Use when you need to choose the right visualization for your data and question, then create a narrated report that highlights insights and recommends actions. Invoke when analyzing data for patterns (trends, comparisons, distributions, relationships, compositions), building dashboards or reports, presenting metrics to stakeholders, monitoring KPIs, exploring datasets for insights, communicating findings from analysis, or when user mentions "visualize this", "what chart should I use", "create a dashboard", "analyze this data", "show trends", "compare these metrics", "report on", "what does this data tell us", or needs to turn data into actionable insights. Apply to business analytics (revenue, growth, churn, funnel, cohort, segmentation), product metrics (usage, adoption, retention, feature performance, A/B tests), marketing analytics (campaign ROI, attribution, funnel, customer acquisition), financial reporting (P&L, budget, forecast, variance), operational metrics (uptime, performance, capacity, SLA), sales analytics (pipeline, forecast, territory, quota attainment), HR metrics (headcount, turnover, engagement, DEI), and any scenario where data needs to become a clear, actionable story with the right visual form.
View on GitHublyndonkl/claude
thinking-frameworks-skills
January 24, 2026
Select agents to install to:
npx add-skill https://github.com/lyndonkl/claude/blob/main/skills/visualization-choice-reporting/SKILL.md -a claude-code --skill visualization-choice-reportingInstallation paths:
.claude/skills/visualization-choice-reporting/# Visualization Choice & Reporting ## Overview **Visualization choice & reporting** matches visualization types to questions and data, then creates narrated dashboards that highlight signal and recommend actions. **Three core components:** **1. Chart selection:** Match chart type to question type and data structure (comparison → bar chart, trend → line chart, distribution → histogram, relationship → scatter, composition → treemap, geographic → map, hierarchy → tree diagram, flow → sankey) **2. Visualization best practices:** Apply perceptual principles (position > length > angle > area > color for accuracy), reduce chart junk, use pre-attentive attributes (color, size, position) to highlight signal, respect accessibility (colorblind-safe palettes, alt text), choose appropriate scales (linear, log, normalized) **3. Narrative reporting:** Lead with insight headline, annotate key patterns, provide context (vs benchmark, vs target, vs previous period), interpret what it means, recommend next actions **When to use:** Data analysis, dashboards, reports, presentations, monitoring, exploration, stakeholder communication ## Workflow Copy this checklist and track your progress: ``` Visualization Choice & Reporting Progress: - [ ] Step 1: Clarify question and profile data - [ ] Step 2: Select visualization type - [ ] Step 3: Design effective chart - [ ] Step 4: Narrate insights and actions - [ ] Step 5: Validate and deliver ``` **Step 1: Clarify question and profile data** Define the question you're answering (What's the trend? How do X and Y compare? What's the distribution? What drives Z? What's the composition?). Profile your data: type (categorical, numerical, temporal, geospatial), granularity (daily, user-level, aggregated), size (10 rows, 10K, 10M), dimensions (1D, 2D, multivariate). See [Question-Data Profiling](#question-data-profiling). **Step 2: Select visualization type** Match question type to chart family using [Chart Selection Guide](#chart-selecti
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