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
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.
Logic to parse nested security database objects into a scalar CVSS score for the final CSV report.
Construct the final CSV output file ensuring strict column compliance with the security audit requirements.
Implements the D3.js force simulation, tooltips, and data interaction logic in visualization.js.
Calculates RMSE between simulated and observed data using exact datetime matching and integer depth binning based on simulation start date 2009-01-01.
Aggregate object counts into a structured CSV file.
HTML/CSS/JS patterns for creating tooltips, conditional interactions based on data attributes, and cross-highlighting elements across different views (like a chart and a table).
Guide for generating technical exploded-view posters using Python (PIL/Pillow). Use this skill when creating engineering diagrams, hardware breakdowns, or product teardowns that require precise layer visualization, annotation leader lines, and technical accuracy. Includes methods for drawing components, layering, annotation, and exporting high-quality images.
Compute F1 score and delta (average distance) metrics for Mars cloud cluster evaluation. Use this skill when evaluating DBSCAN clustering results against expert annotations, including handling edge cases like images with no clusters, no expert points, or no matches.
Identify bug reports by checking if any issue label contains the substring "bug" (case-insensitive matching). Use this skill when categorizing GitHub issues as bugs vs. features, filtering defects, or generating quality/reliability metrics.
Query GitHub API for pull requests and issues within a date range, retrieving all metadata needed for metrics collection. Use this skill whenever you need to fetch GitHub PR or issue data with date filtering, especially when gathering activity reports or computing statistics across a time period.
Aggregate GitHub PR and issue data into structured metrics, including contributor analysis, merge statistics, and bug categorization. Use this skill when compiling activity reports, computing open-source velocity metrics, or generating community pulse summaries.
Calculate average time-to-merge from PR creation to merge timestamps, handling edge cases like unmerged and closed PRs. Use this skill whenever computing PR velocity metrics, measuring merge turnaround, or analyzing PR lifecycle duration.
Synchronize interactions between D3 visualizations and HTML tables, enabling click-to-highlight and hover effects across both elements. Use this skill when you need chart and table elements to stay in sync - clicking a bubble highlights its table row, clicking a row highlights the bubble, and/or hovering shows related data across both. Essential for exploratory dashboards where users need to see data from multiple perspectives simultaneously.
How to create a force-directed bubble chart in D3.js (v6+). Use this skill whenever the user asks to build a bubble chart, cluster bubbles by category, or use d3.forceSimulation to position SVG circles without overlap.
Load, parse, and transform stock market data from CSV files for D3 visualization. Use this skill when working with financial CSV data, handling missing values (ETFs often lack market cap/country data), formatting market capitalization as human-readable strings (1.64T), and preparing data for both visualization and table display. Essential for stock dashboards, portfolio analytics, and financial data pipelines.
Computing RMSE (Root Mean Squared Error) and other metrics for water temperature model validation. Use this skill whenever you need to match simulated temperatures with field observations, compute RMSE by depth categories, calculate annual/seasonal subsets, or prepare model evaluation metrics. Essential for lake model calibration and validation workflows.
Complete workflow for analyzing Super Mario game video frames and generating statistics. Use this skill when you need to extract video frames, process them for analysis, count game objects using template matching, and generate CSV reports of game element statistics.
Define custom distance/similarity metrics for clustering and ML algorithms. Use when working with DBSCAN, sklearn, or scipy distance functions with application-specific metrics.
Perform data analysis on SEC 13-F filing datasets (TSV format) to obtain insights about fund activities such as number of holdings, AUM, and change of holdings between two quarters. Use this skill whenever analyzing hedge fund portfolios, comparing quarterly holdings, or working with SEC EDGAR 13F data files.
> Query GitHub repository activity (PRs, issues) for a date range using the gh CLI. Use this skill whenever you need to fetch pull request or issue data from a GitHub repository, compute statistics like counts, merge times, or identify top contributors. Triggers on tasks involving GitHub activity reports, community pulse summaries, or repository metrics gathering.
> Build structured JSON reports from GitHub repository activity data. Use this skill whenever you need to compile PR and issue statistics into a formatted JSON file for meeting summaries, community pulse reports, or stakeholder updates. Triggers on tasks that require outputting a report.json or similar artifact from GitHub metrics.