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+---
+name: information-architecture
+description: Use when organizing content for digital products, designing navigation systems, restructuring information hierarchies, improving findability, creating taxonomies or metadata schemas, or when users mention information architecture, IA, sitemap, navigation design, content structure, card sorting, tree testing, taxonomy, findability, or need help making information discoverable and usable.
+---
+
+# Information Architecture
+
+## Purpose
+
+Information architecture (IA) is the practice of organizing, structuring, and labeling content to help users find and manage information effectively. Good IA makes complex information navigable, discoverable, and understandable.
+
+Use this skill when:
+- **Designing navigation** for websites, apps, documentation, or knowledge bases
+- **Restructuring content** that users can't find or understand
+- **Creating taxonomies** for classification, tagging, or metadata
+- **Organizing information** at scale (hundreds or thousands of items)
+- **Improving findability** when search and browse both fail
+- **Designing mental models** that match how users think about content
+
+Information architecture bridges user mental models and system structure. The goal: users can predict where information lives and find it quickly.
+
+---
+
+## Common Patterns
+
+### Pattern 1: Content Audit → Card Sort → Sitemap
+
+**When**: Redesigning existing site/app with lots of content
+
+**Process**:
+1. **Content audit**: Inventory all existing content (URLs, titles, metadata)
+2. **Card sorting**: Users group content cards into categories
+3. **Analyze patterns**: What categories emerge? What's grouped together?
+4. **Create sitemap**: Translate patterns into hierarchical structure
+5. **Validate with tree testing**: Can users find content in new structure?
+
+**Example**: E-commerce site with 500 products. Audit products → Card sort with 15 users → Patterns show users group by "occasion" not "product type" → New navigation: "Daily Essentials", "Special Occasions", "Gifts" instead of "Electronics", "Clothing", "Home Goods"
+
+### Pattern 2: Taxonomy Design (Faceted Navigation)
+
+**When**: Users need multiple ways to slice/filter information
+
+**Structure**: Orthogonal facets (dimensions) that combine
+- **Facet 1**: Category (e.g., "Shoes", "Shirts", "Pants")
+- **Facet 2**: Brand (e.g., "Nike", "Adidas", "Puma")
+- **Facet 3**: Price range (e.g., "$0-50", "$50-100", "$100+")
+- **Facet 4**: Color, Size, etc.
+
+**Principle**: Facets are independent. Users can filter by any combination.
+
+**Example**: Amazon product browse. Filter by Category AND Brand AND Price simultaneously. Each facet narrows results without breaking others.
+
+### Pattern 3: Progressive Disclosure (Hub-and-Spoke)
+
+**When**: Content hierarchy is deep, users need overview before details
+
+**Structure**:
+- **Hub page**: High-level overview with clear labels
+- **Spoke pages**: Detailed content, linked from hub
+- **Breadcrumbs**: Show path back to hub
+
+**Principle**: Don't overwhelm with everything at once. Start simple, reveal complexity on-demand.
+
+**Example**: Documentation site. Hub: "Getting Started" with 5 clear options (Install, Configure, First App, Tutorials, Troubleshooting). Each option links to detailed spoke. Users scan hub, pick entry point, dive deep, return to hub if stuck.
+
+### Pattern 4: Flat vs. Deep Navigation
+
+**When**: Deciding navigation depth (breadth vs. depth tradeoff)
+
+**Flat navigation** (broad, shallow):
+- **Structure**: Many top-level categories, few sub-levels (e.g., 10 categories, 2 levels deep)
+- **Pros**: Less clicking, everything visible
+- **Cons**: Overwhelming choice, hard to scan 10+ options
+
+**Deep navigation** (narrow, tall):
+- **Structure**: Few top-level categories, many sub-levels (e.g., 5 categories, 5 levels deep)
+- **Pros**: Manageable choices at each level (5-7 items)
+- **Cons**: Many clicks to reach content, users get lost in depth
+
+**Optimal**: **3-4 levels deep, 5-9 items per level** (Hick's Law: more choices = longer decision time)
+
+**Example**: Software docs. Flat: All 50 API methods visible at once (overwhelming). Deep: APIs → Authentication → Methods → JWT → jwt.sign() (5 clicks, frustrating). Optimal: APIs (8 categories) → Authentication (6 methods) → jwt.sign() (3 clicks).
+
+### Pattern 5: Mental Model Alignment (Card Sorting)
+
+**When**: You don't know how users think about content
+
+**Process**:
+1. **Open card sort**: Users create their own categories (exploratory)
+2. **Closed card sort**: Users fit content into your categories (validation)
+3. **Hybrid card sort**: Users use your categories OR create new ones (refinement)
+4. **Analyze**: What labels do users use? What groupings emerge? What's confusing?
+
+**Example**: SaaS product features. Company calls them "Widgets", "Modules", "Components" (technical terms). Card sort reveals users think "Reports", "Dashboards", "Alerts" (task-based terms). **Insight**: Label by user tasks, not internal architecture.
+
+### Pattern 6: Tree Testing (Reverse Card Sort)
+
+**When**: Validating navigation structure before building
+
+**Process**:
+1. **Create text-based tree** (sitemap without visuals)
+2. **Give users tasks**: "Where would you find X?"
+3. **Track paths**: What route did they take? Did they succeed?
+4. **Measure**: Success rate, directness (fewest clicks), time
+
+**Example**: Navigation tree with "Services → Web Development → E-commerce". Task: "Find information about building an online store". 80% success = good. 40% success = users don't understand "E-commerce" label or "Services" category. Iterate.
+
+---
+
+## Workflow
+
+Use this structured approach when designing or auditing information architecture:
+
+```
+□ Step 1: Understand context and users
+□ Step 2: Audit existing content (if any)
+□ Step 3: Conduct user research (card sorting, interviews)
+□ Step 4: Design taxonomy and navigation
+□ Step 5: Create sitemap and wireframes
+□ Step 6: Validate with tree testing
+□ Step 7: Implement and iterate
+□ Step 8: Monitor findability metrics
+```
+
+**Step 1: Understand context and users** ([details](#1-understand-context-and-users))
+Identify content volume, user goals, mental models, and success metrics (time to find, search queries, bounce rate).
+
+**Step 2: Audit existing content** ([details](#2-audit-existing-content))
+Inventory all content (URLs, titles, metadata). Identify duplicates, gaps, outdated items. Measure current performance (analytics, heatmaps).
+
+**Step 3: Conduct user research** ([details](#3-conduct-user-research))
+Run card sorting (open, closed, or hybrid) with 15-30 users. Analyze clustering patterns, category labels, outliers. Conduct user interviews to understand mental models.
+
+**Step 4: Design taxonomy and navigation** ([details](#4-design-taxonomy-and-navigation))
+Create hierarchical structure (3-4 levels, 5-9 items per level). Design facets for filtering. Choose labeling system (task-based, audience-based, or alphabetical). Define metadata schema.
+
+**Step 5: Create sitemap and wireframes** ([details](#5-create-sitemap-and-wireframes))
+Document structure visually (sitemap diagram). Create low-fidelity wireframes showing navigation, breadcrumbs, filters. Get stakeholder feedback.
+
+**Step 6: Validate with tree testing** ([details](#6-validate-with-tree-testing))
+Test navigation with text-based tree (no visuals). Measure success rate (≥70%), directness (≤1.5× optimal path), time. Identify problem areas, iterate.
+
+**Step 7: Implement and iterate** ([details](#7-implement-and-iterate))
+Build high-fidelity designs and implement. Launch incrementally (pilot → rollout). Gather feedback from real users.
+
+**Step 8: Monitor findability metrics** ([details](#8-monitor-findability-metrics))
+Track time to find, search success rate, navigation abandonment, bounce rate, user feedback. Refine taxonomy based on data.
+
+---
+
+## Critical Guardrails
+
+### 1. Test with Real Users, Not Assumptions
+
+**Danger**: Designing based on stakeholder opinions or personal preferences
+
+**Guardrail**: Always validate with user research (card sorting, tree testing, usability testing). Minimum 15 participants for statistical significance.
+
+**Red flag**: "I think users will understand 'Synergistic Solutions'..." — If you're guessing, you're wrong.
