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version: "1.0.0" name: dashboard-from-research description: Transform research data (Excel, CSV, stickies) into structured insights and interactive dashboards
Dashboard from Research — Research Data to Interactive Dashboard
Transform research data (Excel, CSV, FigJam stickies, markdown) into structured insights and interactive dashboards with dataviz components. Chains research pipeline → specs → code generation → preview.
Freedom Level: High
Full autonomy over data interpretation, visualization choices, and dashboard layout. Must back every design decision with the research data.
When to Use
- User has research data (Excel, CSV, survey results, interview notes)
- FigJam board has stickies from workshops or brainstorming
- Need to create a dashboard that visualizes research findings
- Turning qualitative/quantitative data into actionable UI
Workflow
Step 1: Ingest Research Data
memi research from-file <path> → Excel/CSV parsingmemi research from-stickies → FigJam sticky notesmemi research synthesize → Combine all sources
Output: research/insights.json with structured findings.
Step 2: Analyze & Categorize
Classify insights into dashboard-friendly categories:
Quantitative → KPI cards, charts, trend lines- Metrics: numeric values with labels- Time series: data over time → line/area charts- Comparisons: A vs B → bar charts- Distributions: spread → histogramsQualitative → Text summaries, tag clouds, quotes- Themes: grouped findings → category cards- Quotes: user verbatims → quote components- Sentiment: positive/negative → sentiment indicatorsRelational → Flow diagrams, matrices, maps- User journeys: step sequences → flow components- Relationships: connections → network graphs- Hierarchies: nested structures → tree views
Step 3: Create Specs (Atomic Design)
For each visualization need, create the right spec type:
KPI metric → memi spec component MetricCard (molecule)props: { title, value, change, trend, icon }Trend chart → memi spec dataviz TrendChartchartType: "area" | "line"dataShape: { x: "date", y: "value", series: [...] }Comparison → memi spec dataviz ComparisonChartchartType: "bar"dataShape: { category: "string", values: [...] }The dashboard page → memi spec page ResearchDashboardlayout: "dashboard"sections: [metrics-row, charts-row, insights-section, quotes]
Step 4: Generate Code
memi generate → all specs → React + Tailwindmemi preview → localhost preview server
Step 5: Design in Figma (Optional)
If the dashboard should also exist in Figma:
1. use_figma → create the dashboard layout using components2. figma_take_screenshot → validate3. Self-healing loop (max 3 rounds)4. add_code_connect_map → establish design ↔ code parity
Dashboard Layout Pattern
Frame (VERTICAL, fill, 1280×900)├── Header (HORIZONTAL, hug height, fill width, padding=24)│ ├── Title: "Research Dashboard"│ ├── Subtitle: research date range│ └── Actions: export, filter, refresh├── Metrics Row (HORIZONTAL, fill, gap=16, padding=24)│ └── MetricCard × 4-6 (fill, equal width)├── Charts Section (HORIZONTAL, fill, gap=16, padding=0-24)│ ├── Primary Chart (2/3 width)│ └── Secondary Chart (1/3 width)├── Insights Grid (grid 2-3 col, gap=16, padding=24)│ └── InsightCard × N└── Detail Section (VERTICAL, fill, padding=24)└── DataTable or QuotesList
Data → Chart Type Decision
| Data Pattern | Chart Type | Recharts Component | |
|---|---|---|---|
| Single value + trend | KPI Card | Custom (Card + Badge) | |
| Values over time | Area/Line | <AreaChart> / <LineChart> | |
| Category comparison | Bar | <BarChart> | |
| Part of whole | Pie/Donut | <PieChart> | |
| Two dimensions | Scatter | <ScatterChart> | |
| Distribution | Histogram | <BarChart> (binned) | |
| Multiple metrics | Composed | <ComposedChart> |
Anti-Patterns
- Creating charts without understanding the data first
- Using complex visualizations when a simple KPI card suffices
- Not including data source attribution
- Hardcoding sample data instead of connecting to research output
- Skipping the research synthesis step (going straight to UI)
- Not generating specs before code (violates spec-first)