fix: pipeline P0/P1 — rating bug, retry, health score, blog scrape

## P0 버그 수정 (즉시 영향)

### fix(collect-channel-data): 강남언니 rating 오변환 제거
- 기존: `rating ≤ 5 → ×2` 로직으로 4.8/10을 9.6/10으로 잘못 변환
- Firecrawl 프롬프트가 이미 0-10 반환 지시 → rawValue 직접 신뢰

### fix(generate-report): Perplexity 단일 fetch → fetchWithRetry
- maxRetries:2, backoffMs:[5000,15000], timeoutMs:90s 설정
- 기존: 일시적 429/타임아웃 시 리포트 생성 전체 실패

## P1 기능 추가 (데이터 품질)

### feat(collect-channel-data): channel_snapshots health_score 계산
- `computeHealthScore(channel, data)` 함수 추가 (채널별 0-100 스코어)
- Instagram: followers 기반 선형 보간 + posts bonus
- YouTube: subscribers 기반 + video count bonus
- 강남언니: rating×7 + reviews bonus (max 30pt)
- Google Maps: rating×12 + reviews bonus (max 40pt)
- Naver Blog: presence (50pt) + 언급 수 bonus (max 30pt)
- 모든 channel_snapshots INSERT에 health_score 포함

### feat(collect-channel-data): 네이버 블로그 공식 컨텐츠 스크랩 추가
- 기존: Naver Search API로 3rd-party 언급만 수집
- 추가: Registry에서 확인된 공식 블로그 URL을 Firecrawl로 직접 스크랩
  - 총 게시글 수, 최근 게시물 (제목/날짜/요약), 카테고리 추출
  - 실패 시 non-critical — 기존 Naver Search 결과는 항상 유지

## docs: PIPELINE_IMPROVEMENT_PLAN 감사 결과 반영
- Sprint 0 (Vision), Sprint 1, Sprint 2 완료 표시
- WP-10, WP-11 완료 표시
- 2026-04-07 전수 감사 섹션 추가 (구현 완료/수정/남은 Gap 표)

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

supabase/functions/collect-channel-data/index.ts

@@ -52,6 +52,98 @@ async function runApifyActor(actorId: string, input: Record<string, unknown>, to
   return itemsRes.json();
 }
 
