diff --git a/research_quick_wins.py b/research_quick_wins.py
index 8d9a0b4..6962cce 100644
--- a/research_quick_wins.py
+++ b/research_quick_wins.py
@@ -15,7 +15,7 @@
 load_dotenv()
 
 # Add data_sources to path
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'data_sources'))
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "data_sources"))
 
 from modules.google_search_console import GoogleSearchConsole
 from modules.dataforseo import DataForSEO
@@ -23,6 +23,25 @@
 from modules.opportunity_scorer import OpportunityScorer, OpportunityType
 from modules.search_intent_analyzer import SearchIntentAnalyzer
 
+
+def get_first_ranking(rankings):
+    if isinstance(rankings, list) and rankings:
+        first = rankings[0]
+        if isinstance(first, dict):
+            return first
+    return None
+
+
+def get_serp_features(dfs, keyword):
+    try:
+        serp_data = dfs.get_serp_data(keyword, limit=10)
+        if serp_data and "features" in serp_data:
+            return serp_data.get("features", [])
+    except Exception:
+        return []
+    return []
+
+
 def main():
     print("=" * 80)
     print("QUICK WIN OPPORTUNITIES RESEARCH")
@@ -62,10 +81,7 @@ def main():
     # Get quick wins from GSC
     print("\n2. Fetching keywords ranking positions 11-20...")
     quick_wins = gsc.get_quick_wins(
-        days=30,
-        position_min=11,
-        position_max=20,
-        min_impressions=50
+        days=30, position_min=11, position_max=20, min_impressions=50
     )
 
     if not quick_wins:
@@ -81,11 +97,11 @@ def main():
     detailed_opportunities = []
 
     for i, kw in enumerate(quick_wins[:10], 1):
-        keyword = kw['keyword']
-        position = kw['position']
-        impressions = kw['impressions']
-        clicks = kw['clicks']
-        ctr = kw['ctr'] * 100
+        keyword = kw["keyword"]
+        position = kw["position"]
+        impressions = kw["impressions"]
+        clicks = kw["clicks"]
+        ctr = kw["ctr"] * 100
 
         print(f"\n#{i} KEYWORD: {keyword}")
         print("-" * 80)
@@ -93,7 +109,9 @@ def main():
         print(f"Impressions (30d): {impressions:,}")
         print(f"Clicks (30d): {clicks}")
         print(f"CTR: {ctr:.2f}%")
-        print(f"Commercial Intent: {kw['commercial_intent_category']} ({kw['commercial_intent']}/3.0)")
+        print(
+            f"Commercial Intent: {kw['commercial_intent_category']} ({kw['commercial_intent']}/3.0)"
+        )
         print(f"Opportunity Score: {kw['opportunity_score']:.2f}")
         print(f"Priority: {kw['priority'].upper()}")
 
@@ -104,15 +122,19 @@ def main():
             try:
                 print(f"\nVerifying with DataForSEO...")
                 rankings = dfs.get_rankings(
-                    domain=os.getenv('GSC_SITE_URL', 'yoursite.com').replace('https://', '').replace('http://', '').rstrip('/'),
-                    keywords=[keyword]
+                    domain=os.getenv("GSC_SITE_URL", "yoursite.com")
+                    .replace("https://", "")
+                    .replace("http://", "")
+                    .rstrip("/"),
+                    keywords=[keyword],
                 )
 
-                if rankings and rankings[0]['position']:
-                    dfs_position = rankings[0]['position']
-                    dfs_url = rankings[0]['url']
-                    search_volume = rankings[0]['search_volume']
-                    difficulty = rankings[0].get('difficulty')
+                ranking = get_first_ranking(rankings)
+                if ranking and ranking.get("position"):
+                    dfs_position = ranking["position"]
+                    dfs_url = ranking.get("url")
+                    search_volume = ranking.get("search_volume")
+                    difficulty = ranking.get("difficulty")
 
                     print(f"  DataForSEO Position: {dfs_position}")
                     print(f"  Ranking URL: {dfs_url}")
@@ -121,43 +143,40 @@ def main():
                     if difficulty:
                         print(f"  SEO Difficulty: {difficulty}/100")
 
