---
name: platform-ux-pattern-identifier
description: "Analyzes a set of product surfaces, flows, or components and surfaces repeating UX patterns, inconsistencies, and design debt — producing a pattern inventory, a debt register ranked by leverage, and consolidation recommendations. Use when auditing a platform for interaction consistency, preparing a design system investment case, or diagnosing why surfaces feel disjointed across a growing product."
argument-hint: "[surfaces] [product-context]"
allowed-tools: ["Read"]
---

# Platform UX Pattern Identifier

## Purpose
Analyze a collection of product surfaces or data flows to surface the interaction patterns that repeat, the ones that quietly diverge, and the design debt worth paying down — producing a pattern inventory and a leverage-ranked debt register that tells a design lead exactly where consolidation returns the most consistency per unit of effort.

## When to Use
Invoke this skill when the user asks to:
- Audit a platform or product for UX consistency across surfaces
- Identify repeating interaction patterns to codify into a design system
- Find where similar tasks are handled inconsistently across the product
- Build the case for a design system or component library investment
- Diagnose why a growing product feels fragmented or "stitched together"
- Prioritize design debt paydown before a major redesign or platform expansion

## Inputs Required
Before starting, collect:
- **Surfaces in scope**: The screens, flows, components, or data flows to analyze (list, descriptions, or references)
- **Product type**: Platform, infrastructure product, admin console, developer tool, internal tooling, etc.
- **Primary user roles**: Who uses these surfaces and what they're trying to accomplish
- **Known pain points**: Any consistency complaints, support themes, or design friction already surfaced
- **Existing system maturity**: Design system status — none, informal, partial, mature
- **Constraints**: Team size, upcoming roadmap pressure, or platform areas that are off-limits to change

If existing system maturity or constraints are missing, assume an informal/partial system and a resource-constrained team, and note the assumption.

## Instructions

### Step 1: Decompose Surfaces Into Interaction Primitives
Break each surface down into its constituent UX patterns rather than treating screens as monoliths. For every surface, tag which primitives it contains:
- **Navigation & wayfinding**: Global nav, breadcrumbs, tabs, sidebars, back/cancel behavior
- **Data display**: Tables, lists, cards, detail views, empty states, density
- **Input & configuration**: Forms, inline editing, wizards, filters, search
- **Feedback & status**: Loading, success/error, toasts, validation, confirmations
- **Action patterns**: Primary/secondary CTAs, bulk actions, destructive confirmations, undo
- **State & flow control**: Progressive disclosure, modals vs. inline, save models (autosave vs. explicit)

Produce a coverage matrix: surfaces as rows, primitive categories as columns, noting how each surface handles each primitive.

### Step 2: Cluster Into Repeating Patterns
Group instances that are solving the same interaction problem. For each cluster, record:
- **Pattern name**: A neutral, descriptive label (e.g., "list-to-detail drill-down")
- **Where it appears**: The surfaces using it
- **Variant count**: How many distinct implementations exist for the same job
- **Convergence status**: Consistent / Drifting / Fragmented

A pattern with one implementation across many surfaces is a codification candidate. A pattern with many implementations of the same job is a debt candidate.

### Step 3: Identify Inconsistencies and Their Cost
For each Drifting or Fragmented pattern, characterize the divergence and its impact:
- **Type of inconsistency**: Visual (looks different), behavioral (acts different), structural (organized differently), or semantic (same word/icon means different things)
- **User cost**: Relearning, error risk, broken mental model, trust erosion
- **Team cost**: Duplicated build/maintenance, unclear source of truth, slowed onboarding
- **Severity**: High (behavioral/semantic inconsistency on high-traffic flows) → Low (cosmetic variance on rarely-used surfaces)

Behavioral and semantic inconsistencies are always more expensive than visual ones — rank accordingly.

### Step 4: Build the Design Debt Register
Compile each debt item as a structured entry:
- **Item**: What the debt is
- **Pattern affected**: Which pattern cluster
- **Surfaces impacted**: Count and names
- **Inconsistency type & severity**
- **Leverage score**: High / Medium / Low — a function of (surfaces impacted × user traffic × severity) ÷ estimated effort to resolve

Sort the register by leverage descending. Leverage — not severity alone — drives the ranking, because a high-severity issue on one obscure surface loses to a medium-severity issue replicated across twenty.

### Step 5: Recommend Consolidation Moves
For the top leverage items, specify the resolution:
- **Canonical choice**: Which existing variant should become the standard, and why (favor the most robust, accessible, and traffic-proven implementation)
- **Codification path**: Component, pattern guideline, or documented convention
- **Migration approach**: Big-bang vs. incremental; which surfaces to convert first
- **Guardrail**: What prevents re-divergence (component, lint rule, review checkpoint, ownership)

### Step 6: Frame the Investment Case
Summarize what codifying the top patterns buys the team: consistency gains, maintenance reduction, and onboarding/velocity effects. Name the single highest-leverage consolidation to start with and why. Be opinionated about sequencing.

## Output Format
- **Structure**: Coverage matrix → pattern clusters → inconsistency analysis → leverage-ranked debt register → consolidation recommendations → investment summary
- **Length**: 1,000–1,400 words
- **Format**: Tables for the coverage matrix and debt register; structured bullets for pattern clusters and recommendations; short prose for the investment case
- **Sections**: Primitive Coverage Matrix → Repeating Pattern Inventory → Inconsistency & Cost Analysis → Design Debt Register (ranked) → Consolidation Recommendations → Investment Case & Starting Point
- **Tone**: Design systems lead — systems-first, evidence-driven, decisive about tradeoffs; never hand-wavy about "consistency" without naming the specific divergence

## Quality Checklist
Before delivering output, verify:
- [ ] Every surface in scope appears in the coverage matrix across all primitive categories
- [ ] Each pattern cluster records its variant count and convergence status
- [ ] Every debt register item has an inconsistency type, severity, and leverage score
- [ ] The debt register is sorted by leverage, not by severity or arbitrary order
- [ ] Each top recommendation names a canonical variant and a re-divergence guardrail
- [ ] The investment case names one specific highest-leverage starting move — no hedging

## Notes
- Semantic inconsistencies (same icon or label meaning different things across surfaces) are the most under-reported and most damaging — check for them explicitly, not just visual variance.
- On infrastructure and developer-facing products, behavioral consistency (how save, undo, and destructive actions work) matters more than visual polish; weight severity accordingly.
- The most defensible starting move is usually the pattern with the highest variant count on high-traffic flows — it maximizes consistency gained per component built.
- If surfaces are described only vaguely, ask one clarifying question: "Which two surfaces feel the most inconsistent with each other today?" — that pair usually reveals the deepest debt.

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## How to use this skill

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