 5.5 | $0.00006 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
vitessce 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 6d 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.
What it actually says
Vitessce: Visual Integration Tool
Overview
Vitessce is a web-based visualization framework for exploring multi-modal and spatial single-cell data. It enables the creation of interactive dashboards combining gene expression, protein, spatial, and image data.
When to Use This Skill
This skill should be used when:
- Creating interactive dashboards for spatial omics
- Visualizing multi-modal single-cell data
- Building data portals for single-cell data
- Sharing visualizations with collaborators
- Combining different data types in one view
Quick Start
Installation
# Install Vitessce Python package
pip install vitessce
# Create a Vitessce config
vitessce --init
Creating a Dashboard
from vitessce import (
VitessceConfig,
ViewConf,
FileType,
DataType,
)
# Create configuration
vc = VitessceConfig()
# Add dataset
dataset = vc.add_dataset("My Dataset")
# Add files
dataset.add_file(
url="https://example.com/expression.h5ad",
file_type=FileType.H5AD,
data_type=DataType.OBS_COUNT_MATRIX,
)
# Add view
scatterplot = vc.add_view(dataset, ViewConf.SCATTERPLOT, x=0, y=0, w=6, h=8)
# Export
vc.export_plugins()
vc.export("config.json")
Configuration
Views
- Scatterplot: 2D embeddings (UMAP, t-SNE)
- Spatial: Spatial gene expression
- Heatmap: Gene expression matrices
- CellSets: Cell type annotations
- Genes: Gene lists
Data Types
- Expression matrices (H5AD, Zarr)
- Cell segmentations
- Image stores (OME-TIFF, Zarr)
Best Practices
- Prepare data in Python/R: Use scanpy/Seurat first
- Use appropriate formats: H5AD for expression, OME-TIFF for images
- Test locally: Verify before deployment
Additional Resources
- Documentation: https://vitessce.io/
- GitHub: https://github.com/vitessce/vitessce
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.
- 6d ago First seen · 94 lines · 32 tokens per session scan A 7476981dda3e
vitessce is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 32 tokens to every session and 537 once invoked, about $0.0001 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 2026-10-02.
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