Bigquery View Generator
by jeremylongshore
|
Skill Details
Repository Files
1 file in this skill directory
name: bigquery-view-generator description: | Bigquery View Generator - Auto-activating skill for GCP Skills. Triggers on: bigquery view generator, bigquery view generator Part of the GCP Skills skill category. allowed-tools: Read, Write, Edit, Bash(gcloud:*) version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io
Bigquery View Generator
Purpose
This skill provides automated assistance for bigquery view generator tasks within the GCP Skills domain.
When to Use
This skill activates automatically when you:
- Mention "bigquery view generator" in your request
- Ask about bigquery view generator patterns or best practices
- Need help with google cloud platform skills covering compute, storage, bigquery, vertex ai, and gcp-specific services.
Capabilities
- Provides step-by-step guidance for bigquery view generator
- Follows industry best practices and patterns
- Generates production-ready code and configurations
- Validates outputs against common standards
Example Triggers
- "Help me with bigquery view generator"
- "Set up bigquery view generator"
- "How do I implement bigquery view generator?"
Related Skills
Part of the GCP Skills skill category. Tags: gcp, bigquery, vertex-ai, cloud-run, firebase
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.
