feat(ultraplan-local): v1.6.0 — /ultraresearch-local deep research command

Add /ultraresearch-local for structured research combining local codebase
analysis with external knowledge via parallel agent swarms. Produces research
briefs with triangulation, confidence ratings, and source quality assessment.

New command: /ultraresearch-local with modes --quick, --local, --external, --fg.
New agents: research-orchestrator (opus), docs-researcher, community-researcher,
security-researcher, contrarian-researcher, gemini-bridge (all sonnet).
New template: research-brief-template.md.

Integration: --research flag in /ultraplan-local accepts pre-built research
briefs (up to 3), enriches the interview and exploration phases. Planning
orchestrator cross-references brief findings during synthesis.

Design principle: Context Engineering — right information to right agent at
right time. Research briefs are structured artifacts in the pipeline:
ultraresearch → brief → ultraplan --research → plan → ultraexecute.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

scripts/analytics/src/reports/heatmap.ts

@@ -0,0 +1,85 @@
+import type { PostAnalytics, DayOfWeekMetrics, HeatmapReport } from "../models/types.js";
+
+const DAY_NAMES = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"];
+
+// Convert JS getDay() (0=Sun) to ISO weekday (1=Mon, 7=Sun)
+function toISOWeekday(jsDay: number): number {
+  return jsDay === 0 ? 7 : jsDay;
+}
+
+/**
+ * Generate a day-of-week performance heatmap from post analytics data.
+ * Groups posts by day of week and calculates average metrics per day.
+ */
+export function generateHeatmap(posts: PostAnalytics[]): HeatmapReport {
+  // Initialize buckets for all 7 days (ISO: 1=Mon to 7=Sun)
+  const buckets: Map<number, PostAnalytics[]> = new Map();
+  for (let i = 1; i <= 7; i++) {
+    buckets.set(i, []);
+  }
+
+  // Group posts by ISO weekday
+  for (const post of posts) {
+    const jsDay = new Date(post.publishedDate).getUTCDay();
+    const isoDay = toISOWeekday(jsDay);
+    buckets.get(isoDay)!.push(post);
+  }
+
+  // Build metrics per day
+  const byDayOfWeek: DayOfWeekMetrics[] = [];
+  for (let isoDay = 1; isoDay <= 7; isoDay++) {
+    const dayPosts = buckets.get(isoDay)!;
+    const jsDay = isoDay === 7 ? 0 : isoDay;
+    const dayName = DAY_NAMES[jsDay];
+
+    if (dayPosts.length === 0) {
+      byDayOfWeek.push({
+        dayName,
+        dayIndex: isoDay,
+        postCount: 0,
+        avgImpressions: 0,
+        avgEngagementRate: 0,
+      });
+      continue;
+    }
+
+    const totalImpressions = dayPosts.reduce((sum, p) => sum + p.metrics.impressions, 0);
+    const totalEngagement = dayPosts.reduce((sum, p) => sum + p.metrics.engagementRate, 0);
+    const bestPost = dayPosts.reduce((best, p) =>
+      p.metrics.impressions > best.metrics.impressions ? p : best
+    );
+
+    byDayOfWeek.push({
+      dayName,
+      dayIndex: isoDay,
+      postCount: dayPosts.length,
+      avgImpressions: Math.round(totalImpressions / dayPosts.length),
+      avgEngagementRate: parseFloat((totalEngagement / dayPosts.length).toFixed(1)),
+      bestPost,
+    });
+  }
+
+  // Find best days
+  const daysWithPosts = byDayOfWeek.filter(d => d.postCount > 0);
+  const bestDayImpressions = daysWithPosts.length > 0
+    ? daysWithPosts.reduce((best, d) => d.avgImpressions > best.avgImpressions ? d : best).dayName
+    : "N/A";
+  const bestDayEngagement = daysWithPosts.length > 0
+    ? daysWithPosts.reduce((best, d) => d.avgEngagementRate > best.avgEngagementRate ? d : best).dayName
+    : "N/A";
+
+  // Date range
+  const sortedDates = posts.map(p => p.publishedDate).sort();
+  const dateRange = posts.length > 0
+    ? { from: sortedDates[0], to: sortedDates[sortedDates.length - 1] }
+    : { from: "", to: "" };
+
+  return {
+    generatedAt: new Date().toISOString(),
+    postsAnalyzed: posts.length,
+    dateRange,
+    byDayOfWeek,
+    bestDayImpressions,
+    bestDayEngagement,
+  };
+}