+
+### 2. Avoid Org Chart Navigation
+
+**Danger**: Structuring navigation by internal org structure (Sales, Marketing, Engineering)
+
+**Guardrail**: Structure by user mental models and tasks, not company departments
+
+**Example**: Bad: "About Us → Departments → Engineering → APIs". Good: "For Developers → APIs"
+
+### 3. Keep Navigation Shallow (3-4 Levels Max)
+
+**Danger**: Deep hierarchies (5+ levels) where users get lost
+
+**Guardrail**: Aim for 3-4 levels deep, 5-9 items per level. If deeper needed, add search, filtering, or multiple entry points.
+
+**Rule of thumb**: If users need >4 clicks from homepage to content, rethink structure.
+
+### 4. Use Clear, Specific Labels (Not Jargon)
+
+**Danger**: Vague labels ("Resources", "Solutions") or internal jargon ("SKU Management")
+
+**Guardrail**: Labels must be specific, action-oriented, and match user vocabulary. Test labels in card sorts and tree tests.
+
+**Test**: Could a new user predict what's under this label? If not, clarify.
+
+### 5. Ensure Single, Predictable Location
+
+**Danger**: Content lives in multiple places, or users can't predict location
+
+**Guardrail**: Each content type should have ONE canonical location. If cross-category, use clear primary location + links from secondary.
+
+**Principle**: "Principle of least astonishment" — content is where users expect it.
+
+### 6. Design for Scale
+
+**Danger**: Structure works for 50 items but breaks at 500
+
+**Guardrail**: Think ahead. If you have 50 products now but expect 500, design faceted navigation from start. Don't force retrofitting later.
+
+**Test**: What happens if this category grows 10×? Will structure still work?
+
+### 7. Provide Multiple Access Paths
+
+**Danger**: Only one way to find content (e.g., only browse, no search)
+
+**Guardrail**: Offer browse (navigation), search, filters, related links, breadcrumbs, tags. Different users have different strategies.
+
+**Principle**: Some users are "searchers" (know what they want), others are "browsers" (exploring). Support both.
+
+### 8. Validate Before Building
+
+**Danger**: Building full site/app before testing structure
+
+**Guardrail**: Use tree testing (text-based navigation) to validate structure before expensive design/dev work
+
+**ROI**: 1 day of tree testing saves weeks of rework after launch.
+
+---
+
+## Quick Reference
+
+### IA Methods Comparison
+
+| Method | When to Use | Participants | Deliverable |
+|--------|-------------|--------------|-------------|
+| **Open card sort** | Exploratory, unknown categories | 15-30 users | Category labels, groupings |
+| **Closed card sort** | Validation of existing categories | 15-30 users | Fit quality, confusion points |
+| **Tree testing** | Validate navigation structure | 20-50 users | Success rate, directness, problem areas |
+| **Content audit** | Understand existing content | 1-2 analysts | Inventory spreadsheet, gaps, duplicates |
+| **User interviews** | Understand mental models | 5-10 users | Mental model diagrams, quotes |
+
+### Navigation Depth Guidelines
+
+| Content Size | Recommended Structure | Example |
+|--------------|----------------------|---------|
+| <50 items | Flat (1-2 levels) | Blog, small product catalog |
+| 50-500 items | Moderate (2-3 levels) | Documentation, medium e-commerce |
+| 500-5000 items | Deep with facets (3-4 levels + filters) | Large e-commerce, knowledge base |
+| 5000+ items | Hybrid (browse + search + facets) | Amazon, Wikipedia |
+
+### Labeling Systems
+
+| System | When to Use | Example |
+|--------|-------------|---------|
+| **Task-based** | Users have clear goals | "Book a Flight", "Track Order", "Pay Invoice" |
+| **Audience-based** | Different user types | "For Students", "For Teachers", "For Parents" |
+| **Topic-based** | Reference/learning content | "History", "Science", "Mathematics" |
+| **Format-based** | Media libraries | "Videos", "PDFs", "Podcasts" |
+| **Alphabetical** | No clear grouping, lookup-heavy | "A-Z Directory", "Glossary" |
+
+### Success Metrics
+
+| Metric | Target | Measurement |
+|--------|--------|-------------|
+| **Tree test success rate** | ≥70% | Users find correct destination |
+| **Directness** | ≤1.5× optimal path | Clicks taken / optimal clicks |
+| **Time to find** | <30 sec (simple), <2 min (complex) | Task completion time |
+| **Search success** | ≥60% find without search | % completing task without search |
+| **Bounce rate** | <40% | % leaving immediately from landing page |
+
+---
+
+## Resources
+
+### Navigation to Resources
+
+- [**Templates**](resources/template.md): Content audit template, card sorting template, sitemap template, tree testing script
+- [**Methodology**](resources/methodology.md): Card sorting analysis, taxonomy design, navigation patterns, findability optimization
+- [**Rubric**](resources/evaluators/rubric_information_architecture.json): Evaluation criteria for IA quality (10 criteria)
+
+### Related Skills
+
+- **data-schema-knowledge-modeling**: For database schema and knowledge graphs
+- **mapping-visualization-scaffolds**: For visualizing information structure
+- **discovery-interviews-surveys**: For user research methods
+- **evaluation-rubrics**: For creating IA evaluation criteria
+- **communication-storytelling**: For explaining IA decisions to stakeholders
+
+---
+
+## Examples in Context
+
+### Example 1: E-commerce Navigation Redesign
+
+**Context**: Bookstore with 10,000 books organized by publisher (internal logic)
+
+**Approach**: Content audit → Open card sort (20 users: genre-based, not publisher) → Faceted navigation: Genre × Format × Price × Rating → Tree test (75% success) → **Result**: Time to find -40%, conversion +15%
+
+### Example 2: SaaS Documentation IA
+
+**Context**: Developer docs, high abandonment after 2 pages
+
+**Approach**: User interviews (mental model = tasks not features) → Taxonomy shift: feature-based to task-based ("Get Started", "Store Data") → Progressive disclosure (hub-and-spoke) → Tree test (68% → 82% success) → **Result**: Engagement +50%, support tickets -25%
+
+### Example 3: Internal Knowledge Base
+
+**Context**: Company wiki with 2,000 articles, employees can't find policies
+
+**Approach**: Content audit (40% outdated, 15% duplicates) → Closed card sort (25 employees) → Hybrid: browse (known needs) + search (unknown) + metadata schema → Search best bets → **Result**: Search success 45% → 72%, time to find 5min → 1.5min
--- a/skills/information-architecture/resources/evaluators/rubric_information_architecture.json
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+{
+  "criteria": [
+    {
+      "name": "User Research Validation",
+      "description": "IA decisions based on user research (card sorting, tree testing, interviews), not assumptions or opinions.",
+      "scale": {
+        "1": "No user research. IA based on stakeholder opinions, org chart, or assumptions about users.",
+        "3": "Some user research (5-10 participants) but limited scope or informal methods.",
+        "5": "Rigorous user research: card sorting (15-30 users), tree testing (20-50 users), validated with real users before implementation."
+      }
+    },
+    {
+      "name": "Mental Model Alignment",
+      "description": "Navigation matches user mental models and vocabulary, not internal company jargon or org structure.",
+      "scale": {
+        "1": "Labels use internal jargon, org chart structure, or vague terms ('Solutions', 'Services'). Users confused.",
+        "3": "Mix of user-friendly and internal terms. Some alignment with user mental models.",
+        "5": "Labels match user vocabulary from research. Task-based or user-goal oriented. Clear mental model alignment."
+      }
+    },
+    {
+      "name": "Taxonomy Quality (MECE)",
+      "description": "Categories are mutually exclusive, collectively exhaustive. Clear, non-overlapping structure.",
+      "scale": {
+        "1": "Categories overlap significantly. Gaps in coverage. Items fit multiple places or nowhere.",
+        "3": "Mostly MECE but some overlaps or gaps. Most items have clear home.",
+        "5": "Perfect MECE: every item has exactly one primary home, no gaps, no overlaps. Faceted where appropriate."