-                    kw['dfs_position'] = dfs_position
-                    kw['ranking_url'] = dfs_url
-                    kw['search_volume'] = search_volume
-                    kw['difficulty'] = difficulty
+                    kw["dfs_position"] = dfs_position
+                    kw["ranking_url"] = dfs_url
+                    kw["search_volume"] = search_volume
+                    kw["difficulty"] = difficulty
                 else:
                     print(f"  Not found in top 100 (DataForSEO)")
 
-                # Get SERP features for intent analysis
-                try:
-                    serp_data = dfs.get_serp_data(keyword, limit=10)
-                    if serp_data and 'features' in serp_data:
-                        serp_features = serp_data.get('features', [])
-                        kw['serp_features'] = serp_features
-                        if serp_features:
-                            print(f"  SERP Features: {', '.join(serp_features[:3])}")
-                except:
-                    pass
+                serp_features = get_serp_features(dfs, keyword)
+                kw["serp_features"] = serp_features
+                if serp_features:
+                    print(f"  SERP Features: {', '.join(serp_features[:3])}")
             except Exception as e:
                 print(f"  DataForSEO error: {e}")
 
         # Get page performance from GA4 (if we have the URL)
-        if ga and kw.get('ranking_url'):
+        if ga and kw.get("ranking_url"):
             try:
                 # Extract path from URL
                 from urllib.parse import urlparse
-                path = urlparse(kw['ranking_url']).path
+
+                path = urlparse(kw["ranking_url"]).path
 
                 print(f"\nChecking GA4 performance for {path}...")
                 page_data = ga.get_page_performance(path, days=30)
 
-                if page_data and 'pageviews' in page_data:
+                if page_data and "pageviews" in page_data:
                     print(f"  Pageviews: {page_data['pageviews']:,}")
-                    print(f"  Avg. Engagement: {page_data.get('avg_engagement_time', 0):.0f}s")
+                    print(
+                        f"  Avg. Engagement: {page_data.get('avg_engagement_time', 0):.0f}s"
+                    )
                     print(f"  Bounce Rate: {page_data.get('bounce_rate', 0):.1%}")
 
-                    kw['pageviews'] = page_data['pageviews']
-                    kw['engagement'] = page_data.get('avg_engagement_time', 0)
+                    kw["pageviews"] = page_data["pageviews"]
+                    kw["engagement"] = page_data.get("avg_engagement_time", 0)
             except Exception as e:
                 print(f"  GA4 error: {e}")
 
@@ -166,48 +185,51 @@ def main():
         enhanced_score_result = scorer.calculate_score(
             keyword_data=kw,
             opportunity_type=OpportunityType.QUICK_WIN,
-            search_volume=kw.get('search_volume'),
-            difficulty=kw.get('difficulty'),
+            search_volume=kw.get("search_volume"),
+            difficulty=kw.get("difficulty"),
             serp_features=serp_features,
             cluster_value=50,  # Default - will be enhanced with topic clustering later
             trend_direction=None,  # Will be added with trend detection
-            trend_percent=None
+            trend_percent=None,
         )
 
-        kw['enhanced_score'] = enhanced_score_result['final_score']
-        kw['enhanced_priority'] = enhanced_score_result['priority']
-        kw['score_breakdown'] = enhanced_score_result['score_breakdown']
-        kw['primary_factor'] = enhanced_score_result['primary_factor']
+        kw["enhanced_score"] = enhanced_score_result["final_score"]
+        kw["enhanced_priority"] = enhanced_score_result["priority"]
+        kw["score_breakdown"] = enhanced_score_result["score_breakdown"]
+        kw["primary_factor"] = enhanced_score_result["primary_factor"]
 
         print(f"  Enhanced Score: {enhanced_score_result['final_score']}/100")
         print(f"  Priority: {enhanced_score_result['priority']}")
         print(f"  Key Factor: {enhanced_score_result['primary_factor']}")
 
