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 HolobiomicsLab/asb-skill-collections --skill principal-component-analysis-visualizationgit clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-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/holobiomicslab/asb-skill-collections/principal-component-analysis-visualization)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/principal-component-analysis-visualization"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/principal-component-analysis-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/holobiomicslab/asb-skill-collections/principal-component-analysis-visualization"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/principal-component-analysis-visualization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.01303 |
| Opus 5 | $0.00030 | $0.00651 |
| Sonnet 5 | $0.00012 | $0.00261 |
| Haiku 4.5 | $0.00006 | $0.00130 |
Grade A, and why
principal-component-analysis-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 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
principal-component-analysis-visualization
Summary
Apply PCA to methylation profiles from a unified methylBase object to reveal sample relationships and variance structure in principal component space. Generates a scree plot showing variance explained by each PC and a biplot of the first two principal components to assess methylation-based sample grouping.
When to use
After merging methylation call files from multiple samples using unite() to create a methylBase object, apply PCA when you need to visualize sample-level relationships based on overall methylation similarity across all covered bases, or when you want to determine which principal components explain the most variance in methylation profiles among your samples.
When NOT to use
- Input is a raw methylRawList (unmunged per-sample files) — must first run unite() to create methylBase.
- You need to perform clustering by correlation distance with Ward linkage — use clusterSamples() instead, which produces a dendrogram.
- Sample count is very small (n < 3) — PCA is unstable with fewer samples than dimensions.
Inputs
- methylBase object (unified methylation data from unite() across all samples and bases)
- sample metadata (optional, for labeling and coloring scatter plot points)
Outputs
- scree plot (variance explained by each principal component)
- PC1 vs PC2 scatter plot (sample coordinates in first two principal components)
- principal component scores matrix (numeric coordinates for all samples and PCs)
How to apply
Load the methylBase object produced by unite() from merged methylation calls. Apply the PCASamples() function from methylKit to compute principal components of the methylation profile matrix. Extract the scree plot showing variance explained by each PC to determine the number of informative components. Then visualize the first two principal components (PC1 and PC2) as a scatter plot with samples as points, colored or labeled by experimental group (e.g., test1, test2, ctrl1, ctrl2). The function performs centering and scaling on the methylation data before eigenvalue decomposition. Interpret clustering patterns in PC space: samples that cluster together have similar methylation profiles, while separation along PC1 and PC2 indicates major sources of methylation variation between experimental groups.
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 · 96 lines · 59 tokens per session scan A 5267902c4275
principal-component-analysis-visualization is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 2d ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,303 once invoked, about $0.0003 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 2026-09-06.
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