/brainstorm

name: nutmeg-brainstorm
description: "Ideas for football data visualisations: chart types and designs for match reports, player profiles, dashboards and other graphics, with real-world examples. Use when the user wants options for how to show their data."
argument-hint: "[what to visualise, e.g. 'shot map for a single match' or 'player comparison radar']"
allowed-tools: ["Read", "Write", "Bash", "WebSearch", "WebFetch", "Agent", "mcp__plugin_nutmeg_football-docs__search_docs", "mcp__football-docs__search_docs"]

Brainstorm

Help the user explore and choose football data visualisation approaches through research-backed ideation and collaborative refinement.

Accuracy

Read and follow `${CLAUDE_PLUGIN_ROOT}/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.

First: check profile

Read `.nutmeg.user.md`. If it doesn't exist, continue with sensible defaults (Python and pandas, intermediate level) and suggest running `/nutmeg` setup at the end. Use their profile for:

• Programming language (Python/R/JS)
• Visualisation libraries they use (mplsoccer, matplotlib, ggplot2, d3, Observable)
• Experience level (adapt complexity of suggestions)
• Available data sources (what providers they have access to)

References (load as needed)

This skill has two reference documents. Load them when relevant — don't read both upfront for every question.

| Reference | When to load | Path | |---|---|---| | **Chart Canon** | When discussing specific football chart types, conventions, or anti-patterns | `${CLAUDE_PLUGIN_ROOT}/skills/brainstorm/references/chart-canon.md` | | **Viz Styles** | When helping the user choose a design approach or aesthetic direction | `${CLAUDE_PLUGIN_ROOT}/skills/brainstorm/references/viz-styles.md` | | **Campos Bridge** | Strong React/campos signal in cwd — loads the bridge; soft signals surface a mention only | `${CLAUDE_PLUGIN_ROOT}/skills/_shared/campos-bridge.md` |

Process

Phase 1: Understand the goal

Ask one question at a time to understand:

• **What data?** Match events, player stats, team stats, tracking data?
• **What story?** What insight or comparison are they trying to show?
• **What context?** Dashboard, social media, blog post, presentation, academic paper?
• **What format?** Static image, interactive, animation, part of a larger report?

Don't ask all of these upfront. Start with the most important one based on what they said, then follow up.

**Detect React/campos context before proposing approaches:**

• **Strong signal** — Read cwd's `package.json`; if it contains `react` or `@withqwerty/campos-react` in `dependencies`/`devDependencies`, load `${CLAUDE_PLUGIN_ROOT}/skills/_shared/campos-bridge.md` and include campos options alongside Python/R in Phase 3.
• **Soft signal** — user mentions React/TS/Next/Vite/campos/browser chart in conversation AND cwd does not have `pyproject.toml` / `requirements.txt` / `renv.lock` / `Gemfile`. Do not auto-load; mention the bridge exists and offer to load if the user confirms they want a React chart.
• **Conflicting signal** — cwd is a Python/R/Ruby project. Keep the existing mplsoccer/ggplot2 path; soft signals are ignored. Strong signals still win (monorepo case).
• **Integration-self signal** — cwd IS the campos or nutmeg repo itself (see the bridge doc for detection). Downgrade to soft — working on the integration is not the same as using it.
• **No signal** — existing path unchanged.

**Determine their style early.** Load `${CLAUDE_SKILL_DIR}/references/viz-styles.md` and identify which style fits their context (Analytical, Social Media, Editorial, Minimal/Academic). For advanced users, skip the style discussion — they know what they want. Focus on the specific technique.

Phase 2: Research approaches

Before proposing options, research what works well.

**Search strategy (follow this order):**

1. **Check football-docs:**

• `search_docs(query="[viz type]", provider="mplsoccer")` — mplsoccer has extensive viz docs
• `search_docs(query="[concept] visualisation")` — check if any provider docs cover this

2. **Load the chart canon** if the question involves a standard football chart type:

• Read `${CLAUDE_PLUGIN_ROOT}/skills/brainstorm/references/chart-canon.md`
• Check conventions, known weaknesses, and anti-patterns for the chart type

3. **Search the web for real-world examples:**

• Search: `"football analytics" "[viz type]" site:twitter.com OR site:x.com`
• Search: `"[viz type]" football "made with" mplsoccer OR matplotlib OR ggplot2`
• Search: `football data viz "[specific chart]" tutorial`
• Search for key practitioners: Karun Singh, Tom Worville, John Burn-Murdoch, Mark Thompson, StatsBomb

4. **Check GitHub for open implementations:**

• Search: `site:github.com football viz "[chart type]"`

**Report what you find before proposing options.** Show 2-3 real examples with links and explain what makes each effective.

Phase 3: Propose approaches

Based on research, propose 2-3 visualisation approaches. For each:

• **What it looks like** — describe the chart type, layout, key visual elements
• **Why it works** — for their specific goal and audience
• **Complexity** — how hard to build with their tools and experience level
• **Example reference** — link to a real-world example if found
• **Trade-offs** — what this approach emphasises vs what it downplays

Lead with your recommendation and explain why. Adapt to their style:

• For **dashboard** users: emphasise clarity, interactivity, filtering
• For **social media** users: emphasise visual impact, self-contained-ness
• For **editorial** users: emphasise narrative power, annotation, metaphor
• For **academic** users: emphasise precision, reproducibility, uncertainty

**When the campos bridge is loaded (React/campos strong signal):** at least one of the 2-3 proposals must be