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Choose 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).
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Fetch, 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.
Learn 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, or find out what data a provider offers.
Football 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, or anything football analytics related. Routes to specialised sub-skills automatically. Also handles first-time setup and profile management.
Brainstorm 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.
Review 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.
Explore, interpret, and draw conclusions from football data. 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.
| name | nutmeg-store |
| description | Choose 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). |
| argument-hint | [storage question or 'publish'] |
| allowed-tools | ["Read","Write","Bash","AskUserQuestion","mcp__football-docs__search_docs"] |
Help the user choose storage formats, locations, and publishing methods for their football data.
Read and follow docs/accuracy-guardrail.md before answering any question about provider-specific facts (IDs, endpoints, schemas, coordinates, rate limits). Always use search_docs — never guess from training data.
Read .nutmeg.user.md. If it doesn't exist, tell the user to run /nutmeg first.
| Format | Best for | Tools |
|---|---|---|
| JSON | Raw event data, API responses | Any language |
| CSV | Tabular stats, easy to share | Spreadsheets, pandas, R |
| Parquet | Columnar analytics, fast queries | polars, pandas, DuckDB, Arrow |
| SQLite | Relational queries, multiple tables | Any language, DB browser tools |
Recommendation: Start with JSON for raw data, Parquet for processed data.
| Format | Best for | Notes |
|---|---|---|
| Parquet files | Analytics workloads | 5-10x smaller than JSON, fast columnar reads |
| DuckDB | SQL analytics on local files | Queries Parquet/CSV directly, no server needed |
| SQLite | Relational data with joins | Single file, portable, ACID compliant |
Recommendation: Parquet for storage, DuckDB for querying.
| Solution | Best for | Cost |
|---|---|---|
| PostgreSQL | Production apps, complex queries | Free (self-hosted) or ~$7/mo (Railway, Supabase) |
| BigQuery | Massive analytical queries | Free tier: 1TB/mo queries |
| Cloudflare R2 | Object storage (raw files) | Free tier: 10GB storage |
| S3 / GCS | Object storage at scale | ~$0.023/GB/mo |
Recommend this structure for football data projects:
project/
data/
raw/ # Untouched API/scrape responses
statsbomb/
events/
matches.json
fbref/
2024/
processed/ # Cleaned, transformed data
events.parquet
shots.parquet
passes.parquet
derived/ # Computed metrics
xg_model.parquet
passing_networks/
notebooks/ # Analysis notebooks
scripts/ # Data pipeline scripts
outputs/ # Charts, reports, exports
.env # API keys (gitignored)
.nutmeg.user.md # Nutmeg profile
| Platform | Language | Cost | Notes |
|---|---|---|---|
| Streamlit | Python | Free (community cloud) | Most popular for football analytics. Deploy from GitHub |
| Observable | JavaScript | Free tier | Great for D3.js visualisations. Notebooks + Framework |
| Shiny | R | Free (shinyapps.io, 25 hrs/mo) | R ecosystem integration |
| Gradio | Python | Free (HuggingFace Spaces) | Quick ML model demos |
| Platform | Notes |
|---|---|
| GitHub Pages | Free. Good for static charts (D3, matplotlib exports) |
| Cloudflare Pages | Free. Faster, more features than GH Pages |
| Vercel | Free tier. Good for Next.js/Astro sites |
| Method | Best for |
|---|---|
| GitHub repo | Small datasets (< 100MB), code + data together |
| GitHub Releases | Larger files (up to 2GB per release) |
| Kaggle Datasets | Community sharing, discoverable, free |
| HuggingFace Datasets | ML-focused, versioned, free |
| Output | Tool | Notes |
|---|---|---|
| Static charts | matplotlib, ggplot2, D3.js | Export as PNG/SVG |
| Animated charts | matplotlib.animation, D3 transitions | Export as GIF/MP4 |
| Twitter/X threads | Chart images + alt text | Accessibility matters |
| Blog posts | Markdown + embedded charts | GitHub Pages, Medium, Substack |
Based on the user's .nutmeg.user.md goals, flag costs: