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cs448b-visualization

Name: Cs448b Visualization
Author: sundial-org

// Data visualization design based on Stanford CS448B. Use for: (1) choosing chart types, (2) selecting visual encodings, (3) critiquing visualizations, (4) building D3.js visualizations, (5) designing interactions/animations, (6) choosing colors, (7) visualizing networks, (8) visualizing text. Covers Bertin, Mackinlay, Cleveland & McGill.

Manus에서 실행

$ git log --oneline --stat

stars:149

forks:8

updated:2026년 1월 25일 00:29

파일 탐색기

7 개 파일

SKILL.md

readonly

name	cs448b-visualization
description	Data visualization design based on Stanford CS448B. Use for: (1) choosing chart types, (2) selecting visual encodings, (3) critiquing visualizations, (4) building D3.js visualizations, (5) designing interactions/animations, (6) choosing colors, (7) visualizing networks, (8) visualizing text. Covers Bertin, Mackinlay, Cleveland & McGill.

CS448B Visualization

When to Use Each Reference

Reference	Use When
encoding-perception.md	Choosing how to map data to visual properties, or evaluating if encodings are effective
chart-design.md	Deciding which chart type fits the data, or configuring axes/scales
d3-patterns.md	Writing D3.js code for bindings, scales, axes, transitions
interaction-animation.md	Adding brushing, filtering, tooltips, or animated transitions
color.md	Selecting color palettes or ensuring accessibility
networks-text.md	Visualizing graphs, hierarchies, or text/document data

Critique Checklist

When reviewing any visualization:

Expressiveness - Does it show all the data? Only the data? No misleading elements?
Effectiveness - Is the most important data on the most accurate encoding (position > length > area > color)?
Zero baseline - Do bar charts start at zero?
Accessibility - Works for colorblind viewers (~8% of males)?
Data-ink ratio - Can any non-data elements be removed?
Aspect ratio - Are line charts banked so slopes are ~45°?

Encoding Decision Order

When mapping data fields to visual channels:

Most important quantitative → Position (x or y)
Second quantitative → Position (other axis) or Length
Categories (≤7) → Color hue
Categories (>7) → Position or small multiples
Magnitude/importance → Size (but expect ~30% underestimation)

Chart Selection Logic

One variable, distribution → Histogram
One variable, categories → Bar chart
Two variables, both quantitative → Scatterplot
Two variables, time + quantitative → Line chart
Two variables, both categorical → Heatmap
Hierarchy → Treemap or node-link tree
Network (sparse) → Force-directed layout
Network (dense) → Matrix diagram

Animation Decision

Presentation context → Use animation (faster, engaging)
Analysis context → Use small multiples (more accurate)
State transitions → Animate to maintain object constancy
Duration: 200-300ms quick feedback, 500-700ms standard, 1000ms+ complex

related-skills.json

같은 저장소

skill.md

from "sundial-org/skills"

Find, install, create, improve, and publish AI agent skills through the Sundial ecosystem. Use when the user wants to find or search for skills, install a skill, create a new skill, improve or evaluate an existing skill, or publish a skill to Sundial Hub. Trigger phrases include "find a skill", "install skill", "create a skill", "make a skill", "improve this skill", "evaluate skill", "publish skill", "push skill", "search for skills".

2026-03-07149

skill-to-card.md

from "sundial-org/skills"

End-to-end workflow that creates a skill from a description and attached files, publishes it to Sundial as a private skill, generates a trading card (front + back with QR code), and sends it to a printer. Use when the user wants to create a skill and get a printed trading card, or says "skill to card", "create and print a skill card", "make me a skill with a card".

2026-03-05149

neuro-symbolic-reasoning.md

from "sundial-org/skills"

Neuro-symbolic AI combining LLMs with symbolic solvers. Use when exploring neuro-symbolic approaches (ideation, no code) or implementing solver integrations (code).

2026-01-24149

cs-research-methodology.md

from "sundial-org/skills"

Conduct a literature review and develop a CS research proposal. Use when asked to review a research area, find gaps in existing work, and propose a novel research contribution. The output is a research proposal identifying an assumption to challenge (the "bit flip") and how to validate it.

2026-01-24149

commit-splitter.md

from "sundial-org/skills"

Split large sets of uncommitted changes into logical, well-organized commits. Use when the user has many uncommitted changes and wants structured commits, or proactively suggest when detecting a large diff that would benefit from splitting.

2026-01-24149

ai-co-scientist.md

from "sundial-org/skills"

Transform Claude Code into an AI Scientist that orchestrates research workflows using tree-based hypothesis exploration. Triggers on "research project", "scientific experiment", "run experiments", "AI scientist", "tree search experimentation", "systematic study".

2026-01-24149

package.json

"author": "sundial-org"

"repository": "sundial-org/skills"

GitHub 저장소 열기 Creator 저장소 보기

$ install --global

$ download --local

Manus에서 실행

$ useful --forSOC

웹 개발자컴퓨터 및 수학직15-1254L4

name	cs448b-visualization
description	Data visualization design based on Stanford CS448B. Use for: (1) choosing chart types, (2) selecting visual encodings, (3) critiquing visualizations, (4) building D3.js visualizations, (5) designing interactions/animations, (6) choosing colors, (7) visualizing networks, (8) visualizing text. Covers Bertin, Mackinlay, Cleveland & McGill.

CS448B Visualization

When to Use Each Reference

Reference	Use When
encoding-perception.md	Choosing how to map data to visual properties, or evaluating if encodings are effective
chart-design.md	Deciding which chart type fits the data, or configuring axes/scales
d3-patterns.md	Writing D3.js code for bindings, scales, axes, transitions
interaction-animation.md	Adding brushing, filtering, tooltips, or animated transitions
color.md	Selecting color palettes or ensuring accessibility
networks-text.md	Visualizing graphs, hierarchies, or text/document data

Critique Checklist

When reviewing any visualization:

Expressiveness - Does it show all the data? Only the data? No misleading elements?
Effectiveness - Is the most important data on the most accurate encoding (position > length > area > color)?
Zero baseline - Do bar charts start at zero?
Accessibility - Works for colorblind viewers (~8% of males)?
Data-ink ratio - Can any non-data elements be removed?
Aspect ratio - Are line charts banked so slopes are ~45°?

Encoding Decision Order

When mapping data fields to visual channels:

Most important quantitative → Position (x or y)
Second quantitative → Position (other axis) or Length
Categories (≤7) → Color hue
Categories (>7) → Position or small multiples
Magnitude/importance → Size (but expect ~30% underestimation)

Chart Selection Logic

One variable, distribution → Histogram
One variable, categories → Bar chart
Two variables, both quantitative → Scatterplot
Two variables, time + quantitative → Line chart
Two variables, both categorical → Heatmap
Hierarchy → Treemap or node-link tree
Network (sparse) → Force-directed layout
Network (dense) → Matrix diagram

Animation Decision

Presentation context → Use animation (faster, engaging)
Analysis context → Use small multiples (more accurate)
State transitions → Animate to maintain object constancy
Duration: 200-300ms quick feedback, 500-700ms standard, 1000ms+ complex

cs448b-visualization

CS448B Visualization

When to Use Each Reference

Critique Checklist

Encoding Decision Order

Chart Selection Logic

Animation Decision

이 저장소의 다른 Skills

이 저장소의 다른 Skills

CS448B Visualization

When to Use Each Reference

Critique Checklist

Encoding Decision Order

Chart Selection Logic

Animation Decision