analyze-chart

name	analyze-chart
description	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.
suggest_when	User asks "why did this metric move", "explain this chart", shares a chart URL, or wants to investigate a spike, drop, or anomaly in a specific metric.

Chart Deep Dive

When to Use

• A metric spiked or dropped unexpectedly
• You need to understand what’s driving a trend
• Preparing a detailed, evidence-backed analysis for stakeholders
• Investigating differences between user or event segments

Instructions

Step 0: Identify the Chart

• Accept a chart URL or chart ID
• If the user provides a URL, use Amplitude:getting_data_from_url to extract the chart ID
• If no chart identifier is provided, ask explicitly for the chart URL or ID and stop

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Step 1: Retrieve and Validate Chart Data (Mandatory)

• Use Reading chart data to retrieve the chart definition and data
• If chart data cannot be retrieved or is empty, do not proceed
  • Explain what’s missing (time range, event, filters, permissions)
  • Ask the user to correct the chart or provide a valid chart

Capture and restate:

• Metric being measured
• Time range and granularity
• Chart type (e.g. time series, funnel, retention)
• Existing filters, segments, or breakdowns

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Step 2: Identify the Pattern and Change Window

Use Analyzing chart to characterize what’s happening:

• Spike / Drop: Sudden change on specific date(s)
• Trend: Gradual increase or decrease over time
• Seasonality: Recurring weekly or monthly patterns
• Anomaly: Deviation from recent baseline or historical behavior

Explicitly identify:

• The window of change (start/end)
• Direction and magnitude of the change
• Baseline period used for comparison (default: previous equal-length period)

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Step 3: Investigate Likely Drivers (Bounded)

Instead of broad slicing, use guided segmentation:

1. Use Finding the right event properties to identify the most relevant properties for explaining the change
2. Select up to 9 high-signal properties (e.g. platform, country, plan, version)
3. Re-run Analyzing chart with these properties in mind to determine:
   • Which segments contribute most to the change
   • Whether the pattern is localized or broad-based
   • Only fetch up to 3 charts at a time when using Amplitude:query_charts

Avoid testing more than 9 properties in aggregate unless the user explicitly asks for deeper exploration.

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Step 4: Correlate with Context (Required for Anomalies)

For spikes, drops, or unexpected shifts, gather contextual signals in the same timeframe:

• Use Getting experiments to identify active experiments or flags
• Use Getting deployments to identify releases or rollouts
• Use Searching for content to surface annotations or relevant documentation
• Use Amplitude:get_feedback_insights to search customer feedback trends that might explain the change
• Use Amplitude:get_feedback_mentions to pull in specific customer mentions if there's a likely feedback trend tied to what's being explained.

Determine whether any contextual changes align temporally with the chart pattern.

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Step 5: Synthesize Findings

Present a structured, decision-ready analysis:

1. What Happened
   Clear description of the observed pattern and magnitude

2. When
   Exact timeframe and comparison baseline

3. Primary Hypothesis
   Most likely explanation based on chart data and contextual signals

4. Supporting Evidence

   • Key metrics
   • Segment contributions
   • Relevant experiments, deployments, or annotations

5. Alternative Explanations
   1–3 plausible alternatives and why they are less likely

6. Impact
   Quantify impact where possible (users, events, conversion, revenue proxy)

7. Recommended Next Step
   One clear follow-up action (e.g. deeper segment, experiment review, instrumentation check)

Always include:

• Chart name
• Chart ID
• Link back to the chart
• Coverage (e.g. properties tested, segments analyzed)

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Best Practices

• Always compare against a clear baseline period
• Distinguish observations from hypotheses
• Prefer high-signal segmentation over exhaustive slicing
• Note data quality issues (low volume, incomplete periods, heavy “(none)” values)
• Do not create or edit charts unless the user explicitly asks