+      }
+    },
+    {
+      "name": "Navigation Depth & Breadth",
+      "description": "Optimal depth (3-4 levels) and breadth (5-9 items per level). Not too flat or too deep.",
+      "scale": {
+        "1": "Too flat (>12 top-level items, overwhelming) OR too deep (>5 levels, users lost). Poor balance.",
+        "3": "Acceptable depth/breadth but some levels have <3 or >12 items. Room for optimization.",
+        "5": "Optimal: 3-4 levels deep, 5-9 items per level. Balanced hierarchy, manageable choices at each level."
+      }
+    },
+    {
+      "name": "Information Scent",
+      "description": "Clear labels, trigger words, descriptive breadcrumbs guide users. Strong predictive cues.",
+      "scale": {
+        "1": "Vague labels ('Resources', 'Other'), no breadcrumbs, users guess. Weak scent.",
+        "3": "Some clear labels but inconsistent. Breadcrumbs present but could be more descriptive.",
+        "5": "Strong scent: specific labels, user trigger words, descriptive breadcrumbs, preview text. Users click confidently."
+      }
+    },
+    {
+      "name": "Findability Metrics",
+      "description": "Measurable findability: time to find, success rate, search vs browse balance.",
+      "scale": {
+        "1": "No metrics tracked. Unknown if users can find content.",
+        "3": "Some metrics tracked (analytics, basic usability) but incomplete or informal.",
+        "5": "Comprehensive metrics: tree test ≥70% success, time to find <30 sec (simple) / <2 min (complex), 40-60% search/browse balance."
+      }
+    },
+    {
+      "name": "Tree Testing Validation",
+      "description": "Navigation structure validated with tree testing before implementation.",
+      "scale": {
+        "1": "No tree testing. Built navigation without validation.",
+        "3": "Informal tree testing (5-10 users) or partial testing (only some tasks).",
+        "5": "Rigorous tree testing: 20-50 users, 8-12 tasks, ≥70% success rate, <1.5× directness, iterated based on results."
+      }
+    },
+    {
+      "name": "Multiple Access Paths",
+      "description": "Users can find content via navigation, search, filters, tags, related links. Supports different strategies.",
+      "scale": {
+        "1": "Only one access method (browse-only or search-only). Single path to content.",
+        "3": "Two access methods (browse + search) but limited. No filters, tags, or related links.",
+        "5": "Multiple paths: browse, search, faceted filters, tags, breadcrumbs, related links. Supports searchers and browsers."
+      }
+    },
+    {
+      "name": "Scalability & Evolution",
+      "description": "Structure designed for growth. Handles 10× content without breaking. Governance plan exists.",
+      "scale": {
+        "1": "Structure works for current size but breaks at scale. No governance. No plan for growth.",
+        "3": "Some consideration for scale. Ad-hoc governance. Will need significant rework as content grows.",
+        "5": "Designed for scale: faceted for large sets, versioned taxonomy, governance framework (roles, processes, metrics), evolution plan."
+      }
+    },
+    {
+      "name": "Progressive Disclosure",
+      "description": "Starts simple, reveals complexity on-demand. Hub-and-spoke, collapsed sections, tiered navigation.",
+      "scale": {
+        "1": "Everything visible at once (overwhelming) OR everything hidden (users can't discover).",
+        "3": "Some progressive disclosure but inconsistent or incomplete.",
+        "5": "Well-designed progressive disclosure: hub-and-spoke for guides, collapsed sections for long nav, contextual secondary nav."
+      }
+    }
+  ],
+  "guidance_by_type": {
+    "E-commerce Navigation": {
+      "target_score": 4.2,
+      "key_criteria": ["Taxonomy Quality (MECE)", "Multiple Access Paths", "Findability Metrics"],
+      "common_pitfalls": ["Too many top-level categories", "No faceted filters", "Search vs browse not balanced"],
+      "specific_guidance": "Use faceted navigation (Category × Price × Brand × Rating). Card sort with 20+ customers to understand grouping preferences. Tree test with 'Find product under $X in Y category' tasks. Track search vs browse ratio (target 40-60% each). Provide multiple paths: browse categories, search, filter, sort."
+    },
+    "Documentation IA": {
+      "target_score": 4.0,
+      "key_criteria": ["Mental Model Alignment", "Progressive Disclosure", "Information Scent"],
+      "common_pitfalls": ["Feature-based (not task-based)", "Too deep nesting", "No 'Getting Started' hub"],
+      "specific_guidance": "Structure by user tasks ('How do I...?') not features. Hub-and-spoke: 'Getting Started' hub with 5-7 clear entry points. 3-4 levels max. Breadcrumbs show path. Related links for discovery. Search with code snippet results. Tree test with 'Find how to do X' tasks (≥80% success)."
+    },
+    "Content-Heavy Website": {
+      "target_score": 3.8,
+      "key_criteria": ["User Research Validation", "Taxonomy Quality (MECE)", "Scalability & Evolution"],
+      "common_pitfalls": ["No content audit", "Overlapping categories", "No governance"],
+      "specific_guidance": "Start with content audit: inventory all content, identify duplicates/gaps. Open card sort (20-30 users) to discover categories. MECE taxonomy design. Tree test (30-50 users, 10-12 tasks). Plan for 3× content growth. Establish governance: taxonomy owner, quarterly reviews, tag moderation."
+    },
+    "SaaS Product Navigation": {
+      "target_score": 4.0,
+      "key_criteria": ["Mental Model Alignment", "Navigation Depth & Breadth", "Information Scent"],
+      "common_pitfalls": ["Internal jargon", "Org chart structure", "'Modules' label"],
+      "specific_guidance": "Card sort with active users (not stakeholders). Rename internal terms to user language: 'Widgets' → 'Reports', 'Modules' → 'Features'. Task-based labels: 'Create Report', 'Share Dashboard'. Flat structure (2-3 levels). Contextual help. Tree test with 'Where would you do X?' tasks. Iterate until ≥75% success."
+    },
+    "Intranet / Knowledge Base": {
+      "target_score": 3.5,
+      "key_criteria": ["Findability Metrics", "Multiple Access Paths", "Scalability & Evolution"],
+      "common_pitfalls": ["Outdated content", "Weak search", "No metadata schema"],
+      "specific_guidance": "Content audit: remove 40%+ outdated, merge duplicates. Closed card sort with employees to validate categories. Hybrid approach: browse by department + robust search + best bets for common queries. Metadata schema: author, last updated, audience, content type, tags. Governance: content owners responsible for freshness."
+      }
+  },
+  "guidance_by_complexity": {
+    "Simple (<100 items, clear structure)": {
+      "target_score": 3.5,
+      "focus_areas": ["Mental Model Alignment", "Navigation Depth & Breadth", "Information Scent"],
+      "acceptable_shortcuts": ["Informal card sort (10 users)", "Simple tree test (10 users, 5 tasks)", "Basic analytics"],
+      "specific_guidance": "Flat structure (1-2 levels). Simple categories. Informal user research acceptable (10 users). Basic tree test (5 tasks, ≥70% success). Monitor with analytics (bounce rate, time on site)."
+    },
+    "Standard (100-1000 items, moderate complexity)": {
+      "target_score": 4.0,
+      "focus_areas": ["User Research Validation", "Taxonomy Quality (MECE)", "Tree Testing Validation", "Findability Metrics"],
+      "acceptable_shortcuts": ["Card sort with 15 users (not 30)", "Tree test with 20 users (not 50)"],
+      "specific_guidance": "Card sort (15-20 users). MECE taxonomy (3-4 levels, 5-9 items per level). Tree test (20-30 users, 8-10 tasks, ≥70% success, ≤1.5× directness). Multiple access paths (browse + search). Track findability metrics (time to find, success rate, search/browse ratio)."
+    },
+    "Complex (>1000 items, high complexity)": {
+      "target_score": 4.5,
+      "focus_areas": ["All criteria", "Rigorous validation", "Comprehensive metrics"],
+      "acceptable_shortcuts": ["None - full rigor required"],
+      "specific_guidance": "Full content audit. Card sort (30 users). Faceted navigation (orthogonal facets). MECE taxonomy + controlled vocabulary. Tree test (50 users, 12 tasks, ≥75% success). Multiple access paths (browse, search, filters, tags, related links). Comprehensive metrics dashboard. Governance framework (taxonomy owner, quarterly reviews, versioning, deprecation process)."
