Data dashboard
Skill yigityildiz0/universal-ai-skill-library/skills/common/data-dashboard
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npx -y skills add yigityildiz0/universal-ai-skill-library --skill data-dashboardAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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- 19 days oldThe repository was created 19 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Plan, build, review, or improve a decision-focused dashboard with metric definitions, owners, filters, freshness, alerts, and validation. Use for dashboard, KPI dashboard, analytics dashboard, reporting board, or operational metrics view.
SKILL.md
1.0 KB, 142 tokens by cl100k_base, as published. Nobody here has run it
Data Dashboard
Build a dashboard as a decision interface, not a collection of charts.
- Define audience, decisions, owner, cadence, data freshness, metric contracts, and action thresholds.
- Limit the primary view to the few measures that change decisions; place diagnostic detail behind drill-downs.
- Specify filters, segmentation, comparison baselines, empty/error states, and point-in-time semantics.
- Validate each displayed measure against the source/query and show data freshness/known gaps.
- Review usability, accessibility, and performance before publishing.
Do not silently create external dashboards, connect data sources, or send alerts. Treat threshold colors as decision cues backed by a named owner.
Gives 0 of the 12 instructions most analytics metrics skills give in 142 tokens
Counted across 368 of the 369 authors here whose files we hold, read 2026-08-06
- read product marketing context before asking questionsin 18 of 368, across 12 files
- use lowercase with underscores for event namesin 16 of 368, across 6 files
- track events for decisions not vanity metricsin 15 of 368, across 5 files
- use object-action format for event namesin 15 of 368, across 8 files
- produce a tracking plan documentin 14 of 368, across 4 files
- Call RUBE_SEARCH_TOOLS first to get current schemasin 13 of 368, across 2 files
- establish consistent event naming conventions before implementingin 10 of 368, across 4 files
- Verify dimension and metric compatibility before reportingin 9 of 368, across 2 files
- Encrypt data at rest and in transitin 9 of 368, across 3 files
- use snake_case for event namesin 9 of 368, across 5 files
- monitor technical health during the testin 9 of 368, across 5 files
- use consistent property namesin 8 of 368, across 4 files
Said here and by no other author read
- define audience decisions owner cadence and thresholds
- limit the primary view to decision-changing measures
- place diagnostic detail behind drill-downs
- specify filters segmentation and comparison baselines
- specify empty error and point-in-time states
- validate each displayed measure against the source
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.