Visualization Patterns

Use when designing dashboards, reports, and narratives for GTM stakeholders.

Published by @gtmagents·0 agent reads / 30d·0 saves·Updated June 14, 2026

Analytics Visualization Patterns Skill

When to Use

Planning a new dashboard or updating existing visualizations.
Coaching teams on self-serve analytics adoption.
Auditing visual design, accessibility, and narrative clarity.

Framework

Audience & Story – clarify persona, decision cadence, and key questions.
Layout & Hierarchy – organize tiles by funnel (overview → drill-down → diagnostic), highlight KPIs.
Chart Selection – match metric type to chart (trend, composition, comparison, distribution).
Accessibility – color contrast, labels, tooltips, mobile/responsive considerations.
Narrative & Actions – annotate insights, embed CTA buttons or playbook links.

Templates

Dashboard wireframe grid with KPI slots.
Metric dictionary (definition, source, owner, refresh schedule).
Adoption checklist (stakeholder review, enablement session, feedback form).

Tips

Keep hero KPIs at top-left with trend vs target.
Use consistent color palettes for good/bad variance.
Embed documentation or Loom walkthroughs directly in BI tool when possible.

More on the bench

Dashboard Brief

Convert a business question into a complete dashboard specification. Use when asked to design a dashboard, create a dashboard spec or brief, plan a BI report, or define what charts and metrics a dashboard should include. Produces a structured spec with metrics, dimensions, chart types, filters, and layout guidance.

data-science-ml+2

Data Analysis Standard

Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.

data-science-ml+2

Guardrail Scorecard

Framework for defining, monitoring, and enforcing guardrail metrics across experiments.

product-management+2

Sharebench · Public bench

Connect via MCP.

Add the public bench to any AI tool. Your agents read every artifact you see — no signup needed.

1Pick your tool

# Add the public bench as an MCP server
claude mcp add --transport http sharebench-public https://mcp-public.sharebench.ai

# Settings → MCP → Add new MCP server
{
  "mcpServers": {
    "sharebench-public": {
      "url": "https://mcp-public.sharebench.ai"
    }
  }
}

# Custom GPT or Developer Mode
# Add a remote MCP connector with this URL:
https://mcp-public.sharebench.ai

# Append to ~/.codex/config.toml
[mcp_servers.sharebench_public]
url = "https://mcp-public.sharebench.ai"

2Ask your agent for an artifact

Once connected, just ask in plain language — your agent searches the bench and pulls in what it needs.“Pull the brand voice from sharebench and rewrite this draft.”