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 schardgit 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/schard)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/schard"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/schard/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/schard"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/schard.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 | $0.00073 | $0.01049 |
| Opus 5.5 | $0.00029 | $0.00420 |
| Sonnet 5.5 | $0.00015 | $0.00210 |
| Haiku 4.5 | $0.00007 | $0.00105 |
Grade A, and why
schard 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.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
schard - h5ad to R Converter
The schard R package converts Python scanpy h5ad files to Seurat or SingleCellExperiment objects for analysis in R.
Installation
# Install from GitHub
remotes::install_github("cellgeni/schard")
Downloading Public h5ad Files
From CZI cellxgene
download.file('https://datasets.cellxgene.cziscience.com/c5ac5c36-f60c-4680-8018-2d6cb65c0a37.h5ad', 'vis.heart.h5ad')
download.file('https://datasets.cellxgene.cziscience.com/8cc521c8-c4ff-4cba-a07b-cae67a9dcba9.h5ad', 'sn.heart.h5ad')
From Sanger Atlases
download.file('https://covid19.cog.sanger.ac.uk/baron16.processed.h5ad', 'ba16.h5ad')
Loading h5ad Files
As SingleCellExperiment
ba16.sce = schard::h5ad2sce('ba16.h5ad')
As Seurat Object
# Standard Seurat object
snhx = schard::h5ad2seurat('sn.heart.h5ad')
# Load raw counts instead of normalized data
snhr = schard::h5ad2seurat('sn.heart.h5ad', use.raw = TRUE)
Visium Spatial Data
# Load all Visium samples as single Seurat object
visx = schard::h5ad2seurat_spatial('vis.heart.h5ad')
# Load as list of Seurat objects (one per slide)
visl = schard::h5ad2seurat_spatial('vis.heart.h5ad', simplify = FALSE)
# Raw counts for Visium
visr = schard::h5ad2seurat_spatial('vis.heart.h5ad', use.raw = TRUE)
Working with Spatial Plots
# Plot total counts on tissue
Seurat::SpatialPlot(visx, features = 'total_counts')
# Plot specific sample
Seurat::SpatialPlot(visx, features = 'total_counts', images = 'HCAHeartST11702009')
# Plot from list (per-slide object)
Seurat::SpatialPlot(visl$HCAHeartST11702010, features = 'total_counts')
# Compare normalized vs raw counts
plot(colSums(visx), colSums(visr), pch = 16)
# Raw counts are different from normalized ones
Working with Dimensionality Reductions
# DimPlot works but specify reduction manually for safety
Seurat::DimPlot(snhx, group.by = 'cell_state')
# Note: reduction name is 'Xumap_' (auto-translated from scanpy to Seurat)
# Safer to specify: reduction = 'Xumap_'
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 · 128 lines · 73 tokens per session scan A c2a1c09acb84
schard is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 73 tokens to every session and 1,049 once invoked, about $0.0003 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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