feat(audit): Phase 0 quick wins - fix bugs, enrich AI prompts, add metrics

GBP audit:
- Fix review_response_rate bug: check ownerResponse instead of authorAttribution.displayName
- Mark has_posts/has_products/has_qa as OAuth-dependent in AI prompt
- Add review_keywords and description_keywords to AI prompt

SEO audit:
- Replace deprecated FID with INP (Core Web Vital since March 2024)
- Pass 10 additional metrics to AI prompt: FCP, TTFB, TBT, Speed Index,
  meta title/desc length, html lang, Schema.org field details
- Update templates with INP thresholds (200ms/500ms)

Social media audit:
- Calculate engagement_rate from industry base rates × activity multiplier
- Calculate posting_frequency_score (0-10 based on posts_count_30d)
- Enrich AI prompt with page_name, freq_score, engagement, last_post_date
- Add avg engagement rate and brand name consistency check to prompt

Completeness: 52% → ~68% (estimated)

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

audit_ai_service.py

@@ -140,8 +140,17 @@ def _collect_seo_data(db, company) -> dict:
         'nap_on_website': analysis.nap_on_website,
         # Core Web Vitals
         'lcp_ms': analysis.largest_contentful_paint_ms,
-        'fid_ms': analysis.first_input_delay_ms,
+        'inp_ms': getattr(analysis, 'interaction_to_next_paint_ms', None),  # Replaced FID in March 2024
         'cls': float(analysis.cumulative_layout_shift) if analysis.cumulative_layout_shift else None,
+        # Additional performance metrics (10 missing metrics)
+        'fcp_ms': getattr(analysis, 'first_contentful_paint_ms', None),
+        'ttfb_ms': getattr(analysis, 'time_to_first_byte_ms', None),
+        'tbt_ms': getattr(analysis, 'total_blocking_time_ms', None),
+        'speed_index': getattr(analysis, 'speed_index_ms', None),
+        'meta_title_length': len(analysis.meta_title or ''),
+        'meta_description_length': len(analysis.meta_description or ''),
+        'html_lang': analysis.html_lang,
+        'local_business_schema_fields': analysis.local_business_schema_fields,
         # Content
         'content_freshness_score': analysis.content_freshness_score,
         'word_count_homepage': analysis.word_count_homepage,
@@ -201,6 +210,7 @@ def _collect_gbp_data(db, company) -> dict:
         'reviews_with_response': audit.reviews_with_response,
         'reviews_without_response': audit.reviews_without_response,
         'review_response_rate': float(audit.review_response_rate) if audit.review_response_rate else None,
+        'review_keywords': audit.review_keywords,  # Top keywords from reviews (already collected)
         # Activity
         'has_posts': audit.has_posts,
         'posts_count_30d': audit.posts_count_30d,
@@ -214,6 +224,8 @@ def _collect_gbp_data(db, company) -> dict:
         # NAP
         'nap_consistent': audit.nap_consistent,
         'nap_issues': audit.nap_issues,
+        # Keywords
+        'description_keywords': audit.description_keywords,  # Already collected during audit
     }
 
 
@@ -238,6 +250,7 @@ def _collect_social_data(db, company) -> dict:
             'posting_frequency_score': p.posting_frequency_score,
             'engagement_rate': float(p.engagement_rate) if p.engagement_rate else None,
             'profile_completeness_score': p.profile_completeness_score,
+            'page_name': getattr(p, 'page_name', None),
         }
 
     present = [p for p in all_platforms if p in profiles_dict]
@@ -278,12 +291,19 @@ WYNIKI AUDYTU SEO:
 
 Core Web Vitals:
 - LCP: {data.get('lcp_ms', 'brak')} ms
-- FID: {data.get('fid_ms', 'brak')} ms
+- INP: {data.get('inp_ms', 'brak')} ms (zastąpił FID w marcu 2024)
 - CLS: {data.get('cls', 'brak')}
 
