refactor: remove as= override and render data-spark at runtime

- Drop resolveBlockAs and the as= attribute; scanDoc now switches
  directly on role and warns on unknown roles instead of silently
  stripping
- md-table derives data-spark from its loaded CSV via
  parseSparkTableCsv; delete injectSparkDirectives and the second
  registry pass so pathIndex is exactly scanDoc output
- Align spark-table dice header regex with isSparkTableHeader

src/components/journal/completions.ts

@@ -110,7 +110,7 @@ async function scanClientSide(): Promise<JournalCompletions> {
   const paths = await getPathsByExtension("md");
 
   // Load all .md content into a raw index, then build the registry through
-  // the same shared pipeline as the CLI (scanDoc + spark injection).
+  // the same shared pipeline as the CLI (scanDoc).
   const index: Record<string, string> = {};
   for (const filePath of paths) {
     const content = await getIndexedData(filePath);
@@ -122,8 +122,8 @@ async function scanClientSide(): Promise<JournalCompletions> {
   activeRegistry = registry;
 
   // Write the processed (stripped) content back into the file index so
-  // Article/md-embed render the same content as CLI mode (spark tables
-  // coerced, attributed blocks processed, data-spark injected).
+  // Article/md-embed render the same content as CLI mode (attributed blocks
+  // processed by role).
   for (const [path, content] of Object.entries(registry.pathIndex)) {
     setIndexedData(path, content);
   }

src/components/md-table.tsx

@@ -10,10 +10,11 @@ import {
 import { parseMarkdown } from "../markdown";
 import { parseCSVString, CSV, processVariables } from "./utils/csv-loader";
 import { resolveContentRef } from "./utils/resolve-content";
+import { parseSparkTableCsv } from "./utils/spark-table";
 import {
   areAllLabelsNumeric,
   weightedRandomIndex,
-} from "./utils/weighted-random";
+} from "./utils/weighted-random";;
 
 export interface TableProps {
   roll?: boolean;
@@ -59,16 +60,23 @@ customElement(
         if (content === null) {
           throw new Error(`Failed to resolve table content: "${ref}"`);
         }
-        return parseCSVString(content);
+        return content;
       },
     );
 
-    // 当数据加载完成后更新 rows
+    // 当数据加载完成后更新 rows，并为火花表设置 data-spark（供 RevealManager
+    // 悬停触发 /roll）。data-spark 在渲染时派生，不在扫描时注入。
     createEffect(() => {
-      const data = csvData();
-      if (data) {
-        // 将加载的数据赋值给 rows，CSV 类型已经包含 sourcePath 等属性
-        setRows(data as unknown as CSV<TableRow>);
+      const content = csvData();
+      if (!content) return;
+      setRows(parseCSVString(content) as unknown as CSV<TableRow>);
+      if (element) {
+        const meta = parseSparkTableCsv(content);
+        if (meta) {
+          element.setAttribute("data-spark", meta.slug);
+        } else {
+          element.removeAttribute("data-spark");
+        }
       }
     });
 

src/components/utils/spark-table.ts

@@ -53,7 +53,9 @@ export interface SparkTableMeta {
 // CSV parsing
 // ---------------------------------------------------------------------------
 
-const DICE_HEADER_RE = /^d\d+$/i;
+// Matches the scanner's `isSparkTableHeader` (content-registry): plain dice,
+// counts, and modifiers all count as spark-table first columns.
+const DICE_HEADER_RE = /^\d*d\d+(?:[+-]\d+)?$/i;
 
 /**
  * Parse a CSV string into a SparkTableMeta.