feat: benchmarks + recommendations edge functions

- community-benchmarks: computes per-skill median/p25/p75 duration,
  total runs, and success rate from last 30 days of telemetry events.
  Upserts into community_benchmarks table, cached 1 hour.
- community-recommendations: co-occurrence-based skill suggestions
  ("used by X% of /qa users"). Cached 24 hours.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

supabase/functions/community-recommendations/index.ts

@@ -0,0 +1,106 @@
+// gstack community-recommendations edge function
+// Returns skill recommendations based on co-occurrence patterns.
+// Input: ?skills=qa,ship (user's top skills as comma-separated query param)
+// Output: top 3 recommended skills the user hasn't tried yet.
+// Cached for 24 hours via Cache-Control header.
+
+import { createClient } from "https://esm.sh/@supabase/supabase-js@2";
+
+Deno.serve(async (req) => {
+  const supabase = createClient(
+    Deno.env.get("SUPABASE_URL") ?? "",
+    Deno.env.get("SUPABASE_SERVICE_ROLE_KEY") ?? ""
+  );
+
+  try {
+    const url = new URL(req.url);
+    const userSkills = (url.searchParams.get("skills") ?? "")
+      .split(",")
+      .map((s) => s.trim())
+      .filter(Boolean);
+
+    if (userSkills.length === 0) {
+      return new Response(JSON.stringify({ recommendations: [] }), {
+        status: 200,
+        headers: {
+          "Content-Type": "application/json",
+          "Cache-Control": "public, max-age=86400",
+        },
+      });
+    }
+
+    // Query skill_sequences for co-occurring skills
+    const { data: sequences, error } = await supabase
+      .from("skill_sequences")
+      .select("skill_a, skill_b, co_occurrences")
+      .in("skill_a", userSkills)
+      .order("co_occurrences", { ascending: false })
+      .limit(50);
+
+    if (error) throw error;
+
+    // Find skills the user hasn't used yet, ranked by co-occurrence
+    const userSkillSet = new Set(userSkills);
+    const recommendations: Record<
+      string,
+      { co_occurrences: number; paired_with: string[] }
+    > = {};
+
+    for (const seq of sequences ?? []) {
+      if (userSkillSet.has(seq.skill_b)) continue; // already used
+      if (seq.skill_b.startsWith("_")) continue; // skip internal
+
+      if (!recommendations[seq.skill_b]) {
+        recommendations[seq.skill_b] = {
+          co_occurrences: 0,
+          paired_with: [],
+        };
+      }
+      recommendations[seq.skill_b].co_occurrences += seq.co_occurrences;
+      recommendations[seq.skill_b].paired_with.push(seq.skill_a);
+    }
+
+    // Also get total run counts for percentage calculation
+    const { data: benchmarks } = await supabase
+      .from("community_benchmarks")
+      .select("skill, total_runs");
+
+    const totalBySkill: Record<string, number> = {};
+    for (const b of benchmarks ?? []) {
+      totalBySkill[b.skill] = b.total_runs;
+    }
+
+    // Build top 3 recommendations
+    const sorted = Object.entries(recommendations)
+      .sort(([, a], [, b]) => b.co_occurrences - a.co_occurrences)
+      .slice(0, 3)
+      .map(([skill, data]) => {
+        const pairedSkill = data.paired_with[0];
+        const pairedTotal = totalBySkill[pairedSkill] ?? 0;
+        const pct =
+          pairedTotal > 0
+            ? Math.round((data.co_occurrences / pairedTotal) * 100)
+            : 0;
+
+        return {
+          skill,
+          reason: `used by ${pct}% of /${pairedSkill} users`,
+          co_occurrences: data.co_occurrences,
+        };
+      });
+
+    return new Response(JSON.stringify({ recommendations: sorted }), {
+      status: 200,
+      headers: {
+        "Content-Type": "application/json",
+        "Cache-Control": "public, max-age=86400",
+      },
+    });
+  } catch (err) {
+    console.error("Recommendations error:", err);
+    return new Response(JSON.stringify({ recommendations: [] }), {
+      status: 200,
+      headers: { "Content-Type": "application/json" },
+    });
+  }
+});