         # Calculate traffic potential
-        if kw.get('position') and kw.get('impressions'):
+        if kw.get("position") and kw.get("impressions"):
             traffic_potential = scorer.calculate_potential_traffic(
-                current_position=kw['position'],
+                current_position=kw["position"],
                 target_position=7,  # Target middle of page 1
-                impressions=kw['impressions'],
-                current_clicks=kw['clicks']
+                impressions=kw["impressions"],
+                current_clicks=kw["clicks"],
+            )
+            kw["traffic_potential"] = traffic_potential
+            print(
+                f"  Potential: +{traffic_potential['additional_clicks']} clicks/month (+{traffic_potential['percent_increase']:.0f}%)"
             )
-            kw['traffic_potential'] = traffic_potential
-            print(f"  Potential: +{traffic_potential['additional_clicks']} clicks/month (+{traffic_potential['percent_increase']:.0f}%)")
 
         # Analyze search intent
         if serp_features:
             try:
                 intent_result = intent_analyzer.analyze(
-                    keyword=keyword,
-                    serp_features=serp_features
+                    keyword=keyword, serp_features=serp_features
                 )
                 # Handle SearchIntent enum or string
-                primary_intent = intent_result.get('primary_intent', 'unknown')
-                if hasattr(primary_intent, 'value'):
+                primary_intent = intent_result.get("primary_intent", "unknown")
+                if hasattr(primary_intent, "value"):
                     primary_intent = primary_intent.value
-                kw['search_intent'] = str(primary_intent)
-                kw['intent_confidence'] = float(intent_result.get('confidence', 0))
-                print(f"  Search Intent: {kw['search_intent']} (confidence: {kw['intent_confidence']:.0f}%)")
+                kw["search_intent"] = str(primary_intent)
+                kw["intent_confidence"] = float(intent_result.get("confidence", 0))
+                print(
+                    f"  Search Intent: {kw['search_intent']} (confidence: {kw['intent_confidence']:.0f}%)"
+                )
             except Exception as e:
                 print(f"  Intent analysis error: {e}")
 
@@ -215,13 +237,13 @@ def main():
         recommendation = generate_recommendation(kw)
         print(f"  {recommendation}")
 
-        kw['recommendation'] = recommendation
+        kw["recommendation"] = recommendation
         detailed_opportunities.append(kw)
 
     # Re-sort by enhanced score
     detailed_opportunities.sort(
-        key=lambda x: x.get('enhanced_score', x.get('opportunity_score', 0)),
-        reverse=True
+        key=lambda x: x.get("enhanced_score", x.get("opportunity_score", 0)),
+        reverse=True,
     )
 
     # Generate summary report
@@ -229,9 +251,11 @@ def main():
     print("SUMMARY REPORT")
     print("=" * 80)
 
-    total_impressions = sum(k['impressions'] for k in detailed_opportunities)
-    total_clicks = sum(k['clicks'] for k in detailed_opportunities)
-    avg_position = sum(k['position'] for k in detailed_opportunities) / len(detailed_opportunities)
+    total_impressions = sum(k["impressions"] for k in detailed_opportunities)
+    total_clicks = sum(k["clicks"] for k in detailed_opportunities)
+    avg_position = sum(k["position"] for k in detailed_opportunities) / len(
+        detailed_opportunities
+    )
 
     print(f"\nQuick Wins Identified: {len(detailed_opportunities)}")
     print(f"Total Impressions: {total_impressions:,}")
@@ -256,19 +280,25 @@ def main():
     for i, kw in enumerate(detailed_opportunities[:3], 1):
         print(f"\n{i}. {kw['keyword']}")
         print(f"   Position {kw['position']} → Target: 5-7")
-        print(f"   Current: {kw['clicks']} clicks → Potential: {int(kw['impressions'] * 0.055)} clicks")
-        if kw.get('ranking_url'):
+        print(
+            f"   Current: {kw['clicks']} clicks → Potential: {int(kw['impressions'] * 0.055)} clicks"
+        )
+        if kw.get("ranking_url"):
             print(f"   URL: {kw['ranking_url']}")
 
