Mobile Commerce Ux Auditor
v1.0.0Audit mobile shopping flows, identify conversion friction, prioritize UX issues, and suggest experiment backlogs for product teams, CRO specialists, and e-co...
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Mobile Commerce UX Auditor
Overview
Use this skill to convert mobile shopping-flow notes into a commerce-specific UX audit. It focuses on common conversion blockers in mobile homepages, search, PDPs, carts, checkouts, and reorder flows.
This MVP is heuristic. It does not analyze real heatmaps, session replays, or analytics APIs. Instead, it applies a structured mobile-commerce checklist and produces a markdown audit that can feed a growth backlog.
Trigger
Use this skill when the user wants to:
- audit a mobile storefront or app shopping flow
- diagnose low add-to-cart or checkout completion
- identify quick-win conversion fixes
- turn screenshots or flow notes into a prioritized UX backlog
- generate experiment ideas for A/B testing or redesign sprints
Example prompts
- "Audit our mobile checkout flow"
- "Review these PDP screenshots for conversion friction"
- "Why is mobile add-to-cart low on our skincare store?"
- "Create a quick mobile commerce UX audit from these notes"
Workflow
- Capture the business goal and the stages the user is concerned about.
- Map the input to mobile-commerce stages such as landing, PDP, cart, and checkout.
- Apply a stage-specific issue library around clarity, trust, hierarchy, form burden, and tap friction.
- Prioritize findings by likely revenue impact and implementation effort.
- Return a markdown audit with findings, quick wins, and experiment ideas.
Inputs
The user can provide any mix of:
- screenshots or textual descriptions of the mobile flow
- funnel goal, such as conversion lift or checkout drop-off reduction
- business context and target market
- known symptoms, such as hidden shipping info or low CTA visibility
- notes about device assumptions or audience
Outputs
Return a markdown audit with:
- executive summary
- prioritized UX findings by stage
- quick wins
- experiment backlog
- assumptions and evidence gaps
Safety
- Do not claim access to real analytics or behavioral tools.
- Treat recommendations as heuristics until validated with data.
- Avoid guaranteed uplift claims.
- State clearly when stage coverage is incomplete.
Examples
Example 1
Input: mobile PDP and checkout notes for a fashion store.
Output: highlight weak size guidance, delayed shipping clarity, and low-trust checkout signals, then propose specific fixes and tests.
Example 2
Input: mobile grocery reorder flow.
Output: identify tap friction, reorder-path ambiguity, and cart-message overload, then convert them into a quick-win backlog.
Acceptance Criteria
- Return markdown text.
- Cover at least 4 mobile-commerce findings or stages.
- Prioritize issues by severity or impact.
- Include at least 3 experiment or quick-win ideas.
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