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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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.
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.
Calculate 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.
| name | nutmeg-review |
| description | 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. |
| argument-hint | [what to review, e.g. 'my shot map code' or 'the passmap page'] |
| allowed-tools | ["Read","Write","Bash","Glob","Grep","Agent","WebFetch","mcp__football-docs__search_docs"] |
Dispatch specialised reviewers to check football data code and visualisations for correctness, convention compliance, and edge cases.
Read and follow docs/accuracy-guardrail.md before answering any question about provider-specific facts.
Read .nutmeg.user.md. If it doesn't exist, tell the user to run /nutmeg first.
Look at what the user wants reviewed. Read the relevant files. Then decide which reviewers to dispatch:
| Signal | Dispatch |
|---|---|
| Code processes football data (fetching, filtering, transforming, computing metrics) | data-reviewer agent |
| Code renders a chart or visualisation | chart-reviewer agent (Mode 1: Code Review) |
| User provides a URL or says "check how it looks" | chart-reviewer agent (Mode 2: Visual Inspection) |
| Chart has filters, tooltips, state, or dynamic data | chart-reviewer agent (Mode 3: Interactive Edge Cases) |
Code imports @withqwerty/campos-* (React + campos) | chart-reviewer agent (Mode 4: React + Campos) — pass skills/_shared/campos-bridge.md in context |
| Code does both data processing AND chart rendering | Both agents in parallel |
Always dispatch at least one. If unclear, dispatch both — redundant findings are better than missed issues.
Detection for Mode 4: grep the reviewed files for @withqwerty/campos- or from "@withqwerty/campos. Any match activates Mode 4 alongside Mode 1.
Spawn agents in parallel when dispatching multiple. Each agent receives:
Review the football data code in [FILE_PATHS].
The user is working with [PROVIDER] data in [LANGUAGE].
They built: [DESCRIPTION]
Their concern: [WHAT_THEY_SAID]
Follow the full review checklist in your agent prompt. Use search_docs to verify
provider-specific facts (coordinate systems, qualifier IDs, event types).
Review the chart code in [FILE_PATHS].
Mode(s): [Code Review / Visual Inspection / Interactive Edge Cases]
The user is building: [DESCRIPTION]
Their concern: [WHAT_THEY_SAID]
Stack: [LANGUAGE + LIBRARIES from profile]
[If visual inspection: URL or instructions to render]
Load skills/brainstorm/references/chart-canon.md for convention checking.
After both agents report back:
If the chart-reviewer's code review finds potential rendering issues but can't confirm without seeing the output, suggest:
"The code review found [N] potential rendering issues. Want me to visually inspect the chart? I'll need a URL or you can run it locally."
Don't require visual inspection — many users can't easily serve their chart locally. Code review alone catches most issues.
If findings are found:
If no findings: