bio-data-visualization-flow-and-transition-plots
Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
npx skills add BioTender-max/awesome-bio-agent-skills --skill flow-and-transition-plots --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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>` then `help(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 --
- Version Compatibility
- The Single Most Important Modern Insight -- Sankey vs Alluvial Are Different
- Decision Tree by Use Case
- ggalluvial -- Modern R Default for Alluvial
- networkD3 -- Interactive Sankey
- plotly Sankey (Python)
- CONSORT Diagrams -- The Formal Trial-Flow Standard
- Per-Method Failure Modes
- Sankey used when alluvial is appropriate
- Category ordering within column not specified
- Ribbon coloring by destination instead of origin
- CONSORT diagram missing required boxes
- plotly Sankey value sum mismatch
- Static export of plotly Sankey fails silently
What does the bio-data-visualization-flow-and-transition-plots skill do?
Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill flow-and-transition-plots --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.