+    }
+  },
+  "common_failure_modes": [
+    {
+      "name": "Org Chart Navigation",
+      "symptom": "Navigation structured by company departments (Sales, Marketing, Engineering) not user tasks or mental models.",
+      "detection": "Tree test shows low success (<50%). Users say 'I don't know what department handles this.' Card sort reveals users group differently than org structure.",
+      "fix": "Restructure by user tasks or goals. Card sort with users to discover natural groupings. Rename categories to user-facing labels: 'For Developers' not 'Engineering Department'."
+    },
+    {
+      "name": "No User Research",
+      "symptom": "IA designed by stakeholders or designers based on opinions, not user data. 'I think users will understand...'",
+      "detection": "No card sort, tree test, or user interviews conducted. Decisions justified by 'makes sense to me' not data.",
+      "fix": "Conduct card sorting (20-30 users) to understand mental models. Tree test (20-50 users) to validate proposed structure before building. Iterate based on results."
+    },
+    {
+      "name": "Too Deep Hierarchy",
+      "symptom": "Navigation 5-6+ levels deep. Users need 6+ clicks to reach content. Users get lost, high abandonment.",
+      "detection": "Tree test shows low directness (>2.5×). Analytics shows high bounce rate on intermediate pages. Users complain 'can't find anything'.",
+      "fix": "Flatten structure: 3-4 levels max. Add faceted filters or search to shortcut hierarchy. Use progressive disclosure (hub-and-spoke) instead of deep nesting."
+    },
+    {
+      "name": "Vague Labels",
+      "symptom": "Labels like 'Resources', 'Solutions', 'Services' — users don't know what's inside. Weak information scent.",
+      "detection": "Tree test shows users clicking multiple categories before finding content (low directness). Card sort shows users rename these categories to more specific terms.",
+      "fix": "Use specific, descriptive labels. 'Code Samples' not 'Resources'. 'API Documentation' not 'Solutions'. Test labels with tree testing. Match user vocabulary from card sort."
+    },
+    {
+      "name": "Non-MECE Taxonomy",
+      "symptom": "Items fit multiple categories OR don't fit anywhere. Overlap and gaps.",
+      "detection": "Content audit shows many items marked 'belongs in multiple places'. Users confused where to look. Card sort shows weak clustering (<50% agreement).",
+      "fix": "Redesign taxonomy to be mutually exclusive (no overlap) and collectively exhaustive (no gaps). Use faceted classification for items with multiple attributes. One primary location + facets/tags for cross-category access."
+    },
+    {
+      "name": "No Tree Testing",
+      "symptom": "Built navigation structure without validating users can find content. Launch reveals users struggle.",
+      "detection": "Live site shows low task success, high bounce rate, users complain 'can't find X'. No pre-launch validation done.",
+      "fix": "Always tree test before building. 20-50 users, 8-12 tasks. Target ≥70% success, ≤1.5× directness. Iterate until targets met. Saves rework post-launch."
+    },
+    {
+      "name": "Single Access Path",
+      "symptom": "Only browse (no search) OR only search (no browse). Users with different strategies struggle.",
+      "detection": "Analytics shows 90%+ use search (navigation broken) OR 90%+ browse (search broken). Users complain 'I can't browse' or 'search doesn't work'.",
+      "fix": "Provide multiple paths: browse (navigation), search, faceted filters, tags, breadcrumbs, related links. Support both 'searchers' (know what they want) and 'browsers' (exploring)."
+    },
+    {
+      "name": "Doesn't Scale",
+      "symptom": "Structure works for 100 items but breaks at 1000. Categories become massive, overwhelming.",
+      "detection": "One category has 60%+ of items. Users complain 'too much content'. Search becomes only viable path.",
+      "fix": "Design for scale upfront. Use faceted navigation for large sets. Split large categories into subcategories. Plan for 10× growth. Establish governance to evolve taxonomy as content grows."
+    },
+    {
+      "name": "No Governance",
+      "symptom": "Taxonomy degrades over time. Content in 'Other', empty categories, inconsistent tagging.",
+      "detection": ">10% content in 'Other' or 'Uncategorized'. Empty categories exist. User-generated tags have synonyms, typos, noise.",
+      "fix": "Establish governance framework: taxonomy owner, content owners, quarterly reviews. Monitor metrics (% in 'Other', empty categories, tag quality). Process for adding/removing categories, merging tags."
+    },
+    {
+      "name": "No Progressive Disclosure",
+      "symptom": "Everything visible at once (overwhelming mega-menu) OR everything hidden (users can't discover).",
+      "detection": "Users complain 'too much information' (overwhelming) OR 'I didn't know that existed' (hidden). Low engagement with deep content.",
+      "fix": "Progressive disclosure: hub-and-spoke for guides (overview → details), collapsed sections in navigation (expand on click), tiered navigation (primary always visible, secondary contextual)."
+    }
+  ],
+  "minimum_standard": 3.5,
+  "target_score": 4.0,
+  "excellence_threshold": 4.5
+}
--- a/skills/information-architecture/resources/methodology.md
+++ b/skills/information-architecture/resources/methodology.md
@@ -0,0 +1,494 @@
+# Information Architecture: Advanced Methodology
+
+This document covers advanced techniques for card sorting analysis, taxonomy design, navigation optimization, and findability improvement.
+
+## Table of Contents
+1. [Card Sorting Analysis](#1-card-sorting-analysis)
+2. [Taxonomy Design Principles](#2-taxonomy-design-principles)
+3. [Navigation Depth & Breadth Optimization](#3-navigation-depth--breadth-optimization)
+4. [Information Scent & Findability](#4-information-scent--findability)
+5. [Advanced Topics](#5-advanced-topics)
+
+---
+
+## 1. Card Sorting Analysis
+
+### Analyzing Card Sort Results
+
+**Goal**: Extract meaningful patterns from user groupings
+
+### Similarity Matrix
+
+**What it is**: Shows how often users grouped two cards together
+
+**How to calculate**:
+- For each pair of cards, count how many users put them in the same group
+- Express as percentage: (# users who grouped together) / (total users)
+
+**Example**:
+
+|  | Sign Up | First Login | Quick Start | Reports | Dashboards |
+|--|---------|-------------|-------------|---------|------------|
+| Sign Up | - | 85% | 90% | 15% | 10% |
+| First Login | 85% | - | 88% | 12% | 8% |
+| Quick Start | 90% | 88% | - | 10% | 12% |
+| Reports | 15% | 12% | 10% | - | 75% |
+| Dashboards | 10% | 8% | 12% | 75% | - |
+
+**Interpretation**:
+- **Strong clustering** (>70%): "Sign Up", "First Login", "Quick Start" belong together → "Getting Started" category
+- **Strong clustering** (75%): "Reports" and "Dashboards" belong together → "Analytics" category
+- **Weak links** (<20%): "Getting Started" and "Analytics" are distinct categories
+
+### Dendrogram (Hierarchical Clustering)
+
+**What it is**: Tree diagram showing hierarchical relationships
+
+**How to create**:
+1. Start with each card as its own cluster
+2. Iteratively merge closest clusters (highest similarity)
+3. Continue until all cards in one cluster
+
+**Interpreting dendrograms**:
+- **Short branches**: High agreement (merge early)
+- **Long branches**: Low agreement (merge late)
+- **Clusters**: Cut tree at appropriate height to identify categories
+
+**Example**:
+```
+                        All Cards
+                            |
+        ____________________+_____________________
+        |                                         |
+    Getting Started                          Features
+        |                                         |
+    ____+____                              _____+_____
+    |        |                            |           |
+  Sign Up  First Login              Analytics    Settings
+                                        |
+                                    ____+____
+                                    |        |
+                                 Reports  Dashboards
+```
+
+**Insight**: Users see clear distinction between "Getting Started" (onboarding tasks) and "Features" (ongoing use).