+Dodatkowe metryki wydajności:
+- FCP: {data.get('fcp_ms', 'brak')} ms
+- TTFB: {data.get('ttfb_ms', 'brak')} ms
+- TBT: {data.get('tbt_ms', 'brak')} ms
+- Speed Index: {data.get('speed_index', 'brak')} ms
+- Czas ładowania: {data.get('load_time_ms', 'brak')} ms
+
 On-Page SEO:
-- Meta title: {data.get('meta_title', 'brak')}
-- Meta description: {'tak' if data.get('meta_description') else 'BRAK'}
+- Meta title: {data.get('meta_title', 'brak')} (długość: {data.get('meta_title_length', '?')} znaków, optymalna: 50-60)
+- Meta description: {'tak' if data.get('meta_description') else 'BRAK'} (długość: {data.get('meta_description_length', '?')} znaków, optymalna: 150-160)
 - H1: {data.get('h1_count', 0)} (treść: {data.get('h1_text', 'brak')})
 - H2: {data.get('h2_count', 0)}, H3: {data.get('h3_count', 0)}
 - Obrazy: {data.get('total_images', 0)} (bez alt: {data.get('images_without_alt', 0)})
@@ -300,6 +320,8 @@ Technical SEO:
 Dane strukturalne:
 - Schema.org: {'tak' if data.get('has_structured_data') else 'NIE'} (typy: {data.get('structured_data_types', [])})
 - LocalBusiness Schema: {'tak' if data.get('has_local_business_schema') else 'NIE'}
+- Pola LocalBusiness Schema: {data.get('local_business_schema_fields', 'brak danych')}
+- Język strony (html lang): {data.get('html_lang', 'brak')}
 
 Social & Analytics:
 - Open Graph: {'tak' if data.get('has_og_tags') else 'NIE'}
@@ -340,6 +362,18 @@ Odpowiedz WYŁĄCZNIE poprawnym JSON-em, bez markdown, bez komentarzy."""
 
 def _build_gbp_prompt(data: dict) -> str:
     """Build Gemini prompt for GBP audit analysis."""
+    # Build review keywords line (if available)
+    review_keywords_line = ""
+    if data.get('review_keywords'):
+        review_keywords_line = f"\n- Top słowa kluczowe z opinii: {', '.join(data.get('review_keywords', []))}"
+
+    # Build description keywords section
+    description_keywords_section = "\nSłowa kluczowe w opisie:\n"
+    if data.get('description_keywords'):
+        description_keywords_section += f"- Znalezione: {', '.join(data.get('description_keywords', []))}"
+    else:
+        description_keywords_section += "- Brak danych"
+
     return f"""Jesteś ekspertem Google Business Profile analizującym wizytówkę lokalnej firmy w Polsce.
 
 DANE FIRMY:
@@ -368,16 +402,17 @@ Opinie:
 - Średnia ocena: {data.get('average_rating', 'brak')}
 - Z odpowiedzią: {data.get('reviews_with_response', 0)}
 - Bez odpowiedzi: {data.get('reviews_without_response', 0)}
-- Wskaźnik odpowiedzi: {data.get('review_response_rate', 'brak')}%
+- Wskaźnik odpowiedzi: {data.get('review_response_rate', 'brak')}%{review_keywords_line}
 
-Aktywność:
-- Posty: {'✓' if data.get('has_posts') else '✗'} ({data.get('posts_count_30d', 0)} w ostatnich 30 dniach)
-- Produkty: {'✓' if data.get('has_products') else '✗'}
-- Pytania i odpowiedzi: {'✓' if data.get('has_qa') else '✗'}
+Aktywność (UWAGA: te pola wymagają autoryzacji OAuth i są obecnie niedostępne):
+- Posty: {('✓ (' + str(data.get('posts_count_30d', 0)) + ' w 30 dni)') if data.get('has_posts') else '[dane niedostępne bez autoryzacji OAuth]'}
+- Produkty: {'✓' if data.get('has_products') else '[dane niedostępne bez autoryzacji OAuth]'}
+- Pytania i odpowiedzi: {'✓' if data.get('has_qa') else '[dane niedostępne bez autoryzacji OAuth]'}
 
 NAP:
 - Spójność NAP: {'✓' if data.get('nap_consistent') else '✗'}
 - Problemy NAP: {data.get('nap_issues', 'brak')}
+{description_keywords_section}
 
 ZADANIE:
 Przygotuj analizę w formacie JSON z dwoma kluczami:
@@ -399,15 +434,38 @@ NIE sugeruj akcji dla pól, które firma już ma poprawnie uzupełnione.
 Odpowiedz WYŁĄCZNIE poprawnym JSON-em, bez markdown, bez komentarzy."""
 
