amplitude-copilot-plugin

Summarizes B2B account health by analyzing usage patterns, engagement trends, risk signals, and expansion opportunities. Use for customer success reviews, renewal preparation, QBRs, or account prioritization.

Analyzes what users ask AI agents about and how well each topic is served. Only use when the user has Amplitude Agent Analytics instrumented in their project. Use when the user asks "what are people asking the AI", "top AI topics", "where is the AI struggling", "AI coverage gaps", "what should we improve in our AI", or wants product insights from AI conversation patterns.

Performs deep analysis of a specific Amplitude chart to explain trends, anomalies, and likely drivers. Use when a metric looks unusual, investigating a spike or drop, or understanding the "why" behind numbers.

Deeply analyze Amplitude dashboards by analyzing key charts, surfacing top areas for concern and takeaways, identify anomalies, then explain changes using customer feedback trends

Designs A/B tests with proper metrics and variants, analyzes running or completed experiments, and interprets results with statistical rigor. Use when setting up experiments, checking experiment status, analyzing results, or making ship decisions.

Synthesizes customer feedback into actionable themes including feature requests, bugs, pain points, and praise. Use when planning product roadmap, understanding user sentiment, investigating specific issues, or preparing voice-of-customer reports.

Compares how two user groups behave differently by analyzing Amplitude Session Replays and metrics side by side. Produces a behavioral diff showing what one group does that the other doesn't. Use when a PM or growth lead asks "what do converters do differently", "how do power users behave", "why do churned users leave", "compare these cohorts", "what separates segment A from B", or wants to understand the qualitative behavioral gap between two user populations.

Creates Amplitude charts from natural language descriptions, handling event selection, filters, groupings, and visualization choices. Use when you know what you want to measure but prefer not to build the chart manually.

Builds comprehensive Amplitude dashboards from requirements or goals, organizing charts into logical sections with appropriate layouts. Use when creating a complete dashboard from scratch or assembling existing charts into a cohesive view.

Delivers a daily briefing of the most important changes across your Amplitude instance. Use when the user asks for a "daily download", "morning briefing", "what's happening", "anything I should know", or wants a summary of recent metric changes, experiments, and user feedback.

Turns bug reports into reproducible steps by finding error sessions in Amplitude Session Replay, extracting interaction timelines, and identifying the common action sequence that precedes the failure. Use when a user reports a bug, an error event spikes, someone says "how do I reproduce this", "what happened to user X", "repro steps", or you need to understand what a user did before an error occurred.

Investigates errors across network failures, JavaScript errors, and error clicks to identify what's broken, where, and why. Use when the user says "what's broken", "errors are up", "why are users seeing errors", "JS errors", "network failures", "5xx spike", "something is broken", or wants to triage product reliability issues.

Discovers how analytics tracking calls are actually written in this codebase — the concrete SDK calls, function signatures, and import patterns used to send events. Use this skill whenever you need to understand the existing analytics instrumentation patterns before adding new tracking, when someone asks "how do we track events here?", "show me the analytics setup", "what's the analytics pattern in this codebase?", or any time the instrument-events or discover-event-surfaces skills are about to run and you need to know the correct coding style to follow. Outputs a deduplicated list of patterns with generalized examples and the file paths where each pattern appears, plus the dominant event and property naming conventions inferred from those call sites. Always use this skill before writing any analytics instrumentation code.

Given a change_brief YAML (output from diff-intake), generates an exhaustive list of candidate analytics events to instrument. Takes the perspective of an engineer with a PM mindset — surfaces everything worth considering so a PM can decide what actually matters. Use this as step 2 of the analytics instrumentation workflow, immediately after diff-intake produces a change_brief. Trigger whenever a user has a change_brief YAML and wants to know what analytics events to add, or asks "what should I track for this PR", "what events should I instrument", "generate event candidates", or any request to surface analytics coverage gaps for a code change.

Given event_candidates YAML (output from discover-event-surfaces), generates a concrete instrumentation plan for priority-3 (critical) events. Acts as a Software Architect: discovers existing analytics patterns in the codebase, reads the hinted files to determine what variables are in scope, designs minimal chart-useful properties, and identifies the exact insertion point for each tracking call. Outputs a structured JSON trackingPlan. Use this as step 3 of the analytics instrumentation workflow, after discover-event-surfaces. Trigger whenever a user has event_candidates and wants to generate tracking code, asks "instrument these events", "generate tracking plan", "add analytics for these events", "where should I put the tracking calls", or any request to turn event candidates into concrete implementation guidance.

Deep-dives into specific AI agent sessions or failure patterns to explain why something went wrong. Only use when the user has Amplitude Agent Analytics instrumented in their project. Use when investigating a specific session ID, debugging agent failures, understanding why quality is low, tracing tool errors, or when monitor-ai-quality surfaces an issue that needs root cause analysis.

Monitors AI agent health across quality, cost, performance, and errors. Only use when the user has Amplitude Agent Analytics instrumented in their project. Use when the user asks "how are our AI agents doing", "AI quality check", "agent health", "AI errors", "agent performance", "LLM cost", or wants a proactive health report on their AI/LLM features.

Monitors all active and recently completed experiments across Amplitude projects, triages them by importance, then runs deep analysis and reporting on the most impactful ones. Use when the user asks to "check on experiments", "experiment status", "experiment review", "what experiments are running", or wants a periodic experiment health report.

Delivers a reliability health check using auto-captured network request, JS error, and error click data. Use when the user asks for a "reliability check", "error rate", "quality metrics", "page health", "did the release break anything", "error budget", or wants a proactive product quality report.

Finds and analyzes Amplitude Session Replays to surface UX friction patterns across multiple sessions. Produces a ranked friction map showing where users struggle, hesitate, or abandon. Use when a PM or designer asks "where's the friction", "what's confusing users", "UX issues on this page", "why is this flow clunky", "audit the user experience", or wants qualitative evidence of usability problems in a specific feature or flow.

Retrieves, synthesizes, and prioritizes all recent AI agent results from Amplitude. Queries every agent type available in get_agent_results, validates freshness, and produces a unified narrative ranked by impact. Use when the user asks "what has the AI found", "show me agent insights", "any AI findings", "what did Amplitude discover", "review AI insights", or wants a digest of everything Amplitude's AI agents have surfaced recently.

Source of truth for event taxonomy generation, data auditing, and governance best practices in Amplitude. Use when an agent needs to create, validate, audit, score, or recommend improvements to event tracking plans, naming conventions, property standards, data quality, or deprecation workflows. Covers naming rules, property standards, scoring frameworks, safe metadata operations, deprecation procedures, and AI readiness guidance.

Delivers a weekly briefing summarizing the most important trends, wins, and risks across your Amplitude instance. Use when the user asks for a "weekly review", "weekly summary", "week in review", "what happened this week", or wants a recap of the past 7 days to share with their team or leadership.

Orchestrate event description audits by delegating chunk work to the event-descriptions-worker subagent. Resolve a project name to projectId via get_context when needed, then spawn worker subagents over cursors for a bounded run window and write outputs into run-scoped directories. Use when auditing missing event descriptions at scale without doing per-event analysis directly in this skill.