+
+### Agreement Score (Consensus)
+
+**What it is**: How much users agree on groupings
+
+**Calculation methods**:
+
+1. **Category agreement**: % of users who created similar category
+   - Example: 18/20 users (90%) created "Getting Started" category
+
+2. **Pairwise agreement**: Average similarity across all card pairs
+   - Formula: Sum(all pairwise similarities) / Number of pairs
+   - High score (>70%) = strong consensus
+   - Low score (<50%) = weak consensus, need refinement
+
+**When consensus is low**:
+- Cards may be ambiguous (clarify labels)
+- Users have different mental models (consider multiple navigation paths)
+- Category is too broad (split into subcategories)
+
+### Outlier Cards
+
+**What they are**: Cards that don't fit anywhere consistently
+
+**How to identify**: Low similarity with all other cards (<30% with any card)
+
+**Common reasons**:
+- Card label is unclear → Rewrite card
+- Content doesn't belong in product → Remove
+- Content is unique → Create standalone category or utility link
+
+**Example**: "Billing" card — 15 users put it in "Settings", 3 in "Account", 2 didn't categorize it
+- **Action**: Clarify if "Billing" is settings (configuration) or account (transactions)
+
+---
+
+## 2. Taxonomy Design Principles
+
+### Mutually Exclusive, Collectively Exhaustive (MECE)
+
+**Principle**: Categories don't overlap AND cover all content
+
+**Mutually exclusive**: Each item belongs to exactly ONE category
+- **Bad**: "Products" and "Best Sellers" (best sellers are also products — overlap)
+- **Good**: "Products" (all) and "Featured" (separate facet or tag)
+
+**Collectively exhaustive**: Every item has a category
+- **Bad**: Categories: "Electronics", "Clothing" — but you also sell "Books" (gap)
+- **Good**: Add "Books" OR create "Other" catch-all
+
+**Testing MECE**:
+1. List all content items
+2. Try to categorize each
+3. If item fits >1 category → not mutually exclusive
+4. If item fits 0 categories → not collectively exhaustive
+
+### Polyhierarchy vs. Faceted Classification
+
+**Polyhierarchy**: Item can live in multiple places in hierarchy
+- **Example**: "iPhone case" could be in:
+  - Electronics > Accessories > Phone Accessories
+  - Gifts > Under $50 > Tech Gifts
+- **Pro**: Matches multiple user mental models
+- **Con**: Confusing (where is "canonical" location?), hard to maintain
+
+**Faceted classification**: Item has ONE location, multiple orthogonal attributes
+- **Example**: "iPhone case" is in Electronics (primary category)
+  - Facet 1: Category = Electronics
+  - Facet 2: Price = Under $50
+  - Facet 3: Use Case = Gifts
+- **Pro**: Clear, flexible filtering, scalable
+- **Con**: Requires good facet design
+
+**When to use each**:
+- **Polyhierarchy**: Small content sets (<500 items), clear user need for multiple paths
+- **Faceted**: Large content sets (>500 items), many attributes, users need flexible filtering
+
+### Controlled Vocabulary vs. Folksonomy
+
+**Controlled vocabulary**: Preset tags, curated by admins
+- **Example**: "Authentication", "API", "Database" (exact tags, no variations)
+- **Pro**: Consistency, findability, no duplication ("Auth" vs "Authentication")
+- **Con**: Requires maintenance, may miss user terminology
+
+**Folksonomy**: User-generated tags, anyone can create
+- **Example**: Users tag articles with whatever terms they want
+- **Pro**: Emergent, captures user language, low maintenance
+- **Con**: Inconsistent, duplicates, noise ("Auth", "Authentication", "auth", "Authn")
+
+**Hybrid approach** (recommended):
+- Controlled vocabulary for core categories and facets
+- Folksonomy for supplementary tags (with moderation)
+- Periodically review folksonomy tags → promote common ones to controlled vocabulary
+
+**Tag moderation**:
+- Merge synonyms: "Auth" → "Authentication"
+- Remove noise: "asdf", "test"
+- Suggest tags: When user types "auth", suggest "Authentication"
+
+### Category Size & Balance
+
+**Guideline**: Aim for balanced category sizes (no one category dominates)
+
+**Red flags**:
+- **One huge category**: "Other" with 60% of items → need better taxonomy
+- **Many tiny categories**: 20 categories, each with 2-5 items → over-categorization, consolidate
+- **Unbalanced tree**: One branch 5 levels deep, others 2 levels → inconsistent complexity
+
+**Target distribution**:
+- Top-level categories: 5-9 categories
+- Each category: Roughly equal # of items (within 2× of each other)
+- If one category much larger: Split into subcategories
+
+**Example**: E-commerce with 1000 products
+- **Bad**: Electronics (600), Clothing (300), Books (80), Other (20)
+- **Good**: Electronics (250), Clothing (250), Books (250), Home & Garden (250)
+
+### Taxonomy Evolution
+
+**Principle**: Taxonomies grow and change — design for evolution
+
+**Strategies**:
+1. **Leave room for growth**: Don't create 10 top-level categories if you'll need 15 next year
+2. **Use "Other" temporarily**: New category emerging but not big enough yet? Use "Other" until critical mass
+3. **Versioning**: Date taxonomy versions, track changes over time
+4. **Deprecation**: Don't delete categories immediately — mark "deprecated", redirect users, then remove after transition period
+
+**Example**: Software product adding ML features
+- **Today**: 20 ML-related articles scattered across "Advanced", "API", "Tutorials"
+- **Transition**: Create "Machine Learning" subcategory under "Advanced"
+- **Future**: 100 ML articles → Promote "Machine Learning" to top-level category
+
+---
+
+## 3. Navigation Depth & Breadth Optimization
+
+### Hick's Law & Choice Overload
+
+**Hick's Law**: Decision time increases logarithmically with number of choices
+
+**Formula**: Time = a + b × log₂(n + 1)
+- More choices → longer time to decide
+
+**Implications for IA**:
+- **5-9 items per level**: Sweet spot (Miller's "7±2")
+- **>12 items**: Users feel overwhelmed, scan inefficiently
+- **<3 items**: Feels unnecessarily nested
+
+**Example**:
+- 100 items, flat (1 level, 100 choices): Overwhelming
+- 100 items, 2 levels (10 × 10): Manageable
+- 100 items, 4 levels (3 × 3 × 3 × 4): Too many clicks
+
+**Optimal for 100 items**: 3 levels (5 × 5 × 4) or (7 × 7 × 2)
+
+### The "3-Click Rule" Myth
+
+**Myth**: Users abandon if content requires >3 clicks
+
+**Reality**: Users tolerate clicks if:
+1. **Progress is clear**: Breadcrumbs, page titles show "getting closer"
+2. **Information scent is strong**: Each click brings them closer to goal (see Section 4)
+3. **No dead ends**: Every click leads somewhere useful
+
+**Research** (UIE study): Users successfully completed tasks requiring 5-12 clicks when navigation was clear
+
+**Guideline**: Minimize clicks, but prioritize clarity over absolute number
+- **Good**: 5 clear, purposeful clicks
+- **Bad**: 2 clicks but confusing labels, users backtrack
+
+### Breadth-First vs. Depth-First Navigation
+
+**Breadth-first** (shallow, many top-level options):
+- **Structure**: 10-15 top-level categories, 2-3 levels deep
+- **Best for**: Browsing, exploration, users know general area but not exact item
+- **Example**: News sites, e-commerce homepages
+
+**Depth-first** (narrow, few top-level but deep):
+- **Structure**: 3-5 top-level categories, 4-6 levels deep
+- **Best for**: Specific lookup, expert users, hierarchical domains
+- **Example**: Technical documentation, academic libraries
+
+**Hybrid** (recommended for most):
+- **Structure**: 5-7 top-level categories, 3-4 levels deep
+- **Supplement with**: Search, filters, related links to "shortcut" across hierarchy
+
+### Progressive Disclosure
+
+**Principle**: Start simple, reveal complexity on-demand
+
+**Techniques**:
+
+1. **Hub-and-spoke**: Overview page → Detailed pages
+   - Hub: "Getting Started" with 5 clear entry points
+   - Spokes: Detailed guides linked from hub
+
+2. **Accordion/Collapse**: Hide detail until user expands
+   - Navigation: Show categories, hide subcategories until expanded
+   - Content: Show summary, expand for full text
+
+3. **Tiered navigation**: Primary nav (always visible) + secondary nav (contextual)
+   - Primary: "Products", "Support", "About"
+   - Secondary (when in "Products"): "Electronics", "Clothing", "Books"
+
+4. **"More..." links**: Show top N items, hide rest until "Show more" clicked
+   - Navigation: Top 5 categories visible, "+3 more" link expands
+
+**Anti-pattern**: Mega-menus showing everything at once (overwhelming)
+
+---
+
+## 4. Information Scent & Findability
+
+### Information Scent
+
+**Definition**: Cues that indicate whether a path will lead to desired information
+
+**Strong scent**: Clear labels, descriptive headings, users click confidently
+**Weak scent**: Vague labels, users guess, backtrack often
+
+**Example**:
+- **Weak scent**: "Solutions" → What's in there? (generic)
+- **Strong scent**: "Developer API Documentation" → Clear what's inside
+
+**Optimizing information scent**:
+
+1. **Specific labels** (not generic):
+   - Bad: "Resources" → Too vague
+   - Good: "Code Samples", "Video Tutorials", "White Papers" → Specific
+
+2. **Trigger words** (match user vocabulary):
+   - Card sort reveals users say "How do I..." → Label category "How-To Guides"
+   - Users search "pricing" → Ensure "Pricing" in nav, not "Plans" or "Subscription"
+
+3. **Descriptive breadcrumbs**:
+   - Bad: "Home > Section 1 > Page 3" → No meaning
+   - Good: "Home > Developer Docs > API Reference" → Clear path
+
+4. **Preview text**: Show snippet of content under link
+   - Navigation item: "API Reference" + "Complete list of endpoints and parameters"
+
+### Findability Metrics
+
+**Key metrics to track**:
+
+1. **Time to find**: How long to locate content?
