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 network-visualizationgit 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/network-visualization)<a href="https://agentmods.dev/skills/gptomics/bioskills/network-visualization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/network-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/gptomics/bioskills/network-visualization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/network-visualization.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 | $0.00100 | $0.03615 |
| Opus 5 | $0.00050 | $0.01808 |
| Sonnet 5 | $0.00020 | $0.00723 |
| Haiku 4.5 | $0.00010 | $0.00362 |
Grade A, and why
bio-data-visualization-network-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 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-network-visualization — 92% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: networkx 3.2+, igraph 0.10+ (Python and R), pyvis 0.3+, py4cytoscape 1.9+, matplotlib 3.8+, datashader 0.16+ (for large-graph rasterization).
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_name
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Network Visualization
"Plot a biological network" -> Select a layout algorithm (force-directed for general; hive plot for comparative; ForceAtlas2 for scale-free; circular for small dense), encode node attributes (size by degree/centrality, color by community/module), and choose rendering tier (matplotlib for static publication; PyVis for interactive HTML; Cytoscape for journal-grade compositing). The dominant pitfall is treating layout as biology — node positions in force-directed plots are NOT biologically meaningful; only connectivity is.
- Python:
networkx,pyvis.Network,py4cytoscape,datashader(large graphs) - R:
igraph,ggraph(ggplot2-grammar for networks) - Desktop: Cytoscape (Shannon 2003), Gephi (ForceAtlas2 native)
The Single Most Important Modern Insight -- Layout Is an Artifact, Not Biology
A force-directed layout (Fruchterman-Reingold, ForceAtlas2, spring) is the result of an optimization that minimizes edge crossing and balances repulsion. The visual position of a node has no biological meaning — it is determined by the layout algorithm + random initialization + iteration count + repulsion parameters.
Two consequences:
- Set
random_state/seedfor reproducibility. Without it, the same network produces different layouts across runs. - Do not read "cluster A is closer to cluster B than C" as biology. Inter-community distances in force-directed layouts are not preserved. Only EDGE existence and node DEGREE are biological signals from the visual.
What ships with it
4 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 · 319 lines · 100 tokens per session scan A 8f02e704b36b
bio-data-visualization-network-visualization is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 100 tokens to every session and 3,615 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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