# Replay UX Audit > Synthesize multiple session replays into a UX friction map identifying systemic usability issues. Uses mcp__Amplitude__list_session_replays, mcp__Amplitude__get_session_replay_events, mcp__Amplitude__get_session_replays. - Skill: `kienbui1995/replay-ux-audit` (Agent Skill) - Install (CLI): `npx skillmds@latest add kienbui1995/replay-ux-audit` - Raw SKILL.md: https://api.skillmd.com/api/skills/kienbui1995/replay-ux-audit/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: kienbui1995 (https://skillmd.com/u/kienbui1995) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kienbui1995/replay-ux-audit --- # Replay UX Audit ## When to Use - A product team wants to understand why users struggle with a specific flow before redesigning it - Conversion rate on a key funnel step is lower than expected — need to find the friction - Before a UX research round: use session replays to form hypotheses to test in interviews - After launch of a new feature: is it being used as intended? Where do users get stuck? - When a flow has high bounce/abandon rates but no clear error signals - Quarterly UX review: proactive audit of key flows before they become problems ## Core Jobs ### 1. Define Audit Scope Clarify the focus before sampling sessions. The audit should target one of: - **Specific user flow**: e.g., signup → onboarding → first action (multi-page flow) - **Specific page**: e.g., pricing page (single-page behavior analysis) - **Specific feature**: e.g., search filters, bulk action tool, data export Define: - Entry point (what page/action starts the flow) - Exit point (what constitutes success vs. abandonment) - Target user segment (new users? paid users? mobile-only?) - Time window (last 7 days for recent data, last 30 days for statistical significance) ### 2. Sample 20-50 Relevant Sessions Use `mcp__Amplitude__list_session_replays` to find sessions matching the scope: **Sampling strategy — always mix success and failure:** - ~40% **completed** sessions (users who finished the flow successfully) - ~40% **abandoned** sessions (users who left mid-flow) - ~20% **partial** sessions (users who made progress but didn't complete) This balance is critical: studying only failures shows what's broken, but studying successful sessions reveals what workarounds users invented, which signals non-obvious friction. **Sample size guidance:** - 20 sessions: enough for a quick hypothesis check - 35 sessions: enough for pattern confidence on a specific page - 50 sessions: full audit with quantitative friction estimates ### 3. Watch for Friction Signals For each session, use `mcp__Amplitude__get_session_replay_events` to extract the interaction timeline. **The 5 friction signals — what to look for:** **Rage Clicks** — rapid repeated clicks on the same element ``` Signal: 3+ clicks within 2 seconds on same element Meaning: User expects interaction that isn't happening Examples: disabled button that looks enabled, form submit that silently fails ``` **Dead Clicks** — clicks on non-interactive elements ``` Signal: Click with no page response or state change Meaning: Element looks clickable but isn't; or link is broken Examples: text that looks like a link, icon without hover state, grayed-out element ``` **U-Turns** — backward navigation immediately after forward navigation ``` Signal: User goes to Page B then immediately returns to Page A Meaning: Page B didn't have what they expected, or they made a mistake and noticed immediately Examples: clicking wrong menu item, seeing an unexpected modal, wrong filter applied ``` **Hesitation** — long pause before taking an action (>10s with cursor movement but no click) ``` Signal: Mouse movement without clicks, sustained time on element Meaning: User is unsure what to do next; decision anxiety or unclear affordance Examples: ambiguous CTA labels, multiple similar options, unclear next step ``` **Form Abandonment** — user starts form input then leaves ``` Signal: Focus on form field, then session ends or navigation away Meaning: Form is too long, too confusing, requires unavailable information, or failed Examples: unexpected required fields, confusing validation errors, too many steps ``` ### 4. Build Friction Map After reviewing all sessions, build a structured map of where friction occurs: ``` Flow: Checkout → Payment → Confirmation Step 1: /checkout/cart [friction: LOW] - Clear path forward - Minor: some users don't notice "Apply Coupon" link (dead click on cart total) Step 2: /checkout/shipping [friction: MEDIUM] - 23% of sessions show hesitation on "Same as billing" checkbox - 12% of sessions show u-turn (back to cart, then forward again) - Form validation error on ZIP code triggers rage clicks in 18% of sessions Step 3: /checkout/payment [friction: HIGH] - Rage clicks on "Submit Payment" in 34% of sessions - 41% of abandonment happens at this step - Key signal: users click submit, wait 5s, click again (double submit pattern) Step 4: /checkout/confirmation [friction: NONE] - Sessions that reach here show smooth behavior - Concern: only 59% of sessions that start checkout reach this step ``` ### 5. Quantify Friction Points For each identified friction signal, calculate: - **Frequency**: What % of sampled sessions show this friction? - **Recurrence**: Do the same users encounter this friction multiple times in one session? - **Correlation with abandonment**: What % of sessions with this friction end in abandonment? ``` Friction: Rage clicks on "Submit Payment" - Frequency: 34% of sessions (17/50 sampled) - Recurrence: 71% of affected users click 3+ times - Abandon correlation: 68% of rage-clicking users abandon the session - Estimated impact: 34% × 68% = 23% of checkout sessions lost to this friction ``` ### 6. Prioritize by Frequency × Severity Rank issues using a simple matrix: | Friction Point | Frequency | Severity | Priority Score | Recommendation | |----------------|-----------|----------|---------------|----------------| | Payment double-submit | 34% | High (abandonment) | 9/10 | Fix: show loading state on submit | | ZIP validation rage click | 18% | Medium (frustration) | 6/10 | Fix: inline validation, clear format guide | | Shipping checkbox hesitation | 23% | Low (slows down) | 4/10 | Improve: better label text | | Coupon dead click | 8% | Low (confusion) | 2/10 | Improve: make coupon field more visible | ### 7. Recommend UX Improvements For each high-priority friction point, provide a specific recommendation: **Recommendation format:** - **Problem**: what the user experiences (be concrete — "user clicks Submit 3 times") - **Root cause hypothesis**: why this happens (button doesn't disable after click) - **Recommended fix**: specific UI change (disable button after first click + show spinner) - **Success metric**: how to measure if the fix worked (rage click rate on Submit button) ## MCP Tools - `mcp__Amplitude__list_session_replays` — search and filter sessions for the audit scope (by page, user segment, time range, completion status) - `mcp__Amplitude__get_session_replays` — get session metadata for the sampled set (duration, pages visited, device type) - `mcp__Amplitude__get_session_replay_events` — extract the full interaction timeline per session to identify friction signals ## Key Concepts - **Friction map**: A structured breakdown of where users struggle in a flow, with frequency and severity data - **Rage click**: Rapid repeated clicks on the same element — the strongest signal of user frustration in replay data - **Dead click**: Click with no UI response — indicates mismatched affordance (element looks interactive but isn't) - **U-turn**: Immediate back navigation after forward navigation — signals the destination didn't meet expectations - **Abandon correlation**: The percentage of sessions showing a friction signal that end in task abandonment — the key metric for prioritization - **Friction score**: Frequency × severity, used to rank issues for the roadmap ## Output Format ``` ## UX Audit: Sessions reviewed: N (XX successful, XX abandoned, XX partial) Time window: ### Friction Map by Flow Step ### Top Friction Issues (ranked by priority) **Issue 1:** [Priority: X/10] Frequency: XX% of sessions Signal: Root cause: Fix: Success metric: **Issue 2:** ... ### Summary Metrics - Steps with HIGH friction: N - Estimated sessions lost to friction: XX% - Top 3 improvement opportunities: ### Recommended Next Actions 1. 2. 3. ```