+// ─── Health Score Calculator ───────────────────────────────────────────────
+// Computes a 0-100 health score per channel based on follower/rating/review
+// benchmarks for Korean plastic surgery clinics. Used in channel_snapshots.
+//
+// Design: linear interpolation between (floor, floorScore) and (ceil, ceilScore).
+// E.g., Instagram at 5K followers → mid-range between 1K=40 and 10K=70.
+
+function lerp(value: number, low: number, high: number, scoreLow: number, scoreHigh: number): number {
+  if (value <= low) return scoreLow;
+  if (value >= high) return scoreHigh;
+  return Math.round(scoreLow + (value - low) / (high - low) * (scoreHigh - scoreLow));
+}
+
+function computeHealthScore(channel: string, data: Record<string, unknown>): number {
+  const n = (v: unknown): number => typeof v === 'number' ? v : (parseInt(String(v || 0)) || 0);
+
+  switch (channel) {
+    case 'instagram': {
+      // followers: 0→20, 1K→40, 10K→70, 50K→90, 100K+→100
+      const followers = n(data.followers);
+      const fScore = followers >= 100_000 ? 100
+        : followers >= 50_000 ? lerp(followers, 50_000, 100_000, 90, 100)
+        : followers >= 10_000 ? lerp(followers, 10_000, 50_000, 70, 90)
+        : followers >= 1_000  ? lerp(followers, 1_000, 10_000, 40, 70)
+        : lerp(followers, 0, 1_000, 20, 40);
+      // posts bonus: +5 if active (≥ 50 posts)
+      const posts = n(data.posts);
+      return Math.min(fScore + (posts >= 50 ? 5 : 0), 100);
+    }
+    case 'youtube': {
+      // subscribers: 0→20, 500→40, 5K→65, 50K→85, 200K+→100
+      const subs = n(data.subscribers);
+      const sScore = subs >= 200_000 ? 100
+        : subs >= 50_000 ? lerp(subs, 50_000, 200_000, 85, 100)
+        : subs >= 5_000  ? lerp(subs, 5_000, 50_000, 65, 85)
+        : subs >= 500    ? lerp(subs, 500, 5_000, 40, 65)
+        : lerp(subs, 0, 500, 20, 40);
+      // video count bonus: +5 if ≥ 20 videos
+      const videos = n(data.totalVideos);
+      return Math.min(sScore + (videos >= 20 ? 5 : 0), 100);
+    }
+    case 'facebook': {
+      // followers: 0→20, 500→35, 5K→60, 20K→80, 50K+→100
+      const followers = n(data.followers);
+      return followers >= 50_000 ? 100
+        : followers >= 20_000 ? lerp(followers, 20_000, 50_000, 80, 100)
+        : followers >= 5_000  ? lerp(followers, 5_000, 20_000, 60, 80)
+        : followers >= 500    ? lerp(followers, 500, 5_000, 35, 60)
+        : lerp(followers, 0, 500, 20, 35);
+    }
+    case 'gangnamUnni': {
+      // rating /10: max 70pts. reviews: 0→0, 100→10, 1000→20, 10000→30
+      const rating = n(data.rating);
+      const reviews = n(data.totalReviews);
+      const rScore = Math.round(Math.min(rating / 10, 1.0) * 70);
+      const rvScore = reviews >= 10_000 ? 30 : reviews >= 1_000 ? lerp(reviews, 1_000, 10_000, 20, 30)
+        : reviews >= 100 ? lerp(reviews, 100, 1_000, 10, 20)
+        : lerp(reviews, 0, 100, 0, 10);
+      return Math.min(rScore + rvScore, 100);
+    }
+    case 'googleMaps': {
+      // rating /5: max 60pts. reviews: 0→0, 50→10, 500→25, 5000→40
+      const rating = n(data.rating);
+      const reviews = n(data.reviewCount);
+      const rScore = Math.round(Math.min(rating / 5, 1.0) * 60);
+      const rvScore = reviews >= 5_000 ? 40 : reviews >= 500 ? lerp(reviews, 500, 5_000, 25, 40)
+        : reviews >= 50 ? lerp(reviews, 50, 500, 10, 25)
+        : lerp(reviews, 0, 50, 0, 10);
+      return Math.min(rScore + rvScore, 100);
+    }
+    case 'naverBlog': {
+      // Presence-based: official handle = 50, mention count bonus up to +30, activity +20
+      const hasHandle = Boolean(data.officialBlogHandle);
+      const total = n(data.totalResults);
+      const mentionScore = total >= 1000 ? 30 : total >= 100 ? lerp(total, 100, 1000, 15, 30) : lerp(total, 0, 100, 0, 15);
+      return Math.min((hasHandle ? 50 : 20) + mentionScore, 100);
+    }
+    case 'naverPlace': {
+      // rating /5: max 60pts. reviews: 0→0, 100→15, 1000→30, 10000→40
+      const rating = n(data.rating);
+      const reviews = n(data.reviewCount) || n(data.reviews);
+      const rScore = Math.round(Math.min(rating / 5, 1.0) * 60);
+      const rvScore = reviews >= 10_000 ? 40 : reviews >= 1_000 ? lerp(reviews, 1_000, 10_000, 30, 40)
+        : reviews >= 100 ? lerp(reviews, 100, 1_000, 15, 30)
+        : lerp(reviews, 0, 100, 0, 15);
+      return Math.min(rScore + rvScore, 100);
+    }
+    default:
+      return 50; // Unknown channel — neutral score
+  }
+}
+
 /**
  * Phase 2: Collect Channel Data
  *
@@ -342,17 +434,59 @@ Deno.serve(async (req) => {
         // Get verified Naver Blog handle from Phase 1 for official blog URL
         const nbVerified = verified.naverBlog as Record<string, unknown> | null;
         const officialBlogHandle = nbVerified?.handle ? String(nbVerified.handle) : null;
+        const officialBlogUrl = officialBlogHandle ? `https://blog.naver.com/${officialBlogHandle}` : null;
 