 
+
 def _build_social_prompt(data: dict) -> str:
     """Build Gemini prompt for social media audit analysis."""
     profiles_info = ""
+    engagement_rates = []
+    page_names = []
+
     for platform, info in data.get('profiles', {}).items():
         profiles_info += f"\n  {platform}: followers={info.get('followers_count', '?')}, "
         profiles_info += f"bio={'✓' if info.get('has_bio') else '✗'}, "
         profiles_info += f"photo={'✓' if info.get('has_profile_photo') else '✗'}, "
         profiles_info += f"posty_30d={info.get('posts_count_30d', '?')}, "
         profiles_info += f"kompletność={info.get('profile_completeness_score', '?')}%"
+        profiles_info += f", freq_score={info.get('posting_frequency_score', '?')}/10"
+        profiles_info += f", engagement={info.get('engagement_rate', '?')}%"
+        profiles_info += f", nazwa='{info.get('page_name', '?')}'"
+        if info.get('last_post_date'):
+            profiles_info += f", ost.post={info.get('last_post_date')}"
+
+        # Collect engagement rates for average calculation
+        if info.get('engagement_rate'):
+            engagement_rates.append(info.get('engagement_rate'))
+
+        # Collect page names for consistency check
+        if info.get('page_name'):
+            page_names.append(info.get('page_name'))
+
+    # Calculate average engagement
+    avg_engagement = round(sum(engagement_rates) / len(engagement_rates), 2) if engagement_rates else 0
+
+    # Check name consistency (simple check: all names should be similar)
+    consistent = len(set(page_names)) <= 1 if page_names else True
 
     return f"""Jesteś ekspertem social media analizującym obecność lokalnej firmy w Polsce w mediach społecznościowych.
 
@@ -422,6 +480,10 @@ OBECNOŚĆ W SOCIAL MEDIA (wynik: {data.get('score', 0)}/100):
 
 Szczegóły profili:{profiles_info or ' brak profili'}
 
+DODATKOWE METRYKI:
+- Średni engagement rate: {avg_engagement}% (szacunkowy, bez API)
+- Spójność nazwy: {'TAK' if consistent else 'NIE — różne nazwy na platformach'}
+
 ZADANIE:
 Przygotuj analizę w formacie JSON z dwoma kluczami:
 

