{"id":"e2d5cb5e-7d57-49d2-93f4-8bab94cccf35","shortId":"hWWcsC","kind":"skill","title":"analyzing-insights","tagline":"Interprets Insight data to identify patterns, anomalies, and trends. Use when analyzing visualizations, extracting findings, or explaining patterns in graphs.","description":"# Analyzing Insights\n\n## Quick Start\n\nWhen analyzing an insight:\n1. Identify the chart type and what it measures\n2. Look for patterns (trends, seasonality, anomalies)\n3. Quantify observations with specific numbers\n4. Provide actionable interpretation\n\n## When to Use This Skill\n\n- User asks \"what does this insight show?\"\n- Analyzing visualization results\n- Identifying trends or anomalies\n- Explaining patterns in data\n- Generating insights from visual data\n\n## Chart Types\n\n| Type | Best For | Look For |\n|------|----------|----------|\n| Line | Time series trends | Direction, inflection points |\n| Bar | Category comparison | Relative sizes, outliers |\n| Area | Volume over time | Growth, composition |\n| Pie | Distribution | Proportions, dominance |\n| Table | Detailed data | Patterns, sorting |\n| Metric | Single values | Change from baseline |\n| BarList | Ranked items | Top performers, long tail |\n\n## Analysis Framework\n\n### 1. Describe What You See\n\nStart with objective observations:\n- What is being measured?\n- What is the time range?\n- What are the key dimensions?\n\n### 2. Identify Patterns\n\nLook for:\n- **Trends**: Upward, downward, flat\n- **Seasonality**: Weekly, monthly, yearly cycles\n- **Anomalies**: Spikes, drops, outliers\n- **Inflection points**: Where direction changes\n\n### 3. Quantify Observations\n\nAlways include numbers:\n- Absolute values\n- Percentage changes\n- Comparisons to baselines\n\n### 4. Provide Interpretation\n\nExplain significance:\n- Why might this be happening?\n- What are the implications?\n- What actions should be considered?\n\n## Pattern Recognition\n\n### Trend Patterns\n\n#### Upward Trend\n- Consistent growth over time\n- Look for: slope, acceleration/deceleration\n- Note: sustainability, growth rate\n\n#### Downward Trend\n- Consistent decline over time\n- Look for: rate of decline, stabilization\n- Note: severity, projected impact\n\n#### Flat/Stable\n- No significant change\n- Look for: volatility within range\n- Note: whether stability is expected\n\n### Seasonality Patterns\n\n#### Weekly Cycles\n- Weekday vs weekend differences\n- Monday dips, Friday spikes\n- Note: business day patterns\n\n#### Monthly Cycles\n- Beginning/end of month patterns\n- Billing cycles, payroll effects\n- Note: calendar effects\n\n#### Yearly Cycles\n- Holiday impacts\n- Seasonal business patterns\n- Note: YoY comparisons\n\n### Anomaly Patterns\n\n#### Spikes\n- Sudden increase\n- Look for: magnitude, duration\n- Consider: campaigns, events, bugs\n\n#### Drops\n- Sudden decrease\n- Look for: recovery pattern\n- Consider: outages, issues, seasonality\n\n#### Outliers\n- Values far from normal range\n- Look for: explanation\n- Consider: data quality, real events\n\n## Analysis by Chart Type\n\n### Line Charts\n\nFocus on:\n- Overall trend direction\n- Volatility/smoothness\n- Inflection points\n- Comparisons between lines\n\nQuestions to answer:\n- Is the metric growing or declining?\n- Are there regular patterns?\n- Where are the peaks and troughs?\n\n### Bar Charts\n\nFocus on:\n- Relative bar heights\n- Ordering (if applicable)\n- Gaps between categories\n- Outlier categories\n\nQuestions to answer:\n- Which category leads/lags?\n- Is distribution expected?\n- Are there surprising values?\n\n### Pie Charts\n\nFocus on:\n- Dominant segments\n- Small segments\n- Unexpected proportions\n\nQuestions to answer:\n- Is any segment too dominant?\n- Are proportions as expected?\n- Has composition changed?\n\n### Tables\n\nFocus on:\n- Sorting patterns\n- Extreme values\n- Null/missing data\n- Relationships between columns\n\nQuestions to answer:\n- What patterns emerge?\n- Are there data quality issues?\n- What correlations exist?\n\n## Quantification Guidelines\n\n### Describing Changes\n\n| Change | Description |\n|--------|-------------|\n| +/-5% | Slight change |\n| +/-10-20% | Moderate change |\n| +/-20-50% | Significant change |\n| +/-50%+ | Dramatic change |\n| 2x | Doubled |\n| 3x | Tripled |\n\n### Time Comparisons\n\n- **WoW**: Week-over-week\n- **MoM**: Month-over-month\n- **QoQ**: Quarter-over-quarter\n- **YoY**: Year-over-year\n\n### Statistical Context\n\n- Compare to historical average\n- Note standard deviation if known\n- Reference typical ranges\n\n## Communication Patterns\n\n### Good Insight Format\n\n```\n[What]: Revenue increased 23% this week\n[Context]: From $45,000 to $55,350\n[Comparison]: This is 15% above the 4-week average\n[Interpretation]: Likely driven by the holiday promotion\n[Recommendation]: Consider extending the campaign\n```\n\n### Avoid Vague Statements\n\n| Bad | Good |\n|-----|------|\n| \"Revenue went up\" | \"Revenue increased 23% to $55,350\" |\n| \"There's a trend\" | \"Daily active users grew 5% WoW for 6 consecutive weeks\" |\n| \"Something changed\" | \"Conversion dropped from 3.2% to 2.1% on March 15\" |\n\n## Common Pitfalls\n\n- Making claims without numbers\n- Ignoring context (seasonality, events)\n- Confusing correlation with causation\n- Over-interpreting normal variance\n- Missing obvious anomalies\n- Not considering data quality issues\n\n## Reference Files\n\n- [Insight types detail](references/insight-types.md)\n- [Visualizations guide](references/visualizations.md)\n- [Anomaly 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