/lux:data-explorer — Interactive Data Explorer

You are composing an interactive data explorer in the Lux display window. A data explorer lets the user filter, search, and drill into tabular data. The layout is: filter controls at the top, data table in the middle, detail panel at the bottom. Interaction is the core — the user changes filters, the table updates.

Phase 1: Identify the Data

Determine what to explore. Look at $ARGUMENTS and conversation context:

• If the user specified a data source (e.g., "explore the test results"), read/parse it
• If the user pointed to a file, command output, or API response, extract rows and columns
• If vague ("let me explore the data"), ask what data set — don't guess

You need:

• Rows — a list of records (dicts or arrays)
• Columns — field names for the table headers (3-6 columns is the sweet spot; more than 8 becomes unreadable)

Phase 2: Design the Filters

Prefer built-in table filters (DES-018) — these run at 60fps in the display server with zero round trips. Use separate filter elements + recv()/update() only when you need custom logic that built-in filters can't handle (e.g., numeric ranges, cross-field filters, external lookups).

Built-in Filters (preferred)

Add a filters array to the table element. Two types are available:

Data pattern	Filter type	Config
Free-text search (name, title, description)	search	column: [0, 1] — which columns to search
Categorical field (status, type, category)	combo	column: 2, items: ["All", "open", "closed"]

Rules:

• 1-3 filters is ideal. More than 4 overwhelms the UI.
• Always include a text search if the data has a name/title/description field.
• Include only values that exist in the data for combo items.
• The first combo item should be "All" (no filter).

Built-in Detail Panel (preferred)

Add a detail object to the table for drill-down. This renders a list/detail view with fields and body text, driven entirely by data — no round trips.

Separate Filter Elements (advanced)

For filters that built-in types can't handle (numeric ranges, sliders, cross-field logic), use separate elements and the recv()/update() loop:

Data pattern	Element kind
Boolean field (active, archived)	checkbox
Numeric range (score, price)	slider
Custom compound filter	input_text + custom logic

Phase 3: Compose the Layout

Build the element tree following the data explorer pattern.

Pattern: Table with Built-in Filters + Detail

A single table element with filters and detail gives you a complete data explorer with zero round trips — search, filter, row selection, and detail panel all run at 60fps in the display server.

Reference Example

This is the canonical form — a searchable, filterable list of issues with drill-down detail. Adapt freely to any tabular data: search results, log entries, test cases, inventory, API responses.

{
  "scene_id": "data-explorer",
  "title": "Issue Explorer",
  "elements": [
    {
      "kind": "table", "id": "data-table",
      "columns": ["ID", "Title", "Status", "Priority", "Assignee"],
      "rows": [
        ["ISS-001", "Fix login timeout", "Open", "P1", "alice"],
        ["ISS-002", "Add dark mode", "In Progress", "P2", "bob"],
        ["ISS-003", "Update API docs", "Open", "P3", "carol"],
        ["ISS-004", "Memory leak in worker", "Open", "P0", "alice"],
        ["ISS-005", "Refactor auth module", "Closed", "P2", "bob"]
      ],
      "filters": [
        {"type": "search", "column": [0, 1], "hint": "Filter by ID or title..."},
        {"type": "combo", "column": 2, "items": ["All", "Open", "In Progress", "Closed"], "label": "Status"},
        {"type": "combo", "column": 3, "items": ["All", "P0", "P1", "P2", "P3"], "label": "Priority"}
      ],
      "detail": {
        "fields": ["ID", "Status", "Priority", "Assignee", "Created"],
        "rows": [
          ["ISS-001", "Open", "P1", "alice", "2026-03-01"],
          ["ISS-002", "In Progress", "P2", "bob", "2026-03-02"],
          ["ISS-003", "Open", "P3", "carol", "2026-03-03"],
          ["ISS-004", "Open", "P0", "alice", "2026-03-04"],
          ["ISS-005", "Closed", "P2", "bob", "2026-03-05"]
        ],
        "body": [
          "The login flow times out after 30s on slow connections...",
          "Add system-wide dark mode toggle with persistent preference.",
          "API docs are outdated after the v2 migration.",
          "Worker process leaks ~10MB/hour under sustained load.",
          "Auth module has accumulated tech debt — extract into clean service."
        ]
      },
      "flags": ["borders", "row_bg"]
    }
  ]
}

Layout Adaptations

• No categorical fields: Skip combo filters, use only text search
• Many categories: Use tab_bar with one tab per major category instead of a combo filter
• Large data sets (50+ rows): Paginate — show first N rows, add "Next" / "Previous" buttons
• Hierarchical data: Use tree elements instead of a flat table
• Side-by-side detail: Use group with layout: "columns" to place the table and detail panel next to each other instead of stacked

Phase 4: Display

Call set_theme("imgui_colors_light") before showing — light themes work best for data-dense views. Then call show() with the composed element tree.

Phase 5: Interaction

With built-in filters and detail, the data explorer is fully interactive without any recv()/update() loop. Tell the user:

• Search and filter dropdowns work instantly (no round trips)
• Click any row to see its full details in the side panel
• The result count updates automatically as filters narrow the view

When recv/update IS needed

Use the recv()/update() loop only for operations that require LLM orchestration:

• Actions on selected row — "Close this issue", "Assign to me" (requires external API calls)
• Data refresh — re-reading from a changing data source (add a "Refresh" button)
• Custom filter logic — numeric ranges, cross-field filters, or external lookups that built-in filters can't handle

{
  "scene_id": "data-explorer",
  "patches": [
    {"id": "data-table", "set": {"rows": [/* refreshed data */]}}
  ]
}

Phase 6: Refresh (Optional)

If the underlying data can change (live logs, query results), add a button with id: "refresh" in the filters row. When clicked, re-read the data source and recompute the filtered view.