+   - **Target**: <30 sec for simple tasks, <2 min for complex
+   - **Measurement**: Task completion time in usability tests
+
+2. **Success rate**: % of users who find content?
+   - **Target**: ≥70% (tree test), ≥80% (live site with search)
+   - **Measurement**: Tree test results, task success in usability tests
+
+3. **Search vs. browse**: Do users search or navigate?
+   - **Good**: 40-60% browse, 40-60% search (both work)
+   - **Bad**: 90% search (navigation broken), 90% browse (search broken)
+   - **Measurement**: Analytics (search usage %, nav click-through)
+
+4. **Search refinement rate**: % of searches that are refined?
+   - **Target**: <30% (users find on first search)
+   - **Bad**: >50% (users search, refine, search again → poor results)
+   - **Measurement**: Analytics (queries per session)
+
+5. **Bounce rate by entry point**: % leaving immediately?
+   - **Target**: <40% for landing pages
+   - **Bad**: >60% (users don't find what they expected)
+   - **Measurement**: Analytics (bounce rate by page)
+
+6. **Navigation abandonment**: % who start navigating, then leave?
+   - **Target**: <20%
+   - **Bad**: >40% (users get lost, give up)
+   - **Measurement**: Analytics (drop-off in navigation funnels)
+
+### Search vs. Navigation Trade-offs
+
+**When search is preferred**:
+- Large content sets (>5000 items)
+- Users know exactly what they want ("lookup" mode)
+- Diverse content types (hard to categorize consistently)
+
+**When navigation is preferred**:
+- Smaller content sets (<500 items)
+- Users browsing, exploring ("discovery" mode)
+- Hierarchical domains (clear parent-child relationships)
+
+**Best practice**: Offer BOTH
+- Navigation for discovery, context, exploration
+- Search for lookup, speed, known-item finding
+
+**Optimizing search**:
+- **Autocomplete**: Suggest as user types
+- **Filters**: Narrow results by category, date, type
+- **Best bets**: Featured results for common queries
+- **Zero-results page**: Suggest alternatives, show popular content
+
+**Optimizing navigation**:
+- **Clear labels**: Match user vocabulary (card sort insights)
+- **Faceted filters**: Browse + filter combination
+- **Related links**: Help users discover adjacent content
+- **Breadcrumbs**: Show path, enable backtracking
+
+---
+
+## 5. Advanced Topics
+
+### Mental Models & User Research
+
+**Mental model**: User's internal representation of how system works
+
+**Why it matters**: Navigation should match user's mental model, not company's org chart
+
+**Researching mental models**:
+
+1. **Card sorting**: Reveals how users group/label content
+2. **User interviews**: Ask "How would you organize this?" "What would you call this?"
+3. **Tree testing**: Validates if proposed structure matches mental model
+4. **First-click testing**: Where do users expect to find X?
+
+**Common mismatches**:
+- **Company thinks**: "Features" (technical view)
+- **Users think**: "What can I do?" (task view)
+- **Solution**: Rename to task-based labels ("Create Report", "Share Dashboard")
+
+**Example**: SaaS product
+- **Internal (wrong)**: "Modules" → "Synergistic Solutions" → "Widget Management"
+- **User mental model (right)**: "Features" → "Reporting" → "Custom Reports"
+
+### Cross-Cultural IA
+
+**Challenge**: Different cultures have different categorization preferences
+
+**Examples**:
+- **Alphabetical**: Works for Latin scripts, not ideographic (Chinese, Japanese)
+- **Color coding**: Red = danger (Western), Red = luck (Chinese)
+- **Icons**: Mailbox icon = email (US), doesn't translate (many countries have different mailbox designs)
+
+**Strategies**:
+1. **Localization testing**: Card sort with target culture users
+2. **Avoid culturally-specific metaphors**: "Home run", "touchdown" (US sports)
+3. **Simple, universal labels**: "Home", "Search", "Help" (widely understood)
+4. **Icons + text**: Don't rely on icons alone
+
+### IA Governance
+
+**Problem**: Taxonomy degrades over time without maintenance
+
+**Governance framework**:
+
+1. **Roles**:
+   - **Content owner**: Publishes content, assigns categories/tags
+   - **Taxonomy owner**: Maintains category structure, adds/removes categories
+   - **IA steward**: Monitors usage, recommends improvements
+
+2. **Processes**:
+   - **Quarterly review**: Check taxonomy usage, identify issues
+   - **Change request**: How to propose new categories or restructure
+   - **Deprecation**: Process for removing outdated categories
+   - **Tag moderation**: Review user-generated tags, merge synonyms
+
+3. **Metrics to monitor**:
+   - % content in "Other" or "Uncategorized" (should be <5%)
+   - Empty categories (no content) — remove or consolidate
+   - Oversized categories (>50% of content) — split into subcategories
+
+4. **Tools**:
+   - CMS with taxonomy management
+   - Analytics to track usage
+   - Automated alerts (e.g., "Category X has no content")
+
+### Personalization & Dynamic IA
+
+**Concept**: Navigation adapts to user
+
+**Approaches**:
+
+1. **Audience-based**: Show different nav for different user types
+   - "For Developers", "For Marketers", "For Executives"
+
+2. **History-based**: Prioritize recently visited or frequently used
+   - "Recently Viewed", "Your Favorites"
+
+3. **Context-based**: Show nav relevant to current task
+   - "Related Articles", "Next Steps"
+
+4. **Adaptive search**: Results ranked by user's past behavior
+
+**Caution**: Don't over-personalize
+- Users need consistency to build mental model
+- Personalization should augment, not replace, standard navigation
+
+### IA for Voice & AI Interfaces
+
+**Challenge**: Traditional visual hierarchy doesn't work for voice
+
+**Strategies**:
+
+1. **Flat structure**: No deep nesting (can't show menu)
+2. **Natural language categories**: "Where can I find information about X?" vs. "Navigate to Category > Subcategory"
+3. **Conversational**: "What would you like to do?" vs. "Select option 1, 2, or 3"
+4. **Context-aware**: Remember user's previous question, continue conversation
+
+**Example**:
+- **Web**: Home > Products > Electronics > Phones
+- **Voice**: "Show me phones" → "Here are our top phone options..."
