chore(ab-corpus): add shape + per-category issue inspectors

Two complement scripts that ride alongside replay-detectors.ts:

- inspect-shape.ts: bucket every applied row's root by (width-bucket /
  height-bucket / aspect-ratio / child-count). Used 2026-05-10 to
  diagnose why detectEdgeSectionPadding scored 0 hits on a 220-row
  mobile subset — turned out 49% of mobile rows produce roots with a
  string-typed width ("fill_container" / "fit_content") because the
  element-tools path emits component fragments, not pages. The
  detector predicate `typeof width === 'number'` correctly skips them
  → 0 hits is the EVAL HARNESS coverage gap, not a detector bug.

- inspect-issue-category.ts: print every issue in a chosen category
  with row id + node id + reason. Generic version of inspect-contrast-
  hits.ts. 2026-05-10 used it to validate
  excessive-frame-effects (4/4 TP — blur 48 cards + OTP slot spread)
  and the two borderline mixed-sibling hits (header padding outlier,
  spacer cornerRadius outlier — known role-aware limitation, 0.2%
  noise rate, accepted).

Together with replay-detectors.ts these three give a fast empirical
loop for tuning a detector against real corpus output without burning
fresh API tokens.

scripts/ab-corpus/inspect-issue-category.ts

@@ -0,0 +1,59 @@
+/**
+ * Print every issue of a specific category across an entire run, with the
+ * row id, node id, and reason. Generic version of inspect-contrast-hits
+ * — used for spot-checking any single detector's true-positive vs
+ * false-positive rate against a real corpus.
+ */
+import { readFileSync } from 'node:fs';
+import { join } from 'node:path';
+import {
+  detectAllIssues,
+  parseModelOutput,
+  type Issue,
+  type IssueCategory,
+} from '@zseven-w/pen-ai-skills';
+import { applyToFreshDoc } from './apply';
+
+interface JsonlRow {
+  promptId: string;
+  category: string;
+  difficulty: string;
+  variant: string;
+  rawOutput: string;
+}
+
+async function main(): Promise<void> {
+  const runId = process.argv[2];
+  const wantCat = process.argv[3] as IssueCategory | undefined;
+  if (!runId || !wantCat) {
+    console.error(
+      'usage: bun run scripts/ab-corpus/inspect-issue-category.ts <run-id> <category>',
+    );
+    process.exit(1);
+  }
+  const path = join(import.meta.dir, 'runs', runId, 'scores.jsonl');
+  const rows: JsonlRow[] = readFileSync(path, 'utf-8')
+    .split('\n')
+    .filter(Boolean)
+    .map((l) => JSON.parse(l));
+
+  for (const r of rows) {
+    const parsed = parseModelOutput(r.rawOutput);
+    if (parsed.kind === 'garbage') continue;
+    const result = await applyToFreshDoc(parsed);
+    if (!result.ok || !result.doc) continue;
+    const root = result.doc.children?.[0] ?? null;
+    if (!root) continue;
+    const issues: Issue[] = detectAllIssues(root, result.doc).filter((i) => i.category === wantCat);
+    if (issues.length === 0) continue;
+    console.log(`\n=== ${r.promptId} [${r.category}/${r.difficulty}/${r.variant}] (${issues.length} hits) ===`);
+    for (const issue of issues) {
+      console.log(`  ${issue.nodeId.padEnd(50)}  ${issue.reason}`);
+    }
+  }
+}
+
+main().catch((e) => {
+  console.error(e);
+  process.exit(1);
+});

scripts/ab-corpus/inspect-shape.ts

@@ -0,0 +1,79 @@
+/**
+ * Dump the doc shape (root width / height / layout / direct-children
+ * roles) for every applied row in a category. Used 2026-05-10 to figure
+ * out why detectEdgeSectionPadding scored 0 hits on the 220-row mobile
+ * subset of `2026-05-03-ab-v8-v1-default` — needed to see whether the
+ * model is producing page-shaped roots at all or just fragments that
+ * don't look like a mobile page.
+ */
+import { readFileSync } from 'node:fs';
+import { join } from 'node:path';
+import { parseModelOutput } from '@zseven-w/pen-ai-skills';
+import { applyToFreshDoc } from './apply';
+
+interface JsonlRow {
+  promptId: string;
+  category: string;
+  difficulty: string;
+  variant: string;
+  rawOutput: string;
+}
+
+async function main(): Promise<void> {
+  const runId = process.argv[2];
+  const wantCategory = process.argv[3] ?? 'mobile';
+  if (!runId) {
+    console.error('usage: bun run scripts/ab-corpus/inspect-shape.ts <run-id> [category]');
+    process.exit(1);
+  }
+  const path = join(import.meta.dir, 'runs', runId, 'scores.jsonl');
+  const rows: JsonlRow[] = readFileSync(path, 'utf-8')
+    .split('\n')
+    .filter(Boolean)
+    .map((l) => JSON.parse(l));
+
+  const buckets = new Map<string, number>();
+  for (const r of rows) {
+    if (r.category !== wantCategory) continue;
+    const parsed = parseModelOutput(r.rawOutput);
+    if (parsed.kind === 'garbage') continue;
+    const result = await applyToFreshDoc(parsed);
+    if (!result.ok || !result.doc) continue;
+    const root = result.doc.children?.[0] as
+      | (Record<string, unknown> & { width?: unknown; height?: unknown; children?: unknown[] })
+      | undefined;
+    if (!root) continue;
+    const w = root.width;
+    const h = root.height;
+    const childCount = Array.isArray(root.children) ? root.children.length : 0;
+    const wKey =
+      typeof w === 'number'
+        ? w >= 320 && w <= 480
+          ? 'mobile-w'
+          : w > 480 && w <= 800
+            ? 'tablet-w'
+            : w > 800
+              ? 'desktop-w'
+              : 'tiny-w'
+        : `non-numeric:${typeof w}`;
+    const hKey =
+      typeof h === 'number'
+        ? h >= 568
+          ? 'tall'
+          : 'short'
+        : `non-numeric:${typeof h}`;
+    const ratioKey =
+      typeof w === 'number' && typeof h === 'number' && w > 0 && h / w >= 1.5 ? 'aspect-ok' : 'aspect-low';
+    const key = `${wKey} / ${hKey} / ${ratioKey} / children=${childCount}`;
+    buckets.set(key, (buckets.get(key) ?? 0) + 1);
+  }
+
+  console.log(`shapes for category=${wantCategory}:`);
+  const sorted = Array.from(buckets.entries()).sort((a, b) => b[1] - a[1]);
+  for (const [k, v] of sorted) console.log(`  ${String(v).padStart(4)}  ${k}`);
+}
+
+main().catch((e) => {
+  console.error(e);
+  process.exit(1);
+});