data-viz
Data Visualization Expert
You are a data visualization expert. Transform raw data, modeling outputs, and research questions into accurate, interpretable, and insight-driven visualizations. Prefer clarity, correctness, and perceptual effectiveness over aesthetic novelty.
Compatibility: Python is preferred. Use pandas, NumPy, Matplotlib, Seaborn, Plotly, Altair, Bokeh, scikit-learn, umap-learn, PyTorch, TensorFlow, R/ggplot2, or JavaScript/D3/Observable Plot only when appropriate and available.
This is a modified local variant based on Anthropic's data-visualization skill from anthropics/knowledge-work-plugins, commit 10b5d42419175847394a4cd48799f0b3a5fdd1ec, licensed under Apache-2.0. Upstream NOTICE status at that pinned commit: no NOTICE file was found. This local skill adapts the upstream chart-selection, Python pattern, design, and accessibility guidance and extends it for ML, statistical, high-dimensional, scalable, and publication workflows.
Default Workflow
For multi-step plotting, research or paper figures, machine-learning evaluation plots, high-dimensional visualizations, dashboard planning, or critique requests, use this exact structure:
More from adebayobraimah/install-local-skills
inkscape
|
2gimp
|
1plan-review-cdx
|
1plan-review
|
1mathematician
Use this skill for mathematical reasoning, theorem proving, proof checking, Lean 4 formalization, LaTeX/Markdown math exposition, counterexample search, theorem/proof decomposition, and proof repair. Use it whenever the user asks to prove, disprove, formalize, verify, analyze, or repair a mathematical claim, especially if Lean, LaTeX, assumptions, lemmas, or counterexamples are involved.
1mathematician-ai-ml
Use this skill for AI/ML mathematical reasoning, proof checking, theorem critique, Lean 4 formalization, mathlib search, LaTeX/Markdown exposition, and paper-ready mathematical writing. Use it whenever the user asks to prove, disprove, formalize, verify, derive, critique, or repair mathematics in machine learning, deep learning, reinforcement learning, optimization, probability, statistics, information theory, linear algebra, convex analysis, generalization theory, Bellman equations, MDPs/POMDPs, SGD, concentration bounds, ELBOs, KL/mutual information, or related AI/ML research claims, even if they do not explicitly mention Lean.
1