+
+---
+
+## Summary
+
+**Card sorting** reveals user mental models through similarity matrices, dendrograms, and consensus scores. Outliers indicate unclear content.
+
+**Taxonomy design** follows MECE principle (mutually exclusive, collectively exhaustive). Use faceted classification for scale, controlled vocabulary for consistency, and plan for evolution.
+
+**Navigation optimization** balances breadth (many choices) vs. depth (many clicks). Optimal: 5-9 items per level, 3-4 levels deep. Progressive disclosure reduces initial complexity.
+
+**Information scent** guides users with clear labels, trigger words, and descriptive breadcrumbs. Track findability metrics: time to find (<30 sec), success rate (≥70%), search vs. browse balance (40-60% each).
+
+**Advanced techniques** include mental model research (card sort, interviews), cross-cultural adaptation, governance frameworks, personalization, and voice interface design.
+
+**The goal**: Users can predict where information lives and find it quickly, regardless of access method.
--- a/skills/information-architecture/resources/template.md
+++ b/skills/information-architecture/resources/template.md
@@ -0,0 +1,395 @@
+# Information Architecture Templates
+
+Quick-start templates for content audits, card sorting, sitemaps, tree testing, and taxonomy design.
+
+---
+
+## Template 1: Content Audit
+
+**When to use**: Before redesigning navigation, need inventory of existing content
+
+### Content Audit Spreadsheet Template
+
+| URL/ID | Title | Content Type | Category | Metadata | Last Updated | Owner | Status | Action | Notes |
+|--------|-------|--------------|----------|----------|--------------|-------|--------|--------|-------|
+| /about | About Us | Page | Company | meta-desc, img | 2024-01-15 | Marketing | Keep | Update copy | Outdated team bios |
+| /api/auth | Authentication API | Docs | Developer | tags: auth, jwt | 2024-06-01 | Eng | Keep | None | Current |
+| /blog/old-post | Old Post | Article | Blog | tags: news | 2020-03-10 | Marketing | **Remove** | Archive | 4 years old, irrelevant |
+| /products/widget-a | Widget A | Product | Products | price, sku | 2024-05-20 | Sales | Keep | Recategorize | Should be in "Tools" not "Products" |
+| /help/faq-duplicate | FAQ | Help | Support | tags: help | 2023-12-01 | Support | **Merge** | With /help/faq | Duplicate content |
+
+### Audit Analysis Template
+
+**Total Content Items**: [Count]
+
+**By Type**:
+- Pages: [Count] ([%])
+- Docs: [Count] ([%])
+- Articles: [Count] ([%])
+- Products: [Count] ([%])
+- Other: [Count] ([%])
+
+**By Status**:
+- Keep as-is: [Count] ([%])
+- Update needed: [Count] ([%])
+- Recategorize: [Count] ([%])
+- Merge: [Count] ([%])
+- Remove/Archive: [Count] ([%])
+
+**Quality Issues**:
+- Outdated (>2 years): [Count] items
+- Missing metadata: [Count] items
+- Duplicates: [Count] items
+- Broken links: [Count] items
+- Inconsistent labeling: [Count] items
+
+**Current Performance** (from analytics):
+- Most visited pages: [List top 5]
+- Least visited pages: [List bottom 5]
+- High bounce rate pages (>60%): [List]
+- Search queries with no results: [List top 10]
+- Common navigation paths: [List top 3]
+
+**Key Findings**:
+1. [Finding 1: e.g., "40% of content is outdated"]
+2. [Finding 2: e.g., "Users search for 'pricing' but it's buried 4 levels deep"]
+3. [Finding 3: e.g., "15% of content is duplicated across sections"]
+
+**Recommendations**:
+1. [Recommendation 1]
+2. [Recommendation 2]
+3. [Recommendation 3]
+
+---
+
+## Template 2: Card Sorting Study
+
+**When to use**: Understanding user mental models for content organization
+
+### Card Sorting Plan Template
+
+**Study Type**: [Open / Closed / Hybrid]
+
+**Research Question**: "How do [target users] naturally group and label [content type]?"
+
+**Participants**:
+- Target: [Number, e.g., "20 users"]
+- Recruitment criteria: [e.g., "Active customers who use product 2+ times/week"]
+- Incentive: [e.g., "$25 gift card"]
+
+**Cards** ([Number] total):
+1. [Card 1 name/title]
+2. [Card 2 name/title]
+3. [Card 3 name/title]
+...
+[List all cards — aim for 30-60 cards, representative sample of content]
+
+**Pre-Defined Categories** (for Closed/Hybrid only):
+1. [Category 1]
+2. [Category 2]
+3. [Category 3]
+...
+
+**Tool**: [e.g., "OptimalSort", "UserZoom", "Miro"]
+
+**Instructions for Participants**:
+
+```
+You'll see [X] cards representing different [content/features/topics] on our [website/app].
+
+Your task:
+1. Read each card
+2. Group cards that belong together
+3. Name each group you create
+4. You can create as many groups as you need (typically 5-10 groups work well)
+
+There are no right or wrong answers. We want to understand how YOU think about organizing this content.
+
+Time estimate: 15-20 minutes
+```
+
+### Card Sort Analysis Template
+
+**Participation**:
+- Completed: [X] / [Y] participants ([Z]% completion rate)
+- Average time: [X minutes]
+
+**Category Analysis**:
+
+| Category Label | # Users Who Created It | % Agreement | Cards Most Often Included | Alternative Labels Used |
+|----------------|------------------------|-------------|---------------------------|------------------------|
+| Getting Started | 18 / 20 | 90% | "Sign Up", "First Login", "Quick Start" | "Onboarding", "Setup" |
+| Reports | 16 / 20 | 80% | "Analytics", "Dashboard", "Metrics" | "Insights", "Data" |
+| Settings | 19 / 20 | 95% | "Profile", "Preferences", "Account" | "Configuration", "Options" |
+
+**Dendogram Insights** (hierarchical clustering):
+- **Strong clusters** (>70% agreement): [List categories with high consensus]
+- **Weak clusters** (<50% agreement): [List categories with low consensus — these need clarification]
+- **Outlier cards**: [Cards that don't fit anywhere — may need reconsideration]
+
+**Labeling Insights**:
+- **Most common labels**: [e.g., "Help" (15 users), "Support" (12 users), "Assistance" (3 users)]
+- **Terminology conflicts**: [e.g., "Users say 'Reports' but we call them 'Analytics'"]
+- **Jargon to avoid**: [e.g., "Synergistic Solutions" — 0 users used this term]
+
+**Key Findings**:
+1. [Finding 1: e.g., "Users group by task, not by product feature"]
+2. [Finding 2: e.g., "'Settings' is universally understood (95% agreement)"]
+3. [Finding 3: e.g., "'Modules' label confuses users — split between 'Features' and 'Tools'"]
+
+**Recommended Categories** (based on card sort):
+1. [Category 1] — [Brief description]
+2. [Category 2] — [Brief description]
+3. [Category 3] — [Brief description]
+...
