Natural Language Postgres Presentation
by rebyteai-template
Presentation-focused Natural Language to SQL app with PPT-style visualizations.
Skill Details
Repository Files
1 file in this skill directory
name: natural-language-postgres-presentation description: Presentation-focused Natural Language to SQL app with PPT-style visualizations.
Natural Language Postgres Presentation
A presentation-focused Natural Language to SQL app with PPT-style visualizations for showcasing data insights.
Tech Stack
- Framework: Next.js
- AI: AI SDK
- Database: PostgreSQL
- Package Manager: pnpm
Prerequisites
- PostgreSQL database
- OpenAI API key or other LLM provider
Setup
1. Clone the Template
git clone --depth 1 https://github.com/Eng0AI/natural-language-postgres-presentation.git .
If the directory is not empty:
git clone --depth 1 https://github.com/Eng0AI/natural-language-postgres-presentation.git _temp_template
mv _temp_template/* _temp_template/.* . 2>/dev/null || true
rm -rf _temp_template
2. Remove Git History (Optional)
rm -rf .git
git init
3. Install Dependencies
pnpm install
4. Setup Environment Variables
Create .env with required variables:
POSTGRES_URL- PostgreSQL connection stringOPENAI_API_KEYor other LLM provider key
Build
pnpm build
Development
pnpm dev
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.
