visual-report

name	visual-report
description	Generate visual output — charts, dashboards, and trend graphs — from audit findings, session history, or system health data. Produces an interactive HTML dashboard or a set of SVG/Mermaid charts that make design system health visible at a glance. Trigger when someone says: visualise the findings, show me a chart, create a dashboard, graph the trends, make this visual, health dashboard, show me the data, I need charts for the stakeholder meeting, turn this into a visual report, or anything about producing visual representations of design system data. Do NOT trigger for producing a written stakeholder brief without visuals — use stakeholder-brief for that. Do NOT trigger for running an audit — run the audit skill first, then use visual-report to visualise the output.
references	["../../knowledge-notes/human-oversight-framework.md"]

Visual Report

A skill for transforming audit findings, system health statuses, and session history into visual outputs — interactive HTML dashboards, SVG charts, and Mermaid diagrams — that make design system health visible at a glance.

Output type: File creation. This skill produces HTML dashboard files, SVG chart files, or Mermaid diagram blocks that can be embedded in documentation, presentations, or shared directly.

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Why this exists

Numbers in a markdown table are accurate. They are also invisible. A stakeholder will not register "System health: 🟡 Functional with two weak dimensions" from a text report. But a radar chart showing dimension statuses — with red zones, amber zones, and green zones — communicates instantly.

Design system health data is inherently visual. Token coverage maps, component dependency graphs, severity distribution pie charts, trend lines over time — these are the natural representations of the data that audit skills produce. This skill bridges the gap between raw findings and visual communication.

Boundaries

This skill visualises existing findings — it does not run audits or generate new data. If no audit output or session history exists, there is nothing to visualise; run the relevant audit skill first. If the request is for a written stakeholder summary rather than a visual artefact, use stakeholder-brief instead. This skill produces HTML dashboards, SVG charts, and Mermaid diagrams — if the request is for a slide deck or PDF, the visual output from this skill can feed into those formats but this skill does not produce them directly.

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Configuration

Check for .ds-ops-config.yml in the project root:

visuals:
  brand_primary: "#0052CC"           # Primary colour for charts
  brand_secondary: "#FF5630"         # Accent colour for warnings/critical
  brand_success: "#36B37E"           # Success colour
  brand_neutral: "#6B778C"           # Neutral/baseline colour
  output_format: "html"              # html, svg, or mermaid
  output_directory: ".ds-ops/visuals"

If no configuration exists, use these defaults:

• Primary: #2563EB (blue)
• Secondary: #DC2626 (red)
• Success: #16A34A (green)
• Neutral: #6B7280 (grey)
• Output format: html
• Output directory: current working directory

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Input formats

This skill accepts any of these as input:

1. Raw skill output — Copy-pasted or referenced output from any audit skill
2. Session memory files — Files from the session-memory skill's directory
3. System health statuses — The dimension statuses from system-health
4. Comparison data — Before/after data from session-memory comparisons
5. Manual data — User-provided metrics in any format (will be normalised)

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Step 0: Determine the visual type

Based on the input data and the request, select one or more visual types:

Visual types available

Type	Best for	Format
Health radar	System health dimension statuses	Radar/spider chart
Severity distribution	Audit findings by severity	Donut chart
Trend line	Metric changes over time	Line chart
Coverage heatmap	Token or component coverage	Grid heatmap
Dependency graph	Component relationships	Mermaid flowchart
Comparison bar	Before/after comparisons	Grouped bar chart
Action priority matrix	Findings by effort vs. impact	Scatter plot
Full dashboard	Multiple visuals on one page	HTML dashboard

If the request is vague ("make this visual"), choose the visual type that best fits the data:

• System health statuses → Health radar
• Audit findings → Severity distribution + action priority matrix
• Session history → Trend line
• Before/after data → Comparison bar
• Multiple data types → Full dashboard

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Step 1: Parse and normalise the data

From audit output

Extract:

• Finding IDs, severities, categories
• Metric totals (violation counts, coverage percentages)
• Component names (for dependency graphs)
• Token tiers (for coverage heatmaps)

From session memory

Extract:

• Dates and skill names
• Key metrics per session (aligned for trend lines)
• Deltas between sessions

From system health

Extract:

• Seven dimension statuses (tokens, components, documentation, adoption, governance, AI readiness, platform maturity)
• Overall health status
• Maturity stage

Normalise all data into simple structures:

metrics: [{ label, value, max, category }]
timeseries: [{ date, metric, value }]
findings: [{ id, severity, category, effort, impact }]
relationships: [{ source, target, weight }]

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Step 2: Generate the visuals

Health radar chart

Produce a radar chart with seven axes (one per system health dimension). Map statuses to numeric values for charting: 🟢 Strong = 3, 🟡 Functional = 2, 🟠 Weak = 1, 🔴 Absent = 0.

Display axis labels using the status names, not numbers. The numeric mapping is internal for chart rendering only.

Colour coding:

• 0 (Absent): Red zone
• 1 (Weak): Amber zone
• 2 (Functional): Yellow-green zone
• 3 (Strong): Green zone

Implementation: Use Chart.js radar chart in HTML, or SVG polygon construction.

Severity distribution chart

Produce a donut chart showing the distribution of findings by severity.

Ring segments:

• Critical: Red (brand_secondary)
• High: Orange (#F59E0B)
• Medium: Amber (#D97706)
• Low: Blue (brand_primary)
• Info: Grey (brand_neutral)

Centre text: Total finding count.

Implementation: Chart.js doughnut, or SVG arc paths.

Trend line chart

Produce a line chart with time on the X axis and the metric on the Y axis. One line per metric being tracked.

Include:

• Data points marked with circles
• Hover tooltips with exact values
• A target line if configured
• Positive/negative trend annotation

Implementation: Chart.js line chart in HTML.

