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 flow-and-transition-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/flow-and-transition-plots)<a href="https://agentmods.dev/skills/gptomics/bioskills/flow-and-transition-plots"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/flow-and-transition-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/flow-and-transition-plots"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/flow-and-transition-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 | $0.00089 | $0.02964 |
| Opus 5 | $0.00044 | $0.01482 |
| Sonnet 5 | $0.00018 | $0.00593 |
| Haiku 4.5 | $0.00009 | $0.00296 |
Grade A, and why
bio-data-visualization-flow-and-transition-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-flow-and-transition-plots — 94% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ggalluvial 0.12+, networkD3 0.4+, plotly 4.10+, consort 0.2+ (CONSORT diagrams), pySankey 0.0.1+.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_name - Python:
pip show <package>thenhelp(module.function)
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Flow and Transition Plots
"Show how things flow between categories" -> Render entities as ribbons whose width encodes count, flowing between ordered columns of categories. Sankey emphasizes total flow magnitude; alluvial emphasizes per-entity continuity (each row's path is traceable); CONSORT formalizes the trial-filtering convention. The decision space: which method (Sankey vs alluvial vs CONSORT), how to order categories within each column, and whether to highlight specific entity trajectories.
- R:
ggalluvial::geom_alluvium,networkD3::sankeyNetwork,consort::consort_plot - Python:
plotly.graph_objects.Sankey,pySankey
The Single Most Important Modern Insight -- Sankey vs Alluvial Are Different
Sankey plots show flow from sources to sinks; each ribbon represents an aggregate count. The horizontal direction is "flow." Use for energy flows, web-traffic funnels, cohort dropouts.
Alluvial plots track individual entities through multiple ordered category columns (axes). Each row of input data becomes a continuous ribbon; intersections at each axis show counts in each category. Use for cell-state transitions across timepoints, drug-response trajectories, longitudinal class changes.
A Sankey shows "100 cells became neuron, 50 became glia"; an alluvial shows "of the 100 that became neurons at t2, 80 came from the proliferating pool at t1." Different encoding, different scientific story.
Decision Tree by Use Case
| Use case | Recommended | Tool |
|---|---|---|
| Single timepoint, source-to-sink flow | Sankey | networkD3, plotly |
| Multi-timepoint entity trajectories | Alluvial | ggalluvial |
| Clinical trial patient flow | CONSORT (formal vertical box-and-arrow) | consort R package |
| Variant filtering pipeline | CONSORT-style flow | consort or manual diagrammeR |
| Cell-state transitions (scRNA timepoints) | Alluvial OR Sankey if 2 timepoints | ggalluvial |
| Drug response class changes | Alluvial | ggalluvial |
| Gene-set membership across conditions | UpSet (alternative) | data-visualization/upset-plots |
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 · 235 lines · 89 tokens per session scan A 3daa2c93418d
bio-data-visualization-flow-and-transition-plots is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 89 tokens to every session and 2,964 once invoked, about $0.0004 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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