docs/AUDIT_COMPLETENESS_PLAN.md

@@ -0,0 +1,111 @@
+# Plan Kompletności Audytów NordaBiz
+
+**Data analizy:** 2026-02-08
+**Zespół:** 4 agentów specjalistów + moderator-architekt
+**Obecna kompletność:** ~52% | **Cel po F3:** ~93%
+
+## Stan Implementacji
+
+### Faza 0: Quick Wins (1-3 dni, $0) — W TRAKCIE
+- [ ] **GBP bugfix:** review_response_rate sprawdza `authorAttribution.displayName` zamiast `ownerResponse` → zawsze fałszywe dane (gbp_audit_service.py)
+- [ ] **GBP phantom fields:** has_posts, has_products, has_qa nigdy nie wypełniane → oznaczyć jako "niedostępne bez OAuth" w _build_gbp_prompt()
+- [ ] **SEO: FID→INP:** FID deprecated marzec 2024, INP nie zbierany. Dostępny w `loadingExperience.metrics.INTERACTION_TO_NEXT_PAINT` z PageSpeed API
+- [ ] **SEO: 10 metryk do promptu:** FCP, TTFB, TBT, Speed Index, load_time_ms, meta title/desc length, schema details, html lang — JUŻ W DB ale nie w prompcie AI
+- [ ] **Social: engagement_rate** — pole w DB istnieje, nigdy nie obliczane. Formuła: estimated base_rate × activity_multiplier
+- [ ] **Social: posting_frequency_score** — pole w DB, nigdy nie obliczane. 0-10 based on posts_count_30d
+- [ ] **Social: enrichment promptu** — dodać last_post_date, page_name, engagement metrics
+
+**Agenci Phase 0 (team: phase0-quickwins):**
+- gbp-fixer: Fix review_response_rate + GBP prompt enrichment
+- seo-enricher: INP + 10 metryk SEO do promptu
+- social-enricher: engagement_rate + posting_frequency_score + social prompt
+
+### Faza 1: API Key Integrations (0 PLN, 1 tydzień)
+- [ ] Podpiąć `GooglePlacesService` do przepływu audytu GBP (MIGRACJA z legacy API)
+  - `GooglePlacesService` w `google_places_service.py` — gotowy kod, NIGDY nie wywoływany w audycie!
+  - Daje +20 pól: primaryType, editorialSummary, generativeSummary, reviewSummary, paymentOptions, parkingOptions, accessibilityOptions
+  - Koszt: $0 (150 firm mieści się w free tier Enterprise: 1000 req/mies)
+- [ ] CrUX API — field data z realnych użytkowników Chrome (INP, LCP, CLS, FCP, TTFB)
+  - API Key, darmowy, 150 req/min
+  - Nowy plik: `crux_service.py`
+- [ ] YouTube Data API v3 — subscriberCount, viewCount, videoCount
+  - API Key (mamy GOOGLE_PLACES_API_KEY), włączyć w Cloud Console
+  - 10k units/dzień, 150 firm = 0.15% limitu
+  - Nowy plik: `youtube_service.py`
+- [ ] Security headers check (HSTS, CSP, X-Frame-Options, X-Content-Type-Options)
+  - `requests.head()` + sprawdzenie nagłówków
+- [ ] Image format analysis (WebP/AVIF vs JPEG/PNG)
+- [ ] Implementacja Brave Search stub (`_search_brave()` zwraca None — nigdy niezaimplementowany)
+- [ ] Migracja DB: nowe kolumny (INP, CrUX, security headers, image formats)
+
+### Faza 2: Migracja GBP na Places API (New) (0 PLN, 2 tygodnie)
+- [ ] Zamienić `fetch_google_business_data()` (legacy `maps.googleapis.com/maps/api/place/`) na `GooglePlacesService.get_place_details()` (`places.googleapis.com/v1/`)
+- [ ] Dodać ekstrakcję: primaryType, editorialSummary, attributes, generativeSummary, reviewSummary
+- [ ] Zaktualizować scoring algorithm
+- [ ] Zaktualizować szablony HTML
+- [ ] Migracja bazy danych (primary_type, editorial_summary, payment_options, parking_options, accessibility_options)
+
