replay-ux-audit

replay-ux-audit is a skill for Claude Code from amplitude/mcp-marketplace. It costs 95 tokens per session (2,716 once invoked), scanned A, original, MIT.

A user-experience review based on Amplitude Session Replays, which are recordings of real product interactions. It examines several sessions for a chosen page or flow and turns repeated problems into a ranked friction map.

In plain words
What is it for?
Finding confusing steps, hesitation, repeated actions, abandonment, and other UX issues in a feature or journey. It can support the findings with interaction timelines, funnel results, and feature-adoption data.
Why use it?
It reduces the time needed to watch recordings manually and connects observed struggles with product-analytics data. This makes it easier to separate one unusual session from a repeated usability problem.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the amplitude plugin — 37 skills shipped together

Good fit Finding confusing steps, hesitation, repeated actions, abandonment, and other UX issues in a feature or journey. It can support the findings with interaction timelines, funnel results, and feature-adoption data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/amplitude/mcp-marketplace/replay-ux-audit
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add amplitude/mcp-marketplace --skill replay-ux-audit
Clone the repo
git clone --depth 1 https://github.com/amplitude/mcp-marketplace

Made for: Claude Code.

Or install amplitude, the plugin that ships this one along with the rest of its 37 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for replay-ux-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/replay-ux-audit/github.svg)](https://agentmods.dev/skills/amplitude/mcp-marketplace/replay-ux-audit)
Your own site
<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/replay-ux-audit"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/replay-ux-audit/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for replay-ux-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/replay-ux-audit"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/replay-ux-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,716 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00095 $0.02716
Opus 5 $0.00048 $0.01358
Sonnet 5 $0.00019 $0.00543
Haiku 4.5 $0.00010 $0.00272

Measured 7d ago against content hash 031c1d71b679, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

replay-ux-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/amplitude/skills/replay-ux-audit/SKILL.md · 217 lines

How it starts

The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Replay UX Audit

Watch 5-10 session replays for a specific feature, page, or flow, then synthesize patterns into a ranked friction map. This skill turns hours of manual replay watching into a structured UX report grounded in real user behavior.


CRITICAL: Tool Reference

Primary tools:

  • Amplitude:get_amp_session_replay_info with action: "search" — Find sessions matching event filters, user properties, or time windows. Use this to target sessions for a specific feature or flow.
  • Amplitude:get_amp_session_replay_info with action: "events" — Decode a replay into an interaction timeline: navigations, clicks, inputs, scrolls. This is what you "watch."

Supporting tools:

  • Available taxonomy reader — Inspect the connected catalog and use its event and property read capabilities to discover valid filter names and scopes. Never guess them.
  • Amplitude:get_amplitude_charts with include: "data" — Pull quantitative context (funnel conversion rates, feature adoption) to anchor the qualitative replay findings.
  • Amplitude:use_amplitude_ai_feedback with facet: "insights" / facet: "mentions" — Cross-reference replay friction with customer feedback themes.

Instructions

Step 1: Define the Audit Scope

Determine what to audit from the user's request:

  • Page or URL pattern: A specific page (e.g., /settings, /checkout)
  • Feature or flow: A multi-step process (e.g., onboarding, report creation)
  • Event-based: Sessions containing a specific event (e.g., "Export Clicked")
  • Broad: "Audit the whole product" — narrow this down. Ask: "Which area would you like me to start with?" Suggest 2-3 areas based on high-traffic pages or known problem areas if you can identify them.

Also determine:

  • Time window: Default to last 14 days unless specified.
  • User segment (optional): Specific plan, platform, cohort, or user type.

Step 2: Get Context and Discover Events

  1. Call Amplitude:get_amplitude_context. If multiple projects, ask which to audit.
  2. Use the available taxonomy event reader to find events related to the target area. Look for:
    • Page view or navigation events for the target area
    • Key interaction events (clicks, form submissions) within the flow
    • Error or failure events that may indicate friction
  3. If the user mentioned a flow or funnel, identify the key step events so you can filter sessions that attempted the flow.

Read the full file on GitHub · 217 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago Changed · +1 lines 031c1d71b679
  2. 13d ago First seen · 216 lines · 95 tokens per session scan A e327b1bc45f8

Subscribe to this mod's changes

replay-ux-audit is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 4d ago), licensed MIT. It adds 95 tokens to every session and 2,716 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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