Data Analysis Claude Skills
2 409 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 952 tokens or less — that is what one costs your context window when the agent loads it. 549 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 386 copies of these same skills sitting in other people's repositories — counted once here, not 386 times.
2 409 unique 443 authors 5 updated this month 241 from vendors
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
Advanced CSV security report generation with validation, normalization, and comprehensive error handling
Aggregate and validate GitHub API data for metrics calculation with comprehensive error handling.
Robust CSV loading and type coercion patterns for D3.js applications with multiple data sources
Production-ready D3.js bubble chart clustering with optimized force simulation and labeling
Two-way synchronized selection between D3 visualization and HTML table with tooltips and scroll management
Systematic parameter search strategies for GLM calibration with multiple metrics
Generate and validate CSV files with data integrity checks, verification, and comprehensive error handling for reliable data export.
Excel statistical functions for datasets including MIN, MAX, MEDIAN, AVERAGE, QUARTILE, and SUMPRODUCT
Generate structured CSV security audit reports from Trivy JSON vulnerability data with deduplication, proper quoting, and field mapping.
Comprehensive guide for analyzing SEC 13-F quarterly filings data (TSV format) including fund lookup, AUM, holdings comparison, and stock investor analysis.
Using jq to compute statistics from GitHub API JSON responses including counts, averages, grouping, and label filtering.
D3.js v6 force-directed bubble chart with sector clustering, collision avoidance, size mapping, and cross-component interaction.
D3.js data table with exact column headers, formatted market cap, and bidirectional highlight with bubble chart.
Extracting GLM NetCDF output and computing exact RMSE metrics via datetime+depth merge against field observations.
Extract and validate travel data from CSV/TXT databases ensuring city-specific matching and constraint compliance.
GDP-weighted mean and descriptive statistics for net exports as % of GDP in Excel
Generate structured CSV security audit reports from Trivy JSON output with severity filtering, deduplication, and proper field mapping.
How to assemble and write a structured JSON report for GitHub community pulse statistics with validation.
Creating D3.js v6 bubble charts with market cap sizing (scaleSqrt), sector coloring, inside ticker labels, and ETF uniform sizing — with correct tooltip suppression for ETFs.
Building a scrollable D3.js HTML table linked to a bubble chart with bidirectional click-highlight, correct scrollIntoView for overflow containers, and sector color badges.
Exact RMSE metric computation for GLM lake temperature evaluation - verified matching procedure with correct threshold values.
Explore and query travel dataset files (CSV/TXT) to find cities, restaurants, accommodations, attractions, and driving distances — with precise filtering patterns and data field notes.
Count template objects in keyframes using count_objects.py; parse integer from output string and generate a CSV with results per frame.
Calculate GDP-weighted mean of net exports as % of GDP using SUMPRODUCT in Excel, including all supporting statistics (min, max, median, percentiles).
Greedy point matching and calculation of F1 score and average distance (delta).
Reliable CSV generation for security audits with automated field mapping.
Advanced techniques for analyzing complex enterprise product JSON files containing messages, documents, and transcripts.