+        // ─── 5a. Naver Search: 3rd-party blog mentions ───
         const query = encodeURIComponent(`${clinicName} 후기`);
         const res = await fetchWithRetry(`https://openapi.naver.com/v1/search/blog.json?query=${query}&display=10&sort=sim`, { headers: naverHeaders }, { label: "naver-blog" });
         if (!res.ok) throw new Error(`Naver Blog API returned ${res.status}`);
         const data = await res.json();
+
+        // ─── 5b. Firecrawl: Official blog recent posts ───
+        // Registry always provides the official blog URL — scrape it for real content metrics.
+        let officialBlogContent: Record<string, unknown> | null = null;
+        if (officialBlogUrl) {
+          const FIRECRAWL_KEY = Deno.env.get("FIRECRAWL_API_KEY");
+          if (FIRECRAWL_KEY) {
+            try {
+              const blogScrape = await fetchWithRetry(`https://api.firecrawl.dev/v1/scrape`, {
+                method: "POST",
+                headers: { "Content-Type": "application/json", Authorization: `Bearer ${FIRECRAWL_KEY}` },
+                body: JSON.stringify({
+                  url: officialBlogUrl,
+                  formats: ["json"],
+                  jsonOptions: {
+                    prompt: "Extract the blog's recent posts: title, date, excerpt. Also total post count visible on the page, and the blog category/tag list.",
+                    schema: {
+                      type: "object",
+                      properties: {
+                        totalPosts: { type: "number" },
+                        recentPosts: { type: "array", items: { type: "object", properties: { title: { type: "string" }, date: { type: "string" }, excerpt: { type: "string" } } } },
+                        categories: { type: "array", items: { type: "string" } },
+                      },
+                    },
+                  },
+                  waitFor: 3000,
+                }),
+              }, { label: "firecrawl-naver-blog", timeoutMs: 45000 });
+              if (blogScrape.ok) {
+                const blogData = await blogScrape.json();
+                officialBlogContent = blogData.data?.json || null;
+                console.log(`[naverBlog] Official blog scraped: ${officialBlogContent?.totalPosts ?? 0} posts`);
+              }
+            } catch (e) {
+              console.warn(`[naverBlog] Official blog Firecrawl failed (non-critical):`, e);
+            }
+          }
+        }
+
         channelData.naverBlog = {
           totalResults: data.total || 0, searchQuery: `${clinicName} 후기`,
-          // Official blog URL from Phase 1 verified handle
+          officialBlogUrl,
-          officialBlogUrl: officialBlogHandle ? `https://blog.naver.com/${officialBlogHandle}` : null,
+          officialBlogHandle,
-          officialBlogHandle: officialBlogHandle,
+          // Official blog content (from Firecrawl — actual blog data)
-          // Blog mentions (third-party posts, NOT the official blog)
+          officialContent: officialBlogContent,
+          // Blog mentions (third-party posts via Naver Search)
           posts: (data.items || []).slice(0, 10).map((item: Record<string, string>) => ({
             title: (item.title || "").replace(/<[^>]*>/g, ""),
             description: (item.description || "").replace(/<[^>]*>/g, ""),
@@ -558,6 +692,7 @@ Deno.serve(async (req) => {
           snapshotInserts.push({
             clinic_id: clinicId, run_id: runId, channel: 'instagram',
             handle: igData.username, followers: igData.followers, posts: igData.posts,
+            health_score: computeHealthScore('instagram', igData),
             details: igData,
           });
         }
@@ -568,6 +703,7 @@ Deno.serve(async (req) => {
             clinic_id: clinicId, run_id: runId, channel: 'youtube',
             handle: ytData.handle || ytData.channelName, followers: ytData.subscribers,
             posts: ytData.totalVideos, total_views: ytData.totalViews,
+            health_score: computeHealthScore('youtube', ytData),
             details: ytData,
           });
         }
@@ -577,6 +713,7 @@ Deno.serve(async (req) => {
           snapshotInserts.push({
             clinic_id: clinicId, run_id: runId, channel: 'facebook',
             handle: fbData.pageName, followers: fbData.followers,
+            health_score: computeHealthScore('facebook', fbData),
             details: fbData,
           });
         }
@@ -586,7 +723,9 @@ Deno.serve(async (req) => {
           snapshotInserts.push({
             clinic_id: clinicId, run_id: runId, channel: 'gangnamUnni',
             handle: guData.name, rating: guData.rating, rating_scale: 10,
-            reviews: guData.totalReviews, details: guData,
+            reviews: guData.totalReviews,
+            health_score: computeHealthScore('gangnamUnni', guData),
+            details: guData,
           });
         }
 
@@ -595,7 +734,9 @@ Deno.serve(async (req) => {
           snapshotInserts.push({
             clinic_id: clinicId, run_id: runId, channel: 'googleMaps',
             handle: gmData.name, rating: gmData.rating, rating_scale: 5,
-            reviews: gmData.reviewCount, details: gmData,
+            reviews: gmData.reviewCount,
+            health_score: computeHealthScore('googleMaps', gmData),
+            details: gmData,
           });
         }
 
@@ -603,7 +744,9 @@ Deno.serve(async (req) => {
         if (nbData) {
           snapshotInserts.push({
             clinic_id: clinicId, run_id: runId, channel: 'naverBlog',
-            handle: nbData.officialBlogHandle, details: nbData,
+            handle: nbData.officialBlogHandle,
+            health_score: computeHealthScore('naverBlog', nbData),
+            details: nbData,
           });
         }