visual information architect
Diagnose information logic and design high-fidelity visual systems for complex technical data.
$5
Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
GitHub Copilot
Gemini CLI+20 moreClaude CodeCursorCodex CLIGitHub CopilotGemini CLI
OpenClaw
Manus
Grok+17 agentsvisual information architect
Example session with this skill installed
I have a dataset showing how different microservices (20+) interact, their latency, and which team owns them. I need to visualize this for a root cause analysis dashboard.
- Read your context and instructions
- Compiled the visual information architect
Information Diagnosis
- Audience: DevOps Engineers and SREs
- Context: Root cause analysis dashboard
- Intended decision or action: Identify which service/team is causing a bottleneck during an incident.
- Dominant information structures: Network/Ecosystem (dependencies) + Quantitative distribution (latency).
- Important constraints: Must handle 20+ nodes without becoming a "spaghetti" diagram.
- Assumptions: Latency data is real-time; ownership is static.
Recommended Visual System
- Primary mechanism: Node-link diagram with force-directed layout.
- Why it fits: Clearly maps the many-to-many service dependencies and highlights clusters.
- Secondary mechanisms: Color-coded heat map for latency; nested cards for team ownership.
- What should not be used: A standard table (too hard to see flow) or a linear flowchart (too many circular dependencies).
Comprehension Hierarchy
- Primary takeaway: Which service has the highest latency spike.
- Supporting relationship: Which downstream services are affected by that spike.
- Detail and qualification: Team contact info and specific P99 metrics.
- Entry point: Red-hued nodes (high latency).
- Reading path: Outward from the failing node along dependency lines to assess blast radius.
Visual Specification
Layout
A central interactive canvas where nodes are clustered by team ownership. High-latency nodes scale up in size.
Encodings
| Element | Visual treatment | Meaning |
|---|---|---|
| Node Size | Diameter (10px to 50px) | Current latency vs baseline |
| Node Color | Scale (Green to Red) | Health status / Error rate |
| Edge Thickness | Stroke width | Traffic volume (Requests per second) |
| Grouping | Background shaded zones | Team ownership boundaries |
Content Mapping
| Source content | Visual location | Treatment |
|---|---|---|
| Service Name | Center of node | Sans-serif, 12pt, high contrast |
| Latency (ms) | Tooltip / Hover | Monospaced numeric |
| Team Name | Zone Header | Bold caps, low opacity |
Validation
- Hierarchy: High; color and size immediately draw eye to failures.
- Comprehension: High; dependency lines clarify "who is calling who."
- Structural fidelity: Strong; preserves the non-linear nature of microservices.
- Accessibility: Uses both size and color to denote status; labels are mandatory.
Next steps
- Export the node-link specification to a D3.js or Cytoscape.js implementation plan.
- Define the refresh interval for the latency-to-size encoding.
- User-test the "blast radius" reading path with an SRE.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Complex datasets and technical documents often get crammed into generic bar charts or decorative infographics that obscure the actual logic of the information. Developers and analysts struggle to translate multi-dimensional relationships, causal chains, or hierarchical dependencies into layouts that preserve structural integrity.
What it does
- Analyzes raw information to diagnose the underlying logic, such as taxonomy, sequence, causality, or network ecosystems.
- Selects the specific visual mechanism—like Sankey flows, node-link diagrams, or evidence ladders—that matches the information's structure.
- Establishes a strict comprehension hierarchy to define the viewer's entry point, reading path, and primary takeaway.
- Generates detailed visual specifications including layout zones, element encodings, and content mapping for implementation.
- Validates the system for accessibility, density, and evidence integrity to ensure meaning isn't lost in translation.
Why this beats prompting it yourself
Generic LLM prompts usually produce aesthetic descriptions or basic charts. This skill uses a formal diagnostic framework to ensure the visual grammar matches the data logic, preventing "chart junk" and preserving nuanced technical details like uncertainty and evidence strength.
Use cases
- Designing dashboards for multi-dimensional trade-offs in cloud infrastructure performance.
- Visualizing complex decision logic or causal chains in legal or financial risk assessments.
- Mapping technical ecosystems and service dependencies for architectural documentation.
- Creating structured evidence ladders for scientific or research reporting.
Known limitations
This skill provides structural architecture and specifications; it does not generate final image files or executable frontend code directly.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
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- 2
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- 3
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