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 GPTomics/bioSkills --skill dimensionality-reduction-plotsgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/dimensionality-reduction-plots)<a href="https://agentmods.dev/skills/gptomics/bioskills/dimensionality-reduction-plots"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/dimensionality-reduction-plots/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/gptomics/bioskills/dimensionality-reduction-plots"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/dimensionality-reduction-plots.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 | $0.00133 | $0.04972 |
| Opus 5 | $0.00067 | $0.02486 |
| Sonnet 5 | $0.00027 | $0.00994 |
| Haiku 4.5 | $0.00013 | $0.00497 |
Grade A, and why
bio-data-visualization-dimensionality-reduction-plots 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-data-visualization-dimensionality-reduction-plots — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: scanpy 1.10+, anndata 0.10+, scikit-learn 1.4+, umap-learn 0.5+, openTSNE 1.0+, phate 1.0+, ggplot2 3.5+, PCAtools 2.16+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Dimensionality-Reduction Plots
"Make a PCA / UMAP / t-SNE plot" -> Choose a projection method aligned with what the plot must reveal — variance explained (PCA), local neighborhood structure (t-SNE), manifold approximation with some global structure (UMAP), or continuous transitions (PHATE). Set hyperparameters deliberately. Communicate the projection's limits and refuse to over-interpret 2D distances.
- Python:
sklearn.decomposition.PCA,openTSNE,umap-learn,phate,scanpy.tl.umap/scanpy.tl.tsne/scanpy.tl.pca - R:
prcomp,PCAtools::pca,Seurat::RunPCA/RunUMAP/RunTSNE,phateR
The Single Most Important Modern Insight -- 2D Embeddings Distort
Chari & Pachter 2023 PLOS Comp Biol 19:e1011288 demonstrated that 2D embeddings of single-cell data lose >95% of the high-dimensional geometry — local neighborhoods are preserved by construction, but distances between distant cells, density estimates, and global topology are NOT preserved. The "specious art" of single-cell genomics is the practice of reading 2D layout as biology.
Practical consequence: a UMAP plot communicates "these cells are similar locally" and nothing more. Distance between clusters is meaningless. Density of points within a cluster is dominated by the embedding's repulsion parameter, not the underlying biology. A trajectory inferred from "the gap" between two clusters in UMAP space is an artifact unless validated against the high-dimensional data (RNA velocity, diffusion pseudotime, PHATE).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 327 lines · 133 tokens per session scan A 8776925949ca
bio-data-visualization-dimensionality-reduction-plots is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 133 tokens to every session and 4,972 once invoked, about $0.0007 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 2026-09-03.
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