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
Robust Python logic for community pulse metrics with substring label matching and precise PR status handling.
Robust JSON-to-CSV parser for Trivy vulnerability reports.
Geospatial data analysis with geopandas for distance calculations, filtering, and modern API usage.
Advanced skills for fetching, filtering, and aggregating GitHub data using `gh` CLI and `jq`.
Implementing force-directed bubble charts with category clustering and tooltips.
Optimized loading and pre-processing of CSV data for D3.js.
Improving interaction between D3 charts and HTML tables with selection events.
This skill covers the process of calibrating GLM by iterating through parameters within allowed bounds and verifying metrics against observations.
Improved data querying skill for finding pet-friendly accommodations and specific restaurant cuisines in a given city.
Improved Excel data analysis and formula manipulation using openpyxl.
How to compute precomputed distance matrices for custom distance metrics in Scikit-Learn DBSCAN.
An advanced guide on generating a fully-featured CSV vulnerability report, featuring explicit filtering, missing data fallbacks, and strict header schemas.
Guide to building a polished D3.js (v6) force-directed bubble chart and a synchronized data table, including integrating secondary data for sparklines.
How to load and filter CSV travel data in Python
Calculating and rounding summary statistics like percentiles in Excel.
Match clustered centroids to expert annotations using greedy nearest-neighbor matching with distance constraints. Compute F1 scores and delta metrics for clustering quality assessment.
Format and write Pareto frontier results to CSV file with proper rounding and column ordering.
Write CSV output with correct field quoting and escaping for special characters, using standard CSV formatting.
Correctly identify and extract Trivy vulnerability JSON field names and map them to the required CSV output columns in exact order.
Confirm that the accession_number retrieved corresponds to the correct quarter and report date before using it for analysis. Use this to prevent analyzing data from the wrong quarter.
Compile gathered PR and issue metrics into a properly formatted JSON file at /app/report.json with the exact required structure and data types.
Implement click handlers on both bubble chart and data table that maintain synchronized highlighting/selection state across both visualizations
Processes all extracted keyframes by converting them to grayscale, then counts coins, enemies, and turtles in each frame using template matching. Generates a CSV file with frame-by-frame object counts. Use this as the main analysis pipeline after keyframe extraction.
Calculate net exports as a percentage of GDP for multiple countries, then compute descriptive statistics (min, max, median, mean, percentiles) on the percentage values. Use this when analyzing trade performance across multiple entities.
Calculate a weighted average using SUMPRODUCT when weights and values have a one-to-one correspondence. Use this for computing aggregate statistics where different entities have different importance (weights).
Implementing custom distance metrics for DBSCAN in scikit-learn for specialized coordinate-based clustering.
How to generate a properly formatted CSV security audit report from vulnerability scan results, including handling of special characters and proper escaping.
Core skill for retrieving information from enterprise data files (JSON, JSONL, CSV, Parquet, Markdown, etc.) located at /root/DATA, answering questions from /root/question.txt, and writing structured answers to /root/answer.json. Handles multi-hop reasoning, cross-referencing, and entity resolution across enterprise documents.
How to compile PR and issue statistics into a report.json file for a GitHub repository community pulse report, using jq to build the final JSON structure.
How to merge GLM simulation output with field observations and compute RMSE metrics for Lake Mendota calibration. Use this when computing overall RMSE, annual deep RMSE, and summer deep RMSE from matched observation-simulation pairs.
Use this skill to generate the final CSV file at /root/counting_results.csv with columns frame_id, coins, enemies, turtles. Frame IDs must be in the format /root/keyframes_%03d.png.
How to calculate net exports as % of GDP, descriptive statistics, and weighted mean for GCC countries in the gdp.xlsx workbook. Use this for the specific task of filling in the Task sheet with lookup formulas, percentage calculations, and SUMPRODUCT weighted mean.
How to run DBSCAN with a custom anisotropic distance metric using sklearn, including defining callable metrics and computing cluster centroids from labeled output.
How to compute the Pareto frontier from a set of (F1, delta) optimization results where F1 is maximized and delta is minimized. Includes filtering, dominance checking, and CSV output formatting.
Compile PR and issue metrics into a report.json file at /app/report.json using the required schema for the December community pulse write-up.
Use when building a D3.js v6 bubble chart with force simulation, sector clustering, collision detection, tooltips, and interactive highlighting. Handles ETFs (no marketCap/sector) as a separate cluster.
Use when building an HTML data table that syncs highlighting with a D3 bubble chart on click interactions in both directions.
Use when you need to read and parse CSV files from the filesystem in Python. Handles encoding, missing values, and returns structured data.
Count coins, enemies, and turtles in each keyframe using the provided count_objects.py script with threshold=0.9 and dedup_min_dist=3. Parse stdout for integer counts. Write results to /root/counting_results.csv.
Use when calculating net exports as a percentage of GDP in Excel, including computing summary statistics (min, max, median, mean, percentiles) and a GDP-weighted mean using SUMPRODUCT. Covers formula structure, rounding conventions, and display formatting.
Formatting security audit findings into a standardized CSV format for reporting and compliance.
Processes JSON data from GitHub Search results to compute specific repository health metrics including average merge time, top contributors, and bug resolution counts.
Creates the core D3.js logic in visualization.js. This includes data loading, custom market cap formatting (supporting "T" for Trillions), force simulation for sector clustering, bubble generation, and bidirectional table highlighting.
Initializes the directory structure and populates the environment with necessary data and libraries for the D3.js visualization. This skill should be executed before generating any code files to ensure the target paths exist and the D3 library is available locally.
Calculate specific RMSE metrics by merging simulation and observation data using exact datetime and rounded-depth matching.
Aggregates counting data for all frames and objects into a final CSV file formatted as required.
Use standard Excel functions to calculate basic statistical measures across a range of values.
Defines the official, sanctioned Anthropic brand palette and HEX mapping for all technical documentation and hardware visualization assets.