AI Agent Workflow Collection

Accessibility analysis from session data — keyboard-only navigation patterns, focus order issues, screen reader compatibility, and accessibility regressions. Use when auditing a11y compliance, investigating accessibility bugs, or validating inclusive design.

Create and manage Fullstory timeline annotations — deploy markers, experiment start/end, incident windows, and release notes. Use when the user wants to mark a deploy, experiment, incident, or any notable event on the Fullstory timeline.

Monitor API call patterns from session data — endpoint latency, error rates, popular endpoints, and API-driven UX issues. Use when investigating backend performance impact on UX, API errors affecting users, or endpoint usage patterns.

Establish performance baselines and benchmarks — compare current metrics to historical averages, set targets, and track progress over time. Use when setting KPIs, establishing baselines, or measuring against goals.

Track marketing campaign performance — UTM-tagged traffic, landing page conversion, campaign cohort behavior, and ROI signals. Use when analyzing campaign performance, measuring landing page effectiveness, or comparing marketing channels.

Detect what changed in the product by comparing before and after a deploy or date — find new behaviors, new errors, changed user flows, and unexpected side effects that weren't in the release notes. Use after a deploy or when the user suspects something changed but doesn't know what.

Advanced cohort management — build, compare, and track user cohorts over time. Use when working with persistent user groups (enterprise vs free, new vs returning, by country or plan) across multiple analyses.

How to structure A vs B comparisons in Fullstory — when to use dimensionality (event/session properties) vs separate segments (user-level properties), and why the distinction matters for correctness.

Post-deploy validation — check errors, conversion, and frustrations before and after a deploy timestamp. Use when the user deploys code and wants to know if anything broke.

Investigate JavaScript errors, console errors, network failures, and crashes in production. Use when debugging user-reported issues, tracking error frequency, finding sessions with specific errors, or determining whether a bug is widespread or isolated.

Track feature adoption — who's using the new feature, how deeply, are they sticking with it, and what's blocking adoption. Use when launching a new feature, measuring feature engagement, or understanding feature usage patterns.

Form field-level analysis — abandonment by field, validation errors, time-to-complete, and which fields cause users to leave. Use when investigating form performance, signup drop-off, or checkout form friction.

Finds and ranks user frustration signals — rage clicks, dead clicks, form abandonment, error spikes. Use when asking "what's frustrating users", "where are users struggling", or "what's the biggest UX problem right now". Uses Fullstory's opportunity detection, then drills into sessions for root cause.

Conversion funnel analysis — find where users drop off, quantify the loss, and diagnose why. Use when asking about conversion rates, funnel abandonment, checkout completion, signup flow performance, or "where are we losing users.

Fullstory analytics workflow. Use when answering a question that requires measuring user behavior — counts, rates, trends, breakdowns, or cohort comparisons. Builds segments and metrics, computes results, then investigates sessions to explain what the numbers mean.

Generate click and interaction heatmaps from session data — visualize where users click, tap, and hover on any page. Use when asking "what do users click on most", "are users clicking non-clickable elements", or "where should we place the CTA".

Create detailed bug reports from session evidence — auto-populate Jira tickets with session URLs, error messages, device info, and reproduction steps. Use when the user wants to file a bug from a Fullstory session.

Analyze the user lifecycle — new, active, at-risk, and churned users. Use when understanding user progression, identifying at-risk users, or measuring lifecycle conversion rates.

Audit existing metrics and segments for staleness, duplicates, bad configuration, and naming issues. Use when the user wants to clean up their Fullstory account, find redundant objects, or understand what already exists.

Mobile-specific UX analysis — touch interactions, viewport issues, scroll behavior, mobile vs desktop patterns, and responsive design validation. Use when investigating mobile experience, responsive design bugs, or mobile conversion rates.

Behavioral proxy for user satisfaction — use frustration signals, engagement patterns, and conversion behavior as a real-time NPS alternative. Use when the user wants to measure satisfaction but doesn't have survey data in Fullstory.

Interactive onboarding guide for new Fullstory MCP users — teaches the mental model, walks through first queries, and explains how to use each skill. Use when a new team member needs to learn how to use Fullstory through Claude.

Page-level performance analysis — load times, errors by page, device breakdown, and page-to-page navigation patterns. Use when asking about specific page health, comparing pages, or finding pages with performance issues.

Build behavioral user personas from session data — identify distinct user types by their actual behavior, not demographics. Use when the team needs data-driven personas, wants to understand who their users really are, or needs to align product decisions to real user behavior.