     # Write to markdown file
-    print(f"\n\n4. Writing report to research/quick-wins-{datetime.now().strftime('%Y-%m-%d')}.md...")
+    print(
+        f"\n\n4. Writing report to research/quick-wins-{datetime.now().strftime('%Y-%m-%d')}.md..."
+    )
     write_markdown_report(detailed_opportunities)
 
     print("\n" + "=" * 80)
     print("✅ RESEARCH COMPLETE")
     print("=" * 80)
     print(f"\nNext steps:")
-    print(f"1. Review detailed report: research/quick-wins-{datetime.now().strftime('%Y-%m-%d')}.md")
+    print(
+        f"1. Review detailed report: research/quick-wins-{datetime.now().strftime('%Y-%m-%d')}.md"
+    )
     print(f"2. Prioritize top 3-5 keywords to target first")
     print(f"3. Update content-priorities.md with findings")
     print(f"4. Start content optimization with top priority")
@@ -276,9 +306,9 @@ def main():
 
 def generate_recommendation(kw):
     """Generate action recommendation based on keyword data"""
-    position = kw['position']
-    impressions = kw['impressions']
-    clicks = kw['clicks']
+    position = kw["position"]
+    impressions = kw["impressions"]
+    clicks = kw["clicks"]
     ctr = (clicks / impressions * 100) if impressions > 0 else 0
 
     recommendations = []
@@ -305,20 +335,24 @@ def generate_recommendation(kw):
     if position > 15:
         recommendations.append("Consider: Add 500+ words, update examples, add visuals")
     else:
-        recommendations.append("Consider: Refresh intro, update stats, improve formatting")
+        recommendations.append(
+            "Consider: Refresh intro, update stats, improve formatting"
+        )
 
     return " | ".join(recommendations)
 
 
 def write_markdown_report(opportunities):
     """Write detailed markdown report"""
-    date_str = datetime.now().strftime('%Y-%m-%d')
+    date_str = datetime.now().strftime("%Y-%m-%d")
     filename = f"research/quick-wins-{date_str}.md"
 
-    with open(filename, 'w') as f:
+    with open(filename, "w") as f:
         f.write(f"# Quick Win Opportunities\n\n")
         f.write(f"**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M')}\n\n")
-        f.write(f"**Strategy:** Target keywords ranking positions 11-20 (page 2) to push to page 1\n\n")
+        f.write(
+            f"**Strategy:** Target keywords ranking positions 11-20 (page 2) to push to page 1\n\n"
+        )
         f.write(f"**Opportunities Found:** {len(opportunities)}\n\n")
         f.write("---\n\n")
 
@@ -330,40 +364,54 @@ def write_markdown_report(opportunities):
             f.write(f"- **Impressions (30d):** {kw['impressions']:,}\n")
             f.write(f"- **Clicks (30d):** {kw['clicks']}\n")
             f.write(f"- **CTR:** {kw['ctr'] * 100:.2f}%\n")
-            f.write(f"- **Commercial Intent:** {kw['commercial_intent_category']} ({kw['commercial_intent']}/3.0)\n")
-            if kw.get('search_intent'):
-                f.write(f"- **Search Intent:** {kw['search_intent']} ({kw.get('intent_confidence', 0):.0f}% confidence)\n")
+            f.write(
+                f"- **Commercial Intent:** {kw['commercial_intent_category']} ({kw['commercial_intent']}/3.0)\n"
+            )
+            if kw.get("search_intent"):
+                f.write(
+                    f"- **Search Intent:** {kw['search_intent']} ({kw.get('intent_confidence', 0):.0f}% confidence)\n"
+                )
             f.write(f"\n### Enhanced Opportunity Analysis\n\n")
-            f.write(f"- **Enhanced Score:** {kw.get('enhanced_score', kw['opportunity_score']):.2f}/100\n")
-            f.write(f"- **Priority:** {kw.get('enhanced_priority', kw['priority']).upper()}\n")
+            f.write(
+                f"- **Enhanced Score:** {kw.get('enhanced_score', kw['opportunity_score']):.2f}/100\n"
+            )
+            f.write(
+                f"- **Priority:** {kw.get('enhanced_priority', kw['priority']).upper()}\n"
+            )
             f.write(f"- **Key Factor:** {kw.get('primary_factor', 'volume')}\n\n")
 