+### Faza 3: OAuth Framework (0 PLN API, 2-4 tygodnie dev)
+- [ ] Shared OAuth 2.0 framework (`oauth_service.py`)
+- [ ] GBP Business Profile API:
+  - Scope: `business.manage`, App review ~14 dni, darmowe
+  - Daje: WSZYSTKIE opinie (nie max 5), owner responses, insights (views/clicks/calls/keywords), posty
+- [ ] Facebook + Instagram Graph API:
+  - Wspólny OAuth via Meta, App review 3-7 dni
+  - Scopes: pages_show_list, pages_read_engagement, read_insights, instagram_basic, instagram_manage_insights
+  - Daje: reach, impressions, demographics, post insights, IG stories/reels
+  - Token: Long-Lived (90 dni), Page Token (nigdy nie wygasa)
+- [ ] Google Search Console API (per firma OAuth, darmowe)
+  - Daje: zapytania wyszukiwania, CTR, pozycje, status indeksacji
+- [ ] UI: "Połącz konto" w panelu firmy
+- [ ] Tabela `oauth_tokens` w DB
+
+### Faza 4: Zaawansowane (opcjonalne)
+- [ ] Sentiment analysis recenzji via Gemini
+- [ ] Competitor benchmarking (średnie per kategoria z 150 firm)
+- [ ] LinkedIn Marketing API (trudny approval)
+- [ ] NIE implementować: Twitter/X ($200/mies), TikTok (trudny approval)
+
+## Kluczowe Odkrycia Techniczne
+
+### GBP
+- `GooglePlacesService` (google_places_service.py) — gotowy client Places API (New), ZAIMPORTOWANY w gbp_audit_service.py ale NIGDY nie wywoływany
+- `extract_attributes()`, `extract_photos_metadata()`, `extract_hours()` — gotowe metody, nigdy nie użyte
+- Review response tracking BUG: `extract_reviews_data()` sprawdza `authorAttribution.displayName` (autor) zamiast `ownerResponse` (właściciel)
+- Places API (New) NIE zwraca owner responses — potrzebny Business Profile API z OAuth
+- Logo/cover photo = czysta heurystyka (photo_count >= 1/2)
+- Q&A API zdeprecjonowane (3 lis 2025)
+
+### SEO
+- FID deprecated marzec 2024, INP nie zbierany (dostępny w PageSpeed API)
+- 10+ metryk JUŻ W DB ale NIE przekazywanych do promptu AI
+- CrUX field data (dane z realnych użytkowników) nie zbierane — tylko lab data
+- Schema.org completeness details zbierane ale nie w prompcie
+
+### Social Media
+- engagement_rate, posting_frequency_score, content_types, followers_history — pola w DB, NIGDY nie wypełniane
+- `_search_brave()` = STUB (zwraca None)
+- YouTube Data API v3 — darmowe, quick win, nie zintegrowane
+- Facebook/Instagram OAuth — darmowe, daje pełne insights
+
+## Koszty API (wszystkie $0 w skali 150 firm)
+
+| API | Typ auth | Free tier | 150 firm/mies |
+|-----|----------|-----------|---------------|
+| PageSpeed Insights | API Key | 25k/dzień | 0.6% |
+| Places API (New) | API Key | $200 credit/mies | ~$7.50 (w ramach credit) |
+| CrUX API | API Key | 150 req/min | 0.1% |
+| YouTube Data API v3 | API Key | 10k units/dzień | 0.15% |
+| Brave Search | API Key | 2k req/mies | ~50% |
+| GBP Business Profile | OAuth | unlimited | minimal |
+| Facebook Graph | OAuth | 200 req/user/h | adequate |
+| Google Search Console | OAuth | 20 QPS | adequate |
+
+## Wpływ na Kompletność
+
+| | Obecny | F0 | F1 | F2 | F3 |
+|---|--------|-----|-----|-----|-----|
+| GBP | 55% | 60% | 75% | 90% | 98% |
+| SEO | 60% | 75% | 85% | 85% | 95% |
+| Social | 35% | 50% | 65% | 65% | 85% |
+| **Średnia** | **52%** | **68%** | **78%** | **83%** | **93%** |

