Revenue Health Dashboard
by gtmagents
Visualization blueprint for revenue KPIs, guardrails, and action callouts.
Skill Details
Repository Files
1 file in this skill directory
name: revenue-health-dashboard description: Visualization blueprint for revenue KPIs, guardrails, and action callouts.
Revenue Health Dashboard Skill
When to Use
- Summarizing bookings, pipeline, retention, and efficiency metrics in one place.
- Creating weekly/monthly revenue review packs with consistent layout.
- Highlighting guardrail breaches and ownership for remediation.
Framework
- KPI Stack – bookings, pipeline coverage, win rate, retention/NRR, CAC/payback.
- Segmentation Layer – tabs for region, segment, product, channel.
- Guardrail Banner – visual cues for breached vs healthy thresholds.
- Insight Tiles – annotate root cause, owner, and next action per issue.
- Drill Links – tie charts to deeper dashboards or command outputs.
Templates
- Executive summary slide with KPI dials + traffic lights.
- Detail view for pipeline vs target, retention cohorts, and efficiency metrics.
- Action tracker table embedded in the dashboard.
Tips
- Keep traffic-light rules aligned with
guardrail-scorecardto avoid confusion. - Embed command links (monitor, inspect, build) so reviewers can dive deeper.
- Snapshot dashboards before/after remediation to track improvement.
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.