-            if kw.get('score_breakdown'):
+            if kw.get("score_breakdown"):
                 f.write(f"**Score Breakdown:**\n")
-                breakdown = kw['score_breakdown']
+                breakdown = kw["score_breakdown"]
                 f.write(f"- Volume: {breakdown.get('volume_score', 0):.0f}/100\n")
                 f.write(f"- Position: {breakdown.get('position_score', 0):.0f}/100\n")
                 f.write(f"- Intent: {breakdown.get('intent_score', 0):.0f}/100\n")
-                f.write(f"- Competition: {breakdown.get('competition_score', 0):.0f}/100\n")
+                f.write(
+                    f"- Competition: {breakdown.get('competition_score', 0):.0f}/100\n"
+                )
                 f.write(f"- CTR Opportunity: {breakdown.get('ctr_score', 0):.0f}/100\n")
                 f.write(f"\n")
 
-            if kw.get('traffic_potential'):
-                tp = kw['traffic_potential']
+            if kw.get("traffic_potential"):
+                tp = kw["traffic_potential"]
                 f.write(f"### Traffic Potential\n\n")
-                f.write(f"- **Current:** {tp['current_clicks']} clicks/month at position {tp['current_position']:.1f}\n")
+                f.write(
+                    f"- **Current:** {tp['current_clicks']} clicks/month at position {tp['current_position']:.1f}\n"
+                )
                 f.write(f"- **Target:** Position {tp['target_position']} (page 1)\n")
                 f.write(f"- **Potential:** {tp['potential_clicks']} clicks/month\n")
-                f.write(f"- **Gain:** +{tp['additional_clicks']} clicks (+{tp['percent_increase']:.0f}%)\n\n")
+                f.write(
+                    f"- **Gain:** +{tp['additional_clicks']} clicks (+{tp['percent_increase']:.0f}%)\n\n"
+                )
 
-            if kw.get('ranking_url'):
+            if kw.get("ranking_url"):
                 f.write(f"### Ranking Page\n\n")
                 f.write(f"- **URL:** {kw['ranking_url']}\n")
-                if kw.get('dfs_position'):
+                if kw.get("dfs_position"):
                     f.write(f"- **DataForSEO Position:** {kw['dfs_position']}\n")
-                if kw.get('search_volume'):
+                if kw.get("search_volume"):
                     f.write(f"- **Search Volume:** {kw['search_volume']:,}/month\n")
-                if kw.get('pageviews'):
+                if kw.get("pageviews"):
                     f.write(f"- **Pageviews (30d):** {kw['pageviews']:,}\n")
                     f.write(f"- **Avg. Engagement:** {kw['engagement']:.0f}s\n")
                 f.write("\n")
@@ -384,8 +432,8 @@ def write_markdown_report(opportunities):
             f.write("---\n\n")
 
         # Summary section
-        total_impressions = sum(k['impressions'] for k in opportunities)
-        total_clicks = sum(k['clicks'] for k in opportunities)
+        total_impressions = sum(k["impressions"] for k in opportunities)
+        total_clicks = sum(k["clicks"] for k in opportunities)
         estimated_potential = int(total_impressions * 0.055)
 
         f.write(f"## Summary\n\n")
@@ -393,16 +441,22 @@ def write_markdown_report(opportunities):
         f.write(f"**Combined Metrics:**\n")
         f.write(f"- Total Impressions: {total_impressions:,}\n")
         f.write(f"- Total Current Clicks: {total_clicks}\n")
-        f.write(f"- Current CTR: {(total_clicks/total_impressions*100):.2f}%\n\n")
+        f.write(f"- Current CTR: {(total_clicks / total_impressions * 100):.2f}%\n\n")
         f.write(f"**Potential Impact:**\n")
         f.write(f"- Target CTR (page 1): 5.5%\n")
         f.write(f"- Potential Total Clicks: {estimated_potential}\n")
         f.write(f"- Additional Clicks: +{estimated_potential - total_clicks}\n\n")
 
