executive-dashboard-generator
Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summarie
Install
npx skills add https://github.com/OneWave-AI/claude-skills/tree/main/executive-dashboard-generator
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install onewave-ai-claude-skills@llmmart
git clone https://github.com/OneWave-AI/claude-skills.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole onewave-ai/claude-skills collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Executive Dashboard Generator
Turn raw data into executive-ready insights with visualizations and recommendations.
Contents
references/analysis-coverage.md— supported data inputs and analysis types per domain (financial, sales/marketing, operations, customer).references/output-template.md— the full Markdown dashboard template to populate.references/best-practices.md— reporting principles, visualization do/don't, trigger phrases, example request.
Workflow
- Clarify business context and collect the data sources (files, Sheets links, query output). See
references/analysis-coverage.mdfor accepted inputs and the analysis types to run per domain. - Discover the data: identify structure, date ranges, granularity, key metrics and dimensions, and any data quality issues. Map relationships across datasets.
- Analyze: calculate period-over-period changes, identify trends and patterns, flag outliers and anomalies, run cohort analysis, and set benchmarks and targets.
- Generate insights: synthesize findings into key messages, prioritize by business impact, connect each metric to a business outcome, and develop concrete action recommendations and risk/opportunity flags.
- Choose visualizations: select chart types built for executive readability and maintain a clear visual hierarchy. See
references/best-practices.mdfor the visualization do/don't list. - Assemble the report using the structure in
references/output-template.md. Replace every placeholder with real values; never ship a template with bracketed placeholders left in. - Prioritize recommendations and include scenario planning and a risk assessment, then deliver the dashboard.
Lead with insights over raw numbers, and answer "So what?" and "What should we do?" Apply the reporting principles in references/best-practices.md.
Files (claude-skills)
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references
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analysis-coverage.md 889 B
# Analysis Coverage Supported data inputs and the analysis types to apply per domain. ## Data Input Handling - CSV files (single or multiple) - Excel spreadsheets (.xlsx, .xls) - Google Sheets links - Database query results - JSON/API responses - Text-based data tables ## Analysis Types ### Financial Performance - Revenue trends and growth rates - Cost analysis and burn rate - Profitability metrics - Budget vs. actuals - Cash flow analysis ### Sales & Marketing - Pipeline health and conversion rates - Customer acquisition costs (CAC) - Lifetime value (LTV) - Channel performance - Campaign ROI ### Operations - KPI tracking and goal achievement - Process efficiency metrics - Resource utilization - Quality metrics - Capacity planning ### Customer Metrics - Churn and retention rates - NPS and satisfaction scores - Support ticket trends - Feature adoption - User engagement -
best-practices.md 1.6 KB
# Best Practices and Guidelines ## Reporting Best Practices 1. **Lead with insights, not data**: Start with what the numbers mean, not what they are. 2. **Use a clear status system**: On Track, Monitor, Attention Needed for instant visual status. 3. **Show trends, not points**: Context matters more than single numbers. 4. **Be specific with recommendations**: "Increase X by Y% using Z approach" not "Improve X". 5. **Include a forward-looking view**: Executives care about what's coming. 6. **Highlight outliers**: Call attention to anomalies and explain them. 7. **Connect to business goals**: Tie metrics back to strategy. 8. **Keep it scannable**: Use bullets, tables, and visual breaks. ## Visualization Guidelines For executives, use: - Line charts (trends over time) - Bar charts (comparisons) - KPI cards (single metrics) - Status indicators (text labels) - Simple tables with conditional formatting Avoid: - Pie charts with more than 5 slices - 3D charts - Overly complex visualizations - Charts without clear titles - Confusing color schemes ## Common Use Cases Trigger phrases: - "Turn these spreadsheets into an executive report" - "Create a dashboard from this data" - "Summarize this data for the board meeting" - "Build an executive summary from these CSVs" - "Analyze this data and provide recommendations" Example request: > "I have 10 CSV files with sales data, marketing spend, and customer metrics from the last 6 months. Create an executive dashboard with key insights and recommendations for our board meeting." Closing principle: executives want answers to "So what?" and "What should we do?" not raw data. -
output-template.md 8.3 KB