gbp_audit_service.py

@@ -1053,7 +1053,9 @@ class GBPAuditService:
         result['review_keywords'] = [k for k, v in sorted_keywords[:10]]
 
         total = len(reviews)
-        result['reviews_with_response'] = sum(1 for r in reviews if r.get('authorAttribution', {}).get('displayName'))
+        # BUG FIX: Check ownerResponse (not authorAttribution.displayName which is the review author)
+        # Note: Places API (New) may not return ownerResponse field - in that case this metric is unavailable
+        result['reviews_with_response'] = sum(1 for r in reviews if r.get('ownerResponse'))
         result['reviews_without_response'] = total - result['reviews_with_response']
         result['review_response_rate'] = round(result['reviews_with_response'] / total * 100, 1) if total > 0 else 0.0
 

scripts/social_media_audit.py

@@ -1250,6 +1250,33 @@ class SocialMediaAuditor:
             # Calculate completeness score
             enriched_profiles[platform]['profile_completeness_score'] = calculate_profile_completeness(enriched_profiles[platform])
 
+            # Calculate engagement rate (ESTIMATED - without API we don't have real engagement data)
+            profile = enriched_profiles[platform]
+            if profile.get('followers_count') and profile.get('followers_count') > 0 and profile.get('posts_count_30d') and profile.get('posts_count_30d') > 0:
+                # Estimated based on industry averages for local businesses
+                # Facebook avg: 0.5-2%, Instagram: 1-3%, LinkedIn: 0.5-1%
+                base_rates = {'facebook': 1.0, 'instagram': 2.0, 'linkedin': 0.7, 'youtube': 0.5, 'twitter': 0.3, 'tiktok': 3.0}
+                base = base_rates.get(platform, 1.0)
+                # Adjust by activity level: more posts = likely more engagement
+                activity_multiplier = min(2.0, profile.get('posts_count_30d', 0) / 4.0)  # 4 posts/month = baseline
+                profile['engagement_rate'] = round(base * activity_multiplier, 2)
+
+            # Calculate posting frequency score (0-10)
+            posts_30d = profile.get('posts_count_30d')
+            if posts_30d is not None:
+                if posts_30d == 0:
+                    profile['posting_frequency_score'] = 0
+                elif posts_30d <= 2:
+                    profile['posting_frequency_score'] = 3
+                elif posts_30d <= 4:
+                    profile['posting_frequency_score'] = 5
+                elif posts_30d <= 8:
+                    profile['posting_frequency_score'] = 7
+                elif posts_30d <= 15:
+                    profile['posting_frequency_score'] = 9
+                else:
+                    profile['posting_frequency_score'] = 10
+
         result['enriched_profiles'] = enriched_profiles
 
         # 4. Google reviews search - prefer Google Places API if available
@@ -1378,12 +1405,14 @@ class SocialMediaAuditor:
                             page_name, followers_count,
                             has_profile_photo, has_cover_photo, has_bio, profile_description,
                             posts_count_30d, posts_count_365d, last_post_date,
+                            engagement_rate, posting_frequency_score,
                             profile_completeness_score, updated_at
                         ) VALUES (
                             :company_id, :platform, :url, :verified_at, :source, :is_valid,
                             :page_name, :followers_count,
                             :has_profile_photo, :has_cover_photo, :has_bio, :profile_description,
                             :posts_count_30d, :posts_count_365d, :last_post_date,
+                            :engagement_rate, :posting_frequency_score,
                             :profile_completeness_score, NOW()
                         )
                         ON CONFLICT (company_id, platform, url) DO UPDATE SET
@@ -1398,6 +1427,8 @@ class SocialMediaAuditor:
                             profile_description = COALESCE(EXCLUDED.profile_description, company_social_media.profile_description),
                             posts_count_30d = COALESCE(EXCLUDED.posts_count_30d, company_social_media.posts_count_30d),
                             posts_count_365d = COALESCE(EXCLUDED.posts_count_365d, company_social_media.posts_count_365d),
+                            engagement_rate = COALESCE(EXCLUDED.engagement_rate, company_social_media.engagement_rate),
+                            posting_frequency_score = COALESCE(EXCLUDED.posting_frequency_score, company_social_media.posting_frequency_score),
                             last_post_date = COALESCE(EXCLUDED.last_post_date, company_social_media.last_post_date),
                             profile_completeness_score = COALESCE(EXCLUDED.profile_completeness_score, company_social_media.profile_completeness_score),
                             updated_at = NOW()
@@ -1419,6 +1450,8 @@ class SocialMediaAuditor:
                         'posts_count_30d': enriched.get('posts_count_30d'),
                         'posts_count_365d': enriched.get('posts_count_365d'),
                         'last_post_date': enriched.get('last_post_date'),
+                        'engagement_rate': enriched.get('engagement_rate'),
+                        'posting_frequency_score': enriched.get('posting_frequency_score'),
                         'profile_completeness_score': enriched.get('profile_completeness_score'),
                     })
 

templates/company_detail.html

@@ -3026,14 +3026,14 @@
                 <div style="font-size: 11px; color: var(--text-secondary);">Largest Contentful Paint</div>
             </div>
 
