2 399 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 951 tokens or less — that is what one costs your context window when the agent loads it. 545 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 355 copies of these same skills sitting in other people's repositories — counted once here, not 355 times.
2 399 unique 443 authors 1 328 updated this month 230 from vendors
Generate and write structured JSON reports with proper formatting and validation.
Loading and parsing CSV files with D3.js, data transformation, and handling missing values
D3.js force simulation for creating interactive bubble charts with clustering, collision detection, and physics-based positioning
Essential D3.js v6 concepts including selections, scales, SVG basics, and data binding for building interactive visualizations
D3.js techniques for tooltips, hover effects, click handlers, and cross-filtering between visualizations
Calculating RMSE metrics and parameter calibration for lake model validation
Load and parse CSV/TXT files from travel database for itinerary planning
Generate structured CSV files from Python data using the csv module for tabular data export.
Use the xlsx skill tool to create, read, modify and analyze Excel spreadsheets with formulas and formatting.
Define custom distance/similarity metrics for DBSCAN clustering with sklearn, using weighted Euclidean distances.
Generate structured CSV security audit reports from vulnerability data with proper filtering and formatting.
Patterns for analyzing and outputting structured JSON answers from enterprise data queries.
Analyze SEC 13-F filings data including AUM, holdings count, and cross-quarter comparisons using TSV files.
Load and parse CSV data with D3.js v6 using d3.csv() and d3.autoType for local file serving.
Build interactive HTML data tables with D3.js v6, including row highlighting and click-based selection synced with charts.
Create force-simulation bubble charts with D3.js v6, including sector clustering, collision avoidance, tooltips, and legends.
Reading and analyzing GLM NetCDF output with Python netCDF4 and pandas for RMSE evaluation.
Excel statistical functions (MIN, MAX, MEDIAN, AVERAGE, PERCENTILE, SUMPRODUCT) with ROUND.
Run DBSCAN clustering with a custom distance metric using sklearn, including how to define weighted Euclidean metrics and extract cluster centroids.
Generate structured CSV security audit reports from Trivy JSON vulnerability data with severity filtering and proper field mapping.
Python patterns for analyzing large JSON datasets to find specific information, track tokens, and write answers in the required format.
Add a categorical color legend to a D3 chart using colored rectangles or circles with text labels, supporting both SVG-inline and HTML overlay styles.
Build a force-simulation bubble chart in D3.js v6 where bubbles are sized by a numeric value, colored by category, and clustered by category using forceX/forceY. Covers deterministic layout, collision, and tick-based settling.
Build a sortable, highlightable HTML data table linked to a D3 chart so clicking a chart element highlights the matching table row and vice versa.
Calculating GDP-weighted means and other weighted statistics in Excel using SUMPRODUCT, including net exports as percent of GDP.
Greedy matching algorithm to pair predicted cluster centroids with ground truth points for F1 and distance metrics.
Generate structured CSV security audit reports from vulnerability data with proper filtering and formatting. This skill covers CSV schema design for security reports, using Python csv.DictWriter, severity-based filtering, and field mapping from JSON to tabular format.
Process JSON data from GitHub CLI using jq to calculate metrics like average merge times, counts, and top contributors.
Loading CSV data, formatting currency/large numbers, and implementing tooltips in D3.
Techniques for creating clustered bubble charts using D3 force simulations (v6).
A skill to process GLM NetCDF output and calculate specific RMSE metrics by merging with field observations.
Filter CSV data (restaurants, accommodations, attractions) based on criteria like city, cuisine, budget, and pet-friendliness.
Use a distance matrix CSV to find travel time and distance between cities for self-driving trips.
Covers descriptive statistics (mean, median, percentiles) and weighted averages using Excel formulas.
Creating structured CSV reports for security findings.
Skills for querying GitHub API data using the `gh` CLI, specifically for extracting PR and issue metrics.
This skill covers computing RMSE metrics from observational and simulated water temperature data.
Provides methods to search, parse, and filter local CSV files in /app/data/.
Custom distance metric implementation for use with clustering algorithms like DBSCAN.
Output vulnerability reports as a formatted CSV file.
A skill for parsing and analyzing SEC Form 13F TSV files, including COVERPAGE, INFOTABLE, and SUMMARYPAGE, using pandas.
Parse datetime strings and compute time durations/averages for datasets (like PR merge times) in Python.
Guide for creating clustered bubble charts using D3.js force simulations (v6+), including collision and categorization.
Guide for linking D3.js SVG visualizations with DOM elements like HTML tables for bidirectional interactivity (hover, click, highlight).
Matching observations with simulated data using exact datetime and rounded depth merges in pandas.
Using Python and Pandas to analyze large CSV datasets and filter based on specific criteria like budget, location, and amenities.
Compute and aggregate F1 score and delta from clustering evaluation across images
Implement weighted Euclidean distance metric for DBSCAN with shape parameter