nsforge-formula-management
ๅ ฌๅผๅบซ็ฎก็ใ่งธ็ผ่ฉ๏ผๆพๅ ฌๅผ, ๅๅบ, ๆดๆฐๅ ฌๅผ, ๅช้คๅ ฌๅผ, ๅ ฌๅผๅบซใ
ๅ ฌๅผๅบซ็ฎก็ใ่งธ็ผ่ฉ๏ผๆพๅ ฌๅผ, ๅๅบ, ๆดๆฐๅ ฌๅผ, ๅช้คๅ ฌๅผ, ๅ ฌๅผๅบซใ
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.
This skill should be used when the user asks to "query Chicago data", "find Chicago datasets", "get Chicago crime data", "download Chicago permits", "write a SODA query for Chicago", "search data.cityofchicago.org", or mentions Chicago city data (311, permits, licenses, inspections, crimes, etc.).
Generate and execute SQL queries against the civitai_records PostgreSQL database to analyze video performance on Civitai. Use when users ask about: video engagement metrics (likes, hearts, comments), content performance analysis, tag/theme analysis, quality scores, weekly reports, comparing videos, content recommendations, trend analysis, or any Civitai data queries. Triggers: Civitai, video stats, engagement, likes, hearts, comments, weekly report, tag analysis, quality score, content strategy, top performers, SQL query, video comparison, WoW analysis, ๆฐๆฎๅๆ, ่ง้ข่กจ็ฐ, ๅจๆฅ, ๅ ๅฎนๅๆ.
Execute code to analyze data and perform complex calculations.
Create custom layer types with WebGL rendering, custom tile layers, and blend layers. Use for advanced visualizations and custom data sources.
็ๆๅฎ็จRๅ ๅ็ฑป้กต้ข็่ถ ่ฏฆ็ปๆ็จๅ ๅฎน๏ผๅผบ่ฐๅ ็ๅบๆฏใๅ่ฝไธๅฎ่ทต
Elementos conectados visualmente se perciben como relacionados. Use cuando diseรฑe diagramas, flujos, relaciones entre elementos, o conexiones visuales.
Use when creating tree diagrams, force-directed networks, Voronoi diagrams, or hierarchical layouts. Invoke for org charts, node-link diagrams, treemaps, dendrograms, force simulations, spatial indexing, or network visualizations.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
This skill should be used when the user asks to "create a plot", "make a chart", "visualize data", "create a heatmap", "make a scatter plot", "plot time series", "create publication figures", "customize plot styling", "use matplotlib", "use seaborn", or needs guidance on Python data visualization, statistical graphics, or figure export.
Core data analytics concepts, Excel/Google Sheets fundamentals, and data collection techniques
This skill should be used when the user asks to "query Cook County data", "find Cook County datasets", "get property assessments", "download parcel data", "search datacatalog.cookcountyil.gov", "get medical examiner data", "find court cases", "query State's Attorney data", or mentions Cook County government data (assessor, treasurer, courts, payroll, medical examiner, etc.).
Master data visualization principles including chart selection, dashboard design, color theory, and data storytelling
Expert-level Tableau Desktop/Server, calculated fields, LOD expressions, dashboards, data blending, and performance optimization
Use OPAL subquery syntax (@labels) and union operations to combine multiple datasets or time periods. Essential for period-over-period comparisons, multi-dataset analysis, and complex data transformations. Covers @label <- @ syntax, timeshift for temporal shifts, union for combining results, and any_not_null() for collapsing grouped data.
ๅ จๆนไฝ็่ฉฆ็ฎ่กจๅปบ็ซใ็ทจ่ผฏๅๅๆๅ่ฝ๏ผๆฏๆดๅ ฌๅผใๆ ผๅผๅใ่ณๆๅๆๅ่ฆ่ฆบๅใ็ถ Claude ้่ฆ่็่ฉฆ็ฎ่กจ๏ผ.xlsxใ.xlsmใ.csvใ.tsv ็ญ๏ผๆไฝฟ็จ๏ผๅ ๆฌ๏ผ(1) ๅปบ็ซๅ ๅซๅ ฌๅผๅๆ ผๅผ็ๆฐ่ฉฆ็ฎ่กจใ(2) ่ฎๅๆๅๆ่ณๆใ(3) ไฟฎๆน็พๆ่ฉฆ็ฎ่กจๅๆไฟ็ๅ ฌๅผใ(4) ่ฉฆ็ฎ่กจไธญ็่ณๆๅๆๅ่ฆ่ฆบๅ๏ผๆ (5) ้ๆฐ่จ็ฎๅ ฌๅผ
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.
Style and render geographic data with renderers, symbols, and visual variables. Use for creating thematic maps, heatmaps, class breaks, unique values, labels, and 3D visualization.
ไธไธๆฐๆฎๅฏ่งๅไธๅฎถ๏ผ็ฒพ้็ฐไปฃๅพ่กจๅบใไปช่กจๆฟ่ฎพ่ฎกๅไบคไบๅผๆฐๆฎๅฑ็คบใ่ฝๅคๅฐๅคๆๆฐๆฎ่ฝฌๅไธบ็ด่งใ็พ่งไธๅฏๆๆดๅฏๅ็ๅฏ่งๅไฝๅใ
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
Extracts and analyzes HEC-HMS simulation results from DSS files using HmsDss and HmsResults classes. Handles peak flows, hydrographs, volume summaries, and time series data. Leverages ras-commander's RasDss for DSS V6/V7 support. Use when processing HMS results, extracting peak flows, analyzing hydrographs, computing volumes, or exporting time series. Integrates with HEC-RAS for boundary condition workflows. Trigger keywords: DSS file, results, peak flow, hydrograph, time series, volume, extract results, HMS output, analyze results.