# Output Template Use this Markdown structure for the generated dashboard. Replace bracketed placeholders with real values. Use the text status labels On Track, Monitor, and Attention Needed instead of colored indicators. ```markdown # Executive Dashboard: [Report Title] **Period**: [Date Range] | **Generated**: [Date] | **Status**: [Attention Needed / Monitor / On Track] --- ## Executive Summary **Overall Performance**: [One-sentence verdict] **Key Highlights**: - [Positive achievement with metric] - [Another win with specific number] - [Area of concern with context] - [Critical issue requiring attention] **Bottom Line**: [Two-sentence conclusion with action needed] --- ## Critical Metrics Dashboard ### Performance Scorecard | Metric | Current | Previous Period | Change | Target | Status | |--------|---------|----------------|--------|--------|--------| | Revenue | $X.XM | $X.XM | +X% | $X.XM | On Track | | Customers | X,XXX | X,XXX | +X% | X,XXX | On Track | | Churn Rate | X.X% | X.X% | -X% | <X% | Monitor | | CAC | $XXX | $XXX | +X% | $XXX | Attention Needed | | Burn Rate | $XXX K | $XXX K | -X% | $XXX K | On Track | **Key**: On Track = On/Above Target | Monitor | Attention Needed = Below Target --- ## Trend Analysis ### Revenue Trajectory ``` visualization: line chart x-axis: months y-axis: revenue data points: [detailed monthly data] trend line: included annotation: highlight significant events ``` **Insight**: [2-3 sentences explaining the trend, what's driving it, and projection] **Chart Description**: Revenue has grown X% QoQ, from $X.XM in [Month] to $X.XM in [Month]. The acceleration in [specific month] was driven by [reason]. At current growth rate, we project $X.XM by [future date]. --- ### Customer Acquisition & Retention ``` visualization: dual-axis chart left y-axis: new customers (bars) right y-axis: churn rate (line) x-axis: months ``` **Insight**: [Analysis of acquisition vs. retention balance] **Key Finding**: New customer acquisition is [strong/weak/steady] at XXX per month (+X% MoM), but churn increased to X.X% in [month], driven by [specific reason from data]. Net customer growth is XXX per month. --- ### Channel Performance ``` visualization: stacked bar chart or treemap categories: [Marketing channels] metric: revenue contribution and ROI ``` | Channel | Revenue | % of Total | Cost | ROI | Trend | |---------|---------|-----------|------|-----|-------| | Organic Search | $XXX K | XX% | $X K | XX:1 | Up | | Paid Social | $XXX K | XX% | $XX K | X:1 | Down | | Direct | $XXX K | XX% | $X K | N/A | Flat | | Referral | $XXX K | XX% | $X K | XX:1 | Up | | Email | $XXX K | XX% | $X K | XX:1 | Flat | **Insight**: [Which channels are performing, which need optimization] --- ## Deep Dive: [Most Important Finding] ### The Issue/Opportunity **What We're Seeing**: [Describe the pattern or anomaly in data] **By The Numbers**: - [Specific metric 1]: [Value] ([% change]) - [Specific metric 2]: [Value] ([% change]) - [Specific metric 3]: [Value] ([% change]) **Why It Matters**: [Business impact and implications] **Root Cause Analysis**: 1. **Primary Factor**: [What data shows is the main driver] - Supporting data: [Specific numbers] - Time frame: [When it started/changed] 2. **Contributing Factors**: - [Factor 2 with evidence] - [Factor 3 with evidence] **Projected Impact**: If trend continues, [describe future state with numbers] --- ## Strategic Recommendations ### Priority 1: [Action Item Title] (URGENT) **Situation**: [What the data shows] **Action**: [Specific recommendation] **Expected Impact**: [Projected improvement with numbers] **Timeline**: [When to implement and see results] **Owner**: [Recommended department/role] **Resources Required**: [Budget, people, tools needed] **Supporting Data**: - [Metric 1] currently at [value], target is [value] - [Metric 2] trending [direction], showing [pattern] - Industry benchmark is [value], we're at [value] --- ### Priority 2: [Action Item Title] (IMPORTANT) **Situation**: [What the data shows] **Action**: [Specific recommendation] **Expected Impact**: [Projected improvement] **Timeline**: [Implementation timeline] **Owner**: [Department/role] **Resources Required**: [What's needed] --- ### Priority 3: [Action Item Title] (OPPORTUNITY) **Situation**: [What the data shows] **Action**: [Specific recommendation] **Expected Impact**: [Projected improvement] **Timeline**: [Timeline] **Owner**: [Department/role] --- ## Departmental Scorecards ### Sales Performance | Metric | Current | Target | Status | Insight | |--------|---------|--------|--------|---------| | Pipeline Value | $X.XM | $X.XM | On Track | Up X% from last quarter | | Win Rate | XX% | XX% | Monitor | Declined X% due to [reason] | | Sales Cycle | XX days | XX days | On Track | Improved by X days | | Avg Deal Size | $XX K | $XX K | Attention Needed | Down X% need pricing review | **Overall**: [One sentence summary of sales health] --- ### Marketing Performance | Metric | Current | Target | Status | Insight | |--------|---------|--------|--------|---------| | Leads Generated | X,XXX | X,XXX | On Track | X% above target | | MQL Conversion | XX% | XX% | Monitor | Quality needs improvement | | CAC | $XXX | $XXX | Attention Needed | Up X% from paid channels | | Website Traffic | XXX K | XXX K | On