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 heatmaps-clusteringgit 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/heatmaps-clustering)<a href="https://agentmods.dev/skills/gptomics/bioskills/heatmaps-clustering"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/heatmaps-clustering/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/heatmaps-clustering"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/heatmaps-clustering.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 | $0.00102 | $0.05740 |
| Opus 5 | $0.00051 | $0.02870 |
| Sonnet 5 | $0.00020 | $0.01148 |
| Haiku 4.5 | $0.00010 | $0.00574 |
Grade A, and why
bio-data-visualization-heatmaps-clustering 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-heatmaps-clustering — 94% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ComplexHeatmap 2.18+, pheatmap 1.0.13 (still maintained as of 2025-06), circlize 0.4.16+, seaborn 0.13+, scipy 1.12+, scanpy 1.10+, ggplot2 3.5+.
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.
Heatmaps and Hierarchical Clustering
"Make a clustered heatmap" -> Render an expression / feature matrix as a colored grid with hierarchical-clustering dendrograms, after committing to (a) how to scale the data (row z-score vs raw vs robust), (b) which distance metric (Euclidean vs correlation vs Manhattan), (c) which linkage criterion (ward.D2 vs complete vs average), (d) how to order the leaves (default vs optimal leaf ordering), (e) how to map values to color (sequential vs diverging, robust quantile bounds), and (f) which package can handle the matrix size and annotation complexity.
- R:
ComplexHeatmap::Heatmap()(modern default),pheatmap::pheatmap()(still maintained, simpler API) - Python:
seaborn.clustermap(),scanpy.pl.heatmap()(single-cell-aware)
The Single Most Important Modern Insight -- Distance, Linkage, and Scaling Are Three Independent Decisions
A heatmap's dendrograms are produced by three orthogonal choices, each with material biological consequences:
- Scaling decides what "similar" means. Row z-scoring asks "do these genes covary across samples?" — it strips absolute level. Raw values ask "do these genes have similar magnitude AND pattern?" Robust scaling (quantile clip) asks "do these covary after suppressing outliers?"
- Distance metric decides how dissimilar two profiles are. Euclidean on z-scored data ≈
1 − Pearsoncorrelation; Manhattan tolerates outliers; correlation distance preserves co-regulation patterns regardless of amplitude. - Linkage decides how to merge clusters. Ward minimizes within-cluster sum of squares (compact, spherical clusters); complete uses max distance (compact, outlier-sensitive); average is balanced; single chains (almost never what genomics wants).
What ships with it
3 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 · 339 lines · 102 tokens per session scan A 4bc6f5c1a572
bio-data-visualization-heatmaps-clustering is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 102 tokens to every session and 5,740 once invoked, about $0.0005 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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