+
+---
+
+## Template 3: Sitemap & Navigation Structure
+
+**When to use**: Documenting information hierarchy after card sort analysis
+
+### Sitemap Template
+
+```
+Homepage
+├── [Category 1]
+│   ├── [Subcategory 1.1]
+│   │   ├── [Page 1.1.1]
+│   │   └── [Page 1.1.2]
+│   ├── [Subcategory 1.2]
+│   │   ├── [Page 1.2.1]
+│   │   └── [Page 1.2.2]
+│   └── [Subcategory 1.3]
+│
+├── [Category 2]
+│   ├── [Subcategory 2.1]
+│   ├── [Subcategory 2.2]
+│   └── [Subcategory 2.3]
+│
+├── [Category 3]
+│   ├── [Page 3.1]
+│   ├── [Page 3.2]
+│   └── [Page 3.3]
+│
+└── [Category 4]
+    ├── [Subcategory 4.1]
+    │   ├── [Page 4.1.1]
+    │   └── [Page 4.1.2]
+    └── [Subcategory 4.2]
+```
+
+### Example: E-commerce Sitemap
+
+```
+Homepage
+├── Shop by Category (Electronics, Clothing, Home & Garden)
+├── Shop by Occasion (Daily Essentials, Special Occasions, Gifts)
+├── Deals & Promotions (Today's Deals, Clearance, Bundles)
+└── Support (Help Center, Track Order, Returns, Contact Us)
+```
+
+### Navigation Specification Template
+
+**Primary Navigation** (Top-level categories):
+- [Category 1]: [Description, user goal, target audience]
+- [Category 2]: [Description, user goal, target audience]
+- [Category 3]: [Description, user goal, target audience]
+
+**Secondary Navigation** (Utility links):
+- [Link 1]: [e.g., "Search", "Cart", "Account"]
+- [Link 2]: [e.g., "Help", "Contact"]
+
+**Faceted Filters** (for browse/search):
+- **Facet 1**: [Name] — Options: [Option A, Option B, Option C]
+- **Facet 2**: [Name] — Options: [Option A, Option B, Option C]
+- **Facet 3**: [Name] — Options: [Option A, Option B, Option C]
+
+**Breadcrumbs**: [Show path from homepage to current page]
+- Example: `Home > [Category] > [Subcategory] > [Current Page]`
+
+**Related Links**: [Contextual links on each page]
+- "Related Articles"
+- "You Might Also Like"
+- "Next Steps"
+
+**Search**:
+- Placement: [e.g., "Global header, every page"]
+- Features: [e.g., "Autocomplete, filters, recent searches"]
+- Best bets: [Featured results for common queries]
+
+---
+
+## Template 4: Tree Testing Script
+
+**When to use**: Validating navigation structure before building
+
+### Tree Test Plan Template
+
+**Research Question**: "Can users find [X] in our proposed navigation structure?"
+
+**Participants**: [20-50 users matching target audience]
+
+**Navigation Tree** (text-only structure):
+
+```
+Home
+  Getting Started
+    Sign Up
+    First Login
+    Quick Start Guide
+  Features
+    Reports
+    Dashboards
+    Alerts
+  Settings
+    Profile
+    Preferences
+    Billing
+  Support
+    Help Center
+    Contact Us
+    FAQ
+```
+
+**Tasks** (8-12 tasks typical):
+
+1. **Task 1**: "You want to [user goal]. Where would you find [content]?"
+   - **Correct destination**: [Path, e.g., "Features > Reports"]
+   - **Why this task**: [e.g., "Most common user action"]
+
+2. **Task 2**: "You need to [user goal]. Where would you look?"
+   - **Correct destination**: [Path]
+   - **Why this task**: [Rationale]
+
+3. **Task 3**: [Task description]
+   - **Correct destination**: [Path]
+   - **Why this task**: [Rationale]
+
+[Continue for 8-12 tasks covering key content/features]
+
+### Tree Test Results Template
+
+**Overall Performance**:
+- **Success rate**: [X]% (Target: ≥70%)
+- **Directness**: [X]× optimal path (Target: ≤1.5×)
+- **Average time**: [X] seconds
+
+**Task-Level Results**:
+
+| Task | Success Rate | Directness | Avg Time | Top Incorrect Paths | Notes |
+|------|--------------|------------|----------|---------------------|-------|
+| Task 1 | 85% | 1.2× | 12 sec | None | Good |
+| Task 2 | 45% | 2.5× | 35 sec | "Settings > Profile" (30%), "Support > Help" (25%) | **PROBLEM**: Users confused by label |
+| Task 3 | 75% | 1.4× | 18 sec | "Features > Dashboards" (20%) | Minor issue |
+
+**Problem Areas**:
+
+| Issue | Evidence | Impact | Recommendation |
+|-------|----------|--------|----------------|
+| "Reports" label unclear | Task 2 only 45% success, users looked in "Dashboards" and "Settings" | High | Rename to "Analytics" or merge with "Dashboards" |
+| "Settings" too broad | Tasks 4 & 5 both low success (50%, 55%) | Medium | Split into "Account Settings" and "App Preferences" |
+| Deep nesting in "Support" | Task 7: 65% success, 2.8× directness | Medium | Flatten: move "FAQ" to top level |
+
+**Recommended Changes**:
+1. [Change 1: e.g., "Rename 'Reports' to 'Analytics'"]
+2. [Change 2: e.g., "Split 'Settings' into 'Account' and 'Preferences'"]
+3. [Change 3: e.g., "Move 'FAQ' from Support > Help Center > FAQ to top-level Support > FAQ"]
+
+**Next Steps**:
+- [ ] Implement recommended changes
+- [ ] Re-run tree test on updated structure
+- [ ] Proceed to wireframes once success rate ≥70% on all tasks
+
+---
+
+## Template 5: Taxonomy & Metadata Schema
+
+**When to use**: Designing classification systems, tags, and content attributes
+
+### Taxonomy Design Template
+
+**Content Type**: [e.g., "Product", "Article", "Documentation Page"]
+
+**Primary Taxonomy** (Hierarchical categories):
+```
+[Top-Level Category 1]
+├── [Sub-category 1.1]
+├── [Sub-category 1.2]
+└── [Sub-category 1.3]
+
+[Top-Level Category 2]
+├── [Sub-category 2.1]
+└── [Sub-category 2.2]
+
+[Top-Level Category 3]
+```
+
+**Facets** (Orthogonal dimensions for filtering):
+- **Facet 1**: [Name] — Values: [Value A, Value B, Value C]
+- **Facet 2**: [Name] — Values: [Value A, Value B, Value C]
+- **Facet 3**: [Name] — Values: [Value A, Value B, Value C]
+
+**Tags** (Flexible, user-defined or preset):
+- Preset tags: [List of controlled vocabulary tags]
+- User-generated tags: [Yes/No]
+- Tag moderation: [Yes/No, process]
+
+**Metadata Schema**:
+
+| Field | Type | Required | Values/Format | Purpose |
+|-------|------|----------|---------------|---------|
+| Title | Text | Yes | Plain text, max 60 chars | Display in nav, SEO |
+| Description | Text | No | Plain text, max 160 chars | SEO meta description |
+| Category | Select | Yes | [List of categories] | Primary classification |
+| Tags | Multi-select | No | [Controlled vocabulary] | Cross-category findability |
+| Author | Text | Yes | Name or ID | Attribution, filtering |
+| Last Updated | Date | Yes | YYYY-MM-DD | Freshness indicator |
+| Audience | Select | Yes | [e.g., "Beginner", "Advanced"] | Personalization |
+| Content Type | Select | Yes | [e.g., "Tutorial", "Reference", "FAQ"] | Filtering, display |
+| Status | Select | Yes | [e.g., "Draft", "Published", "Archived"] | Governance |
+
+### Example: Documentation Taxonomy
+
+**Primary Taxonomy**:
+```
+Getting Started
+├── Installation
+├── Configuration
+└── First App
+
+Guides
+├── How-To Guides
+├── Tutorials
+└── Best Practices
+
+Reference
+├── API Documentation
+├── CLI Reference
+└── Configuration Files
+
+Troubleshooting
+├── Common Issues
+├── Error Messages
+└── FAQ
+```
+
+**Facets**:
+- **Product Version**: v1.0, v2.0, v3.0
+- **Difficulty Level**: Beginner, Intermediate, Advanced
+- **Time to Complete**: <10 min, 10-30 min, >30 min
+
+**Metadata** (for each doc page):
+- Title, Description, Category, Tags, Author, Last Updated, Audience, Content Type, Product Version, Difficulty, Est. Time
+
+---
+
+## Quick Reference: When to Use Each Template
+
+| Template | Use Case | Deliverable |
+|----------|----------|-------------|
+| **Content Audit** | Before redesign, need inventory | Spreadsheet with all content, status, recommendations |
+| **Card Sorting** | Don't know how users categorize content | Category labels, groupings, terminology insights |
+| **Sitemap** | After card sort, document structure | Visual hierarchy diagram |
+| **Tree Testing** | Validate navigation before building | Success rates, problem areas, iteration recommendations |
+| **Taxonomy & Metadata** | Design classification system | Taxonomy structure, metadata schema, controlled vocabulary |