analyze-chart
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.
What this skill does
# 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 --- ### 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 --- ### 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) --- ### 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. --- ### 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. --- ### 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) --- ## 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
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