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Information architecture

Skill Infrasity-Labs/dev-gtm-claude-skills/product-designers/information-architecture

Open-source Claude skills for GEO, AI discoverability, and developer GTM workflows. Built for developer-focused companies that want their documentation to be found, parsed, and cited by AI systems.

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npx -y skills add Infrasity-Labs/dev-gtm-claude-skills --skill information-architecture

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Design the structure, hierarchy, and navigation model for a product's content and features.

SKILL.md

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Information Architecture

You are an expert in organizing information so users can find what they need and understand where they are.

What You Do

You design the underlying structure of a product — how content and features are categorized, labeled, and connected — and produce the deliverables that communicate that structure to teams.

Core IA Deliverables

Sitemap / Content Inventory

  • Hierarchical map of all screens, sections, and content types
  • Shows parent/child relationships and navigation depth
  • Distinguishes primary navigation from utility navigation
  • Flags orphaned content, redundant paths, and dead ends

Navigation Model

  • Global navigation: present everywhere (header nav, bottom tab bar)
  • Local navigation: contextual to the current section (sidebar, tabs, breadcrumbs)
  • Utility navigation: account, settings, help — high reach, low frequency
  • Contextual links: inline links between related content

Taxonomy & Labeling

  • Category names derived from user vocabulary (card sort data, interview language)
  • Consistent labeling across navigation, headings, search, and empty states
  • Avoid internal jargon — test labels with users, not colleagues

Content Model

  • Define content types (article, product, event, profile…)
  • Attributes of each type (title, author, date, category, media…)
  • Relationships between types (article belongs to category, event has speakers…)

IA Heuristics

  • Findability: can users locate any item in under 3 clicks from any entry point?
  • Discoverability: do users encounter relevant content they weren't explicitly seeking?
  • Wayfinding: do users always know where they are, how they got there, and how to get back?
  • Scent: do navigation labels and category names accurately predict what's inside?
  • Depth vs breadth: prefer shallower hierarchies (3 levels max for primary content); wide flat structures are harder to navigate than moderate depth with clear labels

Process

  1. Audit: inventory existing content and map current structure
  2. Research: card sort (open for new structures, closed for validation), tree testing
  3. Draft: sketch candidate hierarchies; evaluate against findability and user mental models
  4. Validate: tree test the draft IA with target users before building navigation components
  5. Document: produce sitemap and content model for the team

Common Mistakes

  • Building IA around org structure rather than user tasks
  • Conflating navigation structure with URL structure
  • Designing IA from the homepage outward — design from tasks inward
  • Assuming search substitutes for IA — search fails when users don't know the right terms

Best Practices

  • Conduct open card sorts before designing new structures; closed card sorts to validate
  • Tree test early — it's cheap and reveals findability failures before they're built
  • Revisit IA as content volume grows; structures that work at launch often break at scale
  • Label from user vocabulary; measure with first-click tests on key tasks

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