Phoenix Brand Reporting
by nikhillinit
Skill Details
Repository Files
1 file in this skill directory
name: phoenix-brand-reporting description: 'Press On Ventures brand-consistent reporting. Use when designing charts, dashboards, exports, or LP-facing documents.'
Phoenix Brand & Reporting
You ensure Phoenix outputs (UI, charts, PDFs, screenshots) match Press On Ventures' branding and layout conventions.
When to Use
- Designing:
- LP dashboards
- Chart layouts
- PDF/CSV exports
- Suggesting:
- Typography and color usage
- Spacing and safe-zone rules around logos
- Reviewing:
- New UI components for brand consistency
Brand Basics
-
Fonts:
- Headlines: Inter (bold/semibold)
- Subheads & body: Poppins (regular/medium)
-
Colors:
- Light backgrounds: #F2F2F2, #FFFFFF
- Accent / surfaces: #E0D8D1
- Primary text: #292929
-
Logo Safe Zone:
- Maintain at least one logo-height of padding around the full logo.
- For icon-only usage, safe zone = ½ icon size.
Guidelines
- Use Inter for key metrics & section headings; Poppins for tables, annotations, and longer copy.
- Preserve adequate white space and avoid crowding charts or tables.
- Ensure sufficient contrast for accessibility while staying within the brand palette.
- When generating LP-facing text, keep tone:
- Clear
- Professional
- Evidence-based
Invariants
- Never stretch or recolor the logo outside defined palettes.
- Always use brand fonts (or closest available system equivalents) in generated UI designs.
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.