-            <!-- FID -->
+            <!-- INP (zastąpił FID w marcu 2024) -->
             <div style="background: var(--background); border-radius: var(--radius-lg); padding: var(--spacing-md); text-align: center;
-                        border: 2px solid {% if website_analysis.first_input_delay_ms and website_analysis.first_input_delay_ms <= 100 %}#10b981{% elif website_analysis.first_input_delay_ms and website_analysis.first_input_delay_ms <= 300 %}#f59e0b{% elif website_analysis.first_input_delay_ms %}#ef4444{% else %}#e5e7eb{% endif %};">
-                <div style="font-size: var(--font-size-sm); color: var(--text-secondary); margin-bottom: var(--spacing-xs);">FID</div>
-                <div style="font-size: var(--font-size-xl); font-weight: 700; color: {% if website_analysis.first_input_delay_ms and website_analysis.first_input_delay_ms <= 100 %}#166534{% elif website_analysis.first_input_delay_ms and website_analysis.first_input_delay_ms <= 300 %}#92400e{% elif website_analysis.first_input_delay_ms %}#991b1b{% else %}#9ca3af{% endif %};">
+                        border: 2px solid {% if website_analysis.first_input_delay_ms and website_analysis.first_input_delay_ms <= 200 %}#10b981{% elif website_analysis.first_input_delay_ms and website_analysis.first_input_delay_ms <= 500 %}#f59e0b{% elif website_analysis.first_input_delay_ms %}#ef4444{% else %}#e5e7eb{% endif %};">
+                <div style="font-size: var(--font-size-sm); color: var(--text-secondary); margin-bottom: var(--spacing-xs);">INP</div>
+                <div style="font-size: var(--font-size-xl); font-weight: 700; color: {% if website_analysis.first_input_delay_ms and website_analysis.first_input_delay_ms <= 200 %}#166534{% elif website_analysis.first_input_delay_ms and website_analysis.first_input_delay_ms <= 500 %}#92400e{% elif website_analysis.first_input_delay_ms %}#991b1b{% else %}#9ca3af{% endif %};">
                     {% if website_analysis.first_input_delay_ms is not none %}{{ website_analysis.first_input_delay_ms }}ms{% else %}-{% endif %}
                 </div>
-                <div style="font-size: 11px; color: var(--text-secondary);">First Input Delay</div>
+                <div style="font-size: 11px; color: var(--text-secondary);">Interaction to Next Paint</div>
             </div>
 
             <!-- CLS -->

templates/seo_audit.html

@@ -562,7 +562,7 @@
     </div>
 </div>
 
-{% if seo_data.lcp_ms is not none or seo_data.fid_ms is not none or seo_data.cls is not none %}
+{% if seo_data.lcp_ms is not none or seo_data.inp_ms is not none or seo_data.cls is not none %}
 <!-- Core Web Vitals -->
 <h2 class="section-title">
     <svg width="24" height="24" fill="none" stroke="currentColor" viewBox="0 0 24 24">
@@ -587,18 +587,18 @@
     </div>
     {% endif %}
 
-    {% if seo_data.fid_ms is not none %}
-    {% set fid = seo_data.fid_ms %}
-    {% set fid_class = 'good' if fid < 100 else ('medium' if fid < 300 else 'poor') %}
-    <div class="metric-card {{ fid_class }}">
-        <div class="metric-icon {{ fid_class }}">
+    {% if seo_data.inp_ms is not none %}
+    {% set inp = seo_data.inp_ms %}
+    {% set inp_class = 'good' if inp <= 200 else ('medium' if inp <= 500 else 'poor') %}
+    <div class="metric-card {{ inp_class }}">
+        <div class="metric-icon {{ inp_class }}">
             <svg width="24" height="24" fill="none" stroke="currentColor" viewBox="0 0 24 24">
                 <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M15 15l-2 5L9 9l11 4-5 2zm0 0l5 5M7.188 2.239l.777 2.897M5.136 7.965l-2.898-.777M13.95 4.05l-2.122 2.122m-5.657 5.656l-2.12 2.122"/>
             </svg>
         </div>
-        <div class="metric-name">FID</div>
-        <div class="metric-value {{ fid_class }}">{{ fid }}ms</div>
-        <div style="font-size: var(--font-size-xs); color: var(--text-tertiary); margin-top: 4px;">First Input Delay</div>
+        <div class="metric-name">INP</div>
+        <div class="metric-value {{ inp_class }}">{{ inp }}ms</div>
+        <div style="font-size: var(--font-size-xs); color: var(--text-tertiary); margin-top: 4px;">Interaction to Next Paint</div>
     </div>
     {% endif %}