All authors
Claude Skills by withqwerty
github.com/withqwerty10 skills0 installs81 views
- AcquireFetch, scrape, or download football data from any source. Also handles API key setup and credential management. Use when the user wants to get data from StatsBomb, Opta, FBref, Understat, SportMonks, Wyscout, Kaggle, or any football data source. Also use when they ask about API keys, authentication, setting up access to a provider, or what data is available free vs paid.Votes: 0GitHub stars: 48
- AnalyseExplore, interpret, and draw conclusions from football data, including judging players or teams from stats (rankings, comparisons, headlines, social posts). Use when the user wants to analyse match events, compare teams or players, understand tactical patterns, build visualisations, or needs guidance on what questions to ask of their data. Adapts to the user's experience level.Votes: 0GitHub stars: 48
- BrainstormBrainstorm football data visualisations and chart designs. Use when the user wants ideas for how to visualise football data, needs inspiration for chart types, wants to explore design approaches for match reports, player profiles, team dashboards, or any football analytics graphic. Searches the web for popular approaches and real-world examples before proposing options.Votes: 0GitHub stars: 48
- ComputeCalculate derived football metrics and models. Use when the user wants to compute xG, xGOT, PPDA, passing networks, expected threat, possession value, pressing intensity, or any derived football statistic from raw data.Votes: 0GitHub stars: 48
- HealFix broken data scrapers and pipelines. Use when data acquisition fails, a scraper breaks, an API returns errors, or data format has changed. Also handles submitting upstream issues or PRs when the problem is in a dependency like soccerdata or kloppy.Votes: 0GitHub stars: 48
- LearnLearn about football analytics concepts and explore provider documentation. Use when the user asks what a metric means (xG, PPDA, expected threat, xT), wants learning resources, papers, or courses, is new to football analytics, or wants a learning path. Also use when the user asks about data provider documentation — qualifier IDs, coordinate systems, event types, API schemas, field mappings, identity surfaces, provider ID schemes — or wants to compare providers, look something up in the docs,...Votes: 0GitHub stars: 48
- NutmegFootball data analytics — the single entry point. Use whenever the user mentions football data, xG, expected goals, match analysis, player stats, scouting, match reports, shot maps, passing networks, Premier League data, Champions League stats, scraping FBref/Understat/Transfermarkt, building football charts, judging players or teams from stats (rankings, 'who is the best', 'is he a poor finisher', headlines or social posts about stats), or anything football analytics related. Routes to speci...Votes: 0GitHub stars: 48
- ReviewReview football data code and visualisations for correctness. Use after building a chart, data pipeline, or analysis. Dispatches specialised reviewers for data correctness, chart conventions, visual inspection, and interactive edge cases.Votes: 0GitHub stars: 48
- StoreChoose how and where to store football data. Use when the user asks about database choices, file formats, cloud storage, data pipelines, or how to organise their football data project. Also covers publishing and sharing outputs (Streamlit, Observable, GitHub Pages).Votes: 0GitHub stars: 48
- WrangleTransform, filter, reshape, join, and manipulate football data. Use when the user needs to clean data, merge datasets, convert between formats, handle missing values, work with large datasets, or do any data manipulation task on football data.Votes: 0GitHub stars: 48