Track | Organic growth strong | **Overall**: [One sentence summary of marketing performance] --- ### Customer Success | Metric | Current | Target | Status | Insight | |--------|---------|--------|--------|---------| | NPS Score | XX | XX | On Track | Improved X points | | Churn Rate | X.X% | X.X% | Attention Needed | Above target, investigate | | Support SLA | XX% | XX% | On Track | Meeting commitments | | Expansion Revenue | $XXX K | $XXX K | Monitor | Slightly below plan | **Overall**: [One sentence summary of CS health] --- ## Scenario Planning ### Best Case Scenario (25% probability) **Assumptions**: [What needs to go right] **Projected Outcomes**: - Revenue: $X.XM (X% growth) - Customers: X,XXX (X% growth) - [Other key metrics] **Triggers**: [Early indicators this is happening] --- ### Expected Scenario (50% probability) **Assumptions**: [Current trends continue] **Projected Outcomes**: - Revenue: $X.XM (X% growth) - Customers: X,XXX (X% growth) - [Other key metrics] **Confidence Level**: [High/Medium based on data stability] --- ### Risk Scenario (25% probability) **Assumptions**: [What concerns materialize] **Projected Outcomes**: - Revenue: $X.XM (X% growth/decline) - Customers: X,XXX (X% growth/decline) - [Other key metrics] **Mitigation Plans**: [What to do if this happens] --- ## Risk Flags ### High Risk **[Risk Title]** - **Severity**: High - **Data Signal**: [Specific metric and threshold] - **Impact**: [Business consequence if not addressed] - **Recommendation**: [Immediate action required] ### Medium Risk **[Risk Title]** - **Severity**: Medium - **Data Signal**: [What data is showing] - **Impact**: [Potential consequence] - **Recommendation**: [Action to monitor/address] --- ## Next Period Outlook ### Goals for [Next Period] **Primary Objectives**: 1. [Objective 1] - Target: [Specific metric goal] 2. [Objective 2] - Target: [Specific metric goal] 3. [Objective 3] - Target: [Specific metric goal] **Key Initiatives to Support Goals**: - [Initiative 1]: [Expected impact] - [Initiative 2]: [Expected impact] - [Initiative 3]: [Expected impact] **Metrics to Watch**: - [Metric 1]: Current [value], Target [value] - [Metric 2]: Current [value], Target [value] - [Metric 3]: Current [value], Target [value] --- ## Appendix: Data Details ### Data Sources - **Source 1**: [File name, date range, rows] - **Source 2**: [File name, date range, rows] - **Last Updated**: [Date and time] ### Methodology - **Period Comparison**: [How periods are compared] - **Calculations**: [Any custom formulas or aggregations] - **Exclusions**: [Any data filtered out and why] - **Data Quality Notes**: [Any issues or caveats] ### Glossary - **[Term 1]**: [Definition] - **[Term 2]**: [Definition] - **[Term 3]**: [Definition] --- ## Report Metadata - **Report ID**: [Unique identifier] - **Version**: [Version number] - **Created By**: Executive Dashboard Generator (AI) - **Review By**: [Designated human reviewer] - **Distribution**: [Who should receive this] - **Next Report**: [When is next update] - **Questions**: [Contact for clarifications] ```
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SKILL.md 2.2 KB
--- name: executive-dashboard-generator description: Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports. --- # Executive Dashboard Generator Turn raw data into executive-ready insights with visualizations and recommendations. ## Contents - `references/analysis-coverage.md` — supported data inputs and analysis types per domain (financial, sales/marketing, operations, customer). - `references/output-template.md` — the full Markdown dashboard template to populate. - `references/best-practices.md` — reporting principles, visualization do/don't, trigger phrases, example request. ## Workflow 1. Clarify business context and collect the data sources (files, Sheets links, query output). See `references/analysis-coverage.md` for accepted inputs and the analysis types to run per domain. 2. Discover the data: identify structure, date ranges, granularity, key metrics and dimensions, and any data quality issues. Map relationships across datasets. 3. Analyze: calculate period-over-period changes, identify trends and patterns, flag outliers and anomalies, run cohort analysis, and set benchmarks and targets. 4. Generate insights: synthesize findings into key messages, prioritize by business impact, connect each metric to a business outcome, and develop concrete action recommendations and risk/opportunity flags. 5. Choose visualizations: select chart types built for executive readability and maintain a clear visual hierarchy. See `references/best-practices.md` for the visualization do/don't list. 6. Assemble the report using the structure in `references/output-template.md`. Replace every placeholder with real values; never ship a template with bracketed placeholders left in. 7. Prioritize recommendations and include scenario planning and a risk assessment, then deliver the dashboard. Lead with insights over raw numbers, and answer "So what?" and "What should we do?" Apply the reporting principles in `references/best-practices.md`.
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