analytics-and-data-visualization
Skill0 starsChanged 3 days agoMITView on GitHub
Select and implement data representations that accurately answer user questions and communicate uncertainty.
In this repository34 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
8 rules. About 0 are common, 8 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 10
Use for charts, reporting, analytics, data exploration, or visualization components.
- Distinctive: line 15
Do not use a chart when a concise number, explanation, or accessible table communicates the result better.
- Distinctive: line 26
Verify data shape, units, time bounds, aggregation, and missing values.
- Distinctive: line 34
Avoid misleading scales, decorative 3D, and color-only distinctions.
- Distinctive: line 35
Test edge cases such as zero, negative, missing, and extreme values.
- Distinctive: line 39
Never imply causation from a visual correlation.
- Distinctive: line 41
Use color palettes with adequate contrast and non-color cues.
- Distinctive: line 58
Check source and calculation, labels, scale, missing-data behavior, keyboard/screen-reader alternative, and small-screen legibility.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Analytics and Data Visualization
When to use
Use for charts, reporting, analytics, data exploration, or visualization components.
When not to use
Do not use a chart when a concise number, explanation, or accessible table communicates the result better.
Inputs and assumptions
Establish the question, data source, units, aggregation, time zone, missing data, audience, and actions the visualization should enable.
Workflow
- Define the question and comparison before choosing a chart type.
- Verify data shape, units, time bounds, aggregation, and missing values.
- Choose a visual encoding that matches the data: position for precise comparison, line for ordered trends, bars for categories, and tables for exact records.
- Label axes, units, periods, and relevant baselines; disclose normalized, sampled, or estimated data.
- Provide keyboard-accessible interaction and a text/table equivalent for essential information.
- Avoid misleading scales, decorative 3D, and color-only distinctions.
- Test edge cases such as zero, negative, missing, and extreme values.
Decision rules
- Never imply causation from a visual correlation.
- Preserve the data's meaning when filtering and aggregating.
- Use color palettes with adequate contrast and non-color cues.
- State when values are illustrative or synthetic.
Example
A time-series comparison should use the same time zone and interval for both periods; explain gaps where telemetry is missing instead of drawing a continuous line.
Common failures
- Truncated axes that exaggerate small differences.
- Unlabeled percentages and inconsistent time ranges.
- Tooltips as the only source of exact values.
Validation
Check source and calculation, labels, scale, missing-data behavior, keyboard/screen-reader alternative, and small-screen legibility.
Does it work?
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