# Analyze Dashboard > Synthesize an Amplitude dashboard into executive narrative with key findings, trends, and risks. Uses mcp__Amplitude__get_dashboard, mcp__Amplitude__query_charts. - Skill: `kienbui1995/analyze-dashboard` (Agent Skill) - Install (CLI): `npx skillmds@latest add kienbui1995/analyze-dashboard` - Raw SKILL.md: https://api.skillmd.com/api/skills/kienbui1995/analyze-dashboard/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: kienbui1995 (https://skillmd.com/u/kienbui1995) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kienbui1995/analyze-dashboard --- # Analyze Amplitude Dashboard ## When to Use - A stakeholder asks for a summary of what a dashboard is showing - You need to prepare talking points for a weekly or monthly business review - An executive wants a written briefing instead of raw charts - You need to synthesize multiple metrics into a single coherent story - Identifying the most important signal across a dashboard with many charts ## Core Jobs ### 1. Load All Charts Call `mcp__Amplitude__get_dashboard` to retrieve the dashboard configuration and chart list. Then use `mcp__Amplitude__query_charts` to fetch current data for all charts. Do not analyze charts individually in isolation — the goal is synthesis across the entire dashboard. Note: query all charts before drawing conclusions. A metric that looks alarming in isolation may be explained by another chart on the same dashboard. ### 2. Identify the Story Across Metrics Look for the narrative thread connecting the charts: - What is the overall health signal? (Positive momentum, stable, declining, or mixed) - Are the leading indicators consistent with the lagging indicators? - If the North Star metric is up, are the supporting metrics aligned? - If the North Star is flat or down, which upstream metrics explain it? - Are there contradictions across charts that need investigation? The story is not a list of what each chart shows. It is the meaning that emerges when the charts are read together. ### 3. Surface Top 3-5 Findings Ranked by Business Impact Select the most important findings across the entire dashboard. Rank by business impact: - **Impact tier 1**: Changes that affect revenue, retention, or growth rate directly - **Impact tier 2**: Changes that affect leading indicators of revenue or retention (activation rate, feature adoption, engagement depth) - **Impact tier 3**: Changes in operational metrics that may explain tier 1 or 2 findings For each finding, provide: the specific metric, the direction and magnitude of change, the time window, and why it matters to the business. ### 4. Highlight Anomalies and Explain Context Flag any metric that deviates unexpectedly from its trend. For each anomaly: - Describe what is unusual (not just "it dropped" — by how much, compared to what baseline) - Offer the most likely explanation based on what other charts show - Flag whether the anomaly is confirmed or requires further investigation - Note if it could be a tracking artifact (incomplete day, instrumentation break) ### 5. Correlate with User Feedback if Available If the dashboard includes qualitative signals (NPS, CSAT, support ticket volume), correlate them with the quantitative metrics: - Are users reporting friction in areas where quantitative metrics also show drop-off? - Are there positive qualitative signals that explain quantitative growth? - Contradictions between qualitative and quantitative signals often reveal the most interesting insights ### 6. Provide Specific Recommendations Conclude with 2-4 concrete recommended actions. Each recommendation should: - Be directly tied to a specific finding from the dashboard - Name the team or person who should act on it - Specify a timeline (this sprint, this week, this month) - Have a clear success metric — how will you know if the recommendation worked? ## MCP Tools - `mcp__Amplitude__get_dashboard` — load dashboard structure and chart list - `mcp__Amplitude__query_charts` — fetch current data for all charts in the dashboard - `mcp__Amplitude__render_chart` — visual rendering of individual charts for pattern recognition - `mcp__Amplitude__get_feedback_insights` — correlate quantitative signals with user feedback (if available) ## Key Concepts - **Narrative synthesis**: Translating a collection of charts into a single coherent story with a beginning (context), middle (findings), and end (recommendations). - **Leading indicator**: A metric that changes before the outcome metric (e.g., activation rate predicts retention). - **Lagging indicator**: A metric that reflects past performance (e.g., monthly revenue reflects decisions made weeks ago). - **Business impact ranking**: Prioritizing findings by their consequence for revenue, retention, or growth — not by the size of the percentage change. - **Anomaly context**: An anomaly is only meaningful when compared to a baseline. Always state both the deviation and the baseline. - **Contradictions**: When two metrics tell opposite stories, that contradiction is often the most important finding. ## Output Format Output is written in narrative paragraphs readable by executives — not labeled database fields or bullet dumps. Structure: 1. **Opening** (1-2 sentences): The single most important thing the dashboard shows right now. 2. **Overall health** (1 paragraph): The big-picture read on the product area — momentum direction, key drivers. 3. **Key findings** (3-5 findings, each 2-3 sentences): Specific metrics with numbers, direction, magnitude, and business significance. 4. **Risks and anomalies** (1 paragraph): What to watch — deviations that need attention or investigation. 5. **What's working** (1 paragraph): Positive signals worth amplifying or doubling down on. 6. **Recommendations** (2-4 bullet points): Concrete next actions, each tied to a specific finding. Every factual claim must include a specific number. No vague language ("metrics look good," "some improvement").