Coverage heatmap

Produce a grid where:

• Rows = token categories or component areas
• Columns = coverage dimensions (primitive, semantic, component, documented, tested)
• Cells = colour-coded by coverage status (green = covered, amber = partial, red = missing, grey = not applicable)

Implementation: HTML table with CSS background colours, or SVG grid.

Dependency graph

Produce a Mermaid flowchart showing component relationships:

graph TD
    Button["Button (fan-in: 12)"]
    Card["Card (fan-in: 8)"]
    Modal["Modal (fan-in: 5)"]
    Button --> Card
    Button --> Modal
    Card --> ProductCard["ProductCard"]
    Card --> UserCard["UserCard"]

Colour nodes by health status if data is available. Use line thickness to indicate dependency weight.

Implementation: Mermaid.js syntax block.

Comparison bar chart

Produce grouped bars showing before/after values for each metric. One group per metric (e.g., violations, critical count, coverage %).

Colour coding:

• Improved metrics: Green bars (after < before for violations; after > before for coverage)
• Worsened metrics: Red bars
• Unchanged metrics: Grey bars

Include delta labels above each bar group: "↓ 33%" or "↑ 5%"

Implementation: Chart.js grouped bar chart in HTML.

Action priority matrix

Produce a scatter plot where:

• X axis = Effort (Low → High)
• Y axis = Impact (Low → High)
• Each dot = one finding, sized by severity
• Quadrants labelled: "Quick wins" (low effort, high impact), "Strategic investments" (high effort, high impact), "Fill-ins" (low effort, low impact), "Consider carefully" (high effort, low impact)

Implementation: Chart.js scatter chart with quadrant overlays in HTML.

Full dashboard

Combine multiple charts into a single HTML page with:

• A header showing system name, date, and overall health status
• A grid layout (2 columns on desktop, 1 column on mobile)
• Charts sized proportionally
• A summary section at the top with 3–5 key metric cards
• An interactive filter (if session history data is present): dropdown to switch between sessions

Implementation: Single self-contained HTML file with Chart.js loaded from CDN. No external dependencies beyond Chart.js. All data embedded inline.

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Step 3: Build the HTML dashboard (for full dashboard mode)

The dashboard is a single HTML file. Structure:

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>[System Name] — Design System Health Dashboard</title>
  <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
  <style>
    /* Inline styles — no external CSS */
    /* Use CSS Grid for layout */
    /* Responsive: 2 columns above 768px, 1 column below */
    /* Cards with subtle shadows for key metrics */
    /* Colour variables from config or defaults */
  </style>
</head>
<body>
  <header>
    <!-- System name, date, overall health status badge -->
  </header>
  <section class="metrics-cards">
    <!-- 3–5 key metric summary cards -->
  </section>
  <section class="charts-grid">
    <!-- Individual chart containers -->
  </section>
  <footer>
    <p>Generated by Design System Ops — visual-report</p>
    <p>[Date]</p>
  </footer>
  <script>
    // All chart configuration inline
    // Data embedded as JS objects
    // Chart.js instantiation for each chart
  </script>
</body>
</html>

Key metric cards

Each card shows:

• Metric label (e.g., "Total violations")
• Current value (large font)
• Delta from previous session (if available, with arrow and colour)
• Sparkline trend (if 3+ data points available)

Responsive behaviour

• Desktop (>768px): 2-column grid for charts, 3–5 cards in a row
• Tablet (768px): 2-column grid for charts, cards wrap to 2 per row
• Mobile (<768px): Single column, cards stack vertically

Colour accessibility

• Never rely on colour alone to convey meaning
• Use patterns (dashed lines for targets, solid for actuals)
• Use labels on all chart segments
• Ensure contrast ratio ≥ 4.5:1 for all text
• Include a text-based summary below each chart for screen readers

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Step 4: Output

For HTML dashboard

Save the file to the configured output directory: [output_directory]/[system-name]-dashboard-[YYYY-MM-DD].html

For individual charts (SVG)

Save each chart as a separate SVG file: [output_directory]/[chart-type]-[YYYY-MM-DD].svg

For Mermaid diagrams

Output the Mermaid syntax block inline in the conversation (for embedding in markdown documentation).

For all formats

Also output a text-based summary of what the visuals show, so the findings are accessible without viewing the visual output:

Dashboard generated: agds-dashboard-2026-03-09.html

What the visuals show:
- Health radar: Strongest in Tokens (🟢 Strong), weakest in Documentation (🟠 Weak)
- Severity distribution: 12 findings — 2 Critical, 4 High, 4 Medium, 2 Low
- Trend: Violations decreased 33% since January
- Coverage: Feedback token category has zero coverage across all tiers
- Priority: 3 findings in the "Quick wins" quadrant — TA-04, NA-02, CA-11

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Integration with other skills

As a follow-up to any audit skill

After any audit completes, suggest: "Run visual-report to generate charts from these findings."

As part of the full-system-diagnostic agent

The diagnostic agent can chain visual-report after Phase 4 to auto-generate a dashboard from the full diagnostic output.

As a companion to stakeholder-brief

When generating a stakeholder brief, suggest: "Run visual-report first and attach the dashboard to the brief."

With session-memory

Load session history from memory files to produce trend lines across multiple runs.

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Quality checks

• All charts render correctly in a standard browser (Chrome, Firefox, Safari)
• No external dependencies beyond Chart.js CDN
• Colours pass WCAG AA contrast ratios
• Text summaries accompany every visual
• Dashboard is responsive across desktop, tablet, and mobile
• Data embedded inline — no external data fetches
• File saved to correct directory with correct naming convention
• Mermaid diagrams use valid syntax that renders in GitHub, GitLab, and Notion
• Provenance marker present: "Generated by Design System Ops — visual-report"