         f.write(f"## Next Steps\n\n")
-        f.write(f"1. **Prioritize:** Start with top 3-5 keywords (highest opportunity score)\n")
-        f.write(f"2. **Research:** Analyze top-ranking competitor content for each keyword\n")
-        f.write(f"3. **Optimize:** Update existing content or create comprehensive new content\n")
+        f.write(
+            f"1. **Prioritize:** Start with top 3-5 keywords (highest opportunity score)\n"
+        )
+        f.write(
+            f"2. **Research:** Analyze top-ranking competitor content for each keyword\n"
+        )
+        f.write(
+            f"3. **Optimize:** Update existing content or create comprehensive new content\n"
+        )
         f.write(f"4. **Monitor:** Track position changes weekly\n")
         f.write(f"5. **Iterate:** Continue with next set of opportunities\n\n")
 
diff --git a/tests/test_research_quick_wins_helpers.py b/tests/test_research_quick_wins_helpers.py
new file mode 100644
index 0000000..424011c
--- /dev/null
+++ b/tests/test_research_quick_wins_helpers.py
@@ -0,0 +1,90 @@
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+
+
+MODULE_PATH = Path(__file__).resolve().parents[1] / "research_quick_wins.py"
+
+
+def load_research_quick_wins_module():
+    fake_dotenv = types.ModuleType("dotenv")
+    fake_dotenv.load_dotenv = lambda *args, **kwargs: None
+
+    modules_pkg = types.ModuleType("modules")
+
+    google_search_console = types.ModuleType("modules.google_search_console")
+    google_search_console.GoogleSearchConsole = object
+    dataforseo = types.ModuleType("modules.dataforseo")
+    dataforseo.DataForSEO = object
+    google_analytics = types.ModuleType("modules.google_analytics")
+    google_analytics.GoogleAnalytics = object
+    opportunity_scorer = types.ModuleType("modules.opportunity_scorer")
+    opportunity_scorer.OpportunityScorer = object
+    opportunity_scorer.OpportunityType = object
+    search_intent = types.ModuleType("modules.search_intent_analyzer")
+    search_intent.SearchIntentAnalyzer = object
+
+    injected = {
+        "dotenv": fake_dotenv,
+        "modules": modules_pkg,
+        "modules.google_search_console": google_search_console,
+        "modules.dataforseo": dataforseo,
+        "modules.google_analytics": google_analytics,
+        "modules.opportunity_scorer": opportunity_scorer,
+        "modules.search_intent_analyzer": search_intent,
+    }
+    previous = {name: sys.modules.get(name) for name in injected}
+    sys.modules.update(injected)
+    try:
+        spec = importlib.util.spec_from_file_location(
+            "research_quick_wins_under_test", MODULE_PATH
+        )
+        if spec is None or spec.loader is None:
+            raise RuntimeError(f"Unable to load {MODULE_PATH}")
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return module
+    finally:
+        for name, value in previous.items():
+            if value is None:
+                sys.modules.pop(name, None)
+            else:
+                sys.modules[name] = value
+
+
+class QuickWinsHelperTests(unittest.TestCase):
+    def test_get_first_ranking_returns_first_dict_only(self):
+        module = load_research_quick_wins_module()
+
+        self.assertEqual(module.get_first_ranking([{"position": 11}]), {"position": 11})
+        self.assertIsNone(module.get_first_ranking([]))
+        self.assertIsNone(module.get_first_ranking([None]))
+
+    def test_get_serp_features_returns_empty_list_on_provider_error(self):
+        module = load_research_quick_wins_module()
+
+        class FakeDFS:
+            def get_serp_data(self, keyword, limit=10):
+                raise RuntimeError("api down")
+
+        self.assertEqual(
+            module.get_serp_features(FakeDFS(), "podcast monetization"), []
+        )
+
+    def test_get_serp_features_extracts_feature_list(self):
+        module = load_research_quick_wins_module()
+
+        class FakeDFS:
+            def get_serp_data(self, keyword, limit=10):
+                return {"features": ["people_also_ask", "featured_snippet"]}
+
+        self.assertEqual(
+            module.get_serp_features(FakeDFS(), "podcast monetization"),
+            ["people_also_ask", "featured_snippet"],
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()