> ## Documentation Index > Fetch the complete documentation index at: https://docs.mavera.io/llms.txt > Use this file to discover all available pages before exploring further. # Trending Content Gap Analysis > Pull YouTube trending videos, identify content themes missing from your channel, and generate specific video concepts to fill the gaps ## Scenario YouTube's Most Popular chart reveals what the platform is amplifying right now — but your brand isn't on it. This job pulls the top 50 trending videos via `videos.list?chart=mostPopular`, extracts their categories, tags, and descriptions, then sends the full dataset to Mave with your brand context. Mave identifies content themes that are trending but absent from your channel — gaps your brand could own with timely content. The result is a content gap report with specific video concepts ranked by opportunity size. ## Architecture ```mermaid theme={"dark"} flowchart LR A["YouTube videos.list?chart=mostPopular"] --> B[Extract categories + tags] --> C["POST /api/v1/mave/chat"] --> D[Content gap analysis + recommendations] ``` ## Code ```python Python theme={"dark"} import os, requests YT = os.environ["YOUTUBE_API_KEY"] MV = os.environ["MAVERA_API_KEY"] YT_BASE = "https://www.googleapis.com/youtube/v3" MV_BASE = "https://app.mavera.io/api/v1" MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"} BRAND_CONTEXT = """ Brand: [Your Brand] — DTC fitness/wellness company Channel focus: Workout tutorials, nutrition science, product reviews Audience: 18-35, health-conscious, 60% female, US-based Current content gaps we suspect: trending challenges, reaction content, collaborations """ # 1. Fetch top 50 trending videos (1 quota unit per call) trending = requests.get(f"{YT_BASE}/videos", params={ "key": YT, "chart": "mostPopular", "part": "snippet,contentDetails,statistics,topicDetails", "maxResults": 50, "regionCode": "US", }).json() if "error" in trending: raise SystemExit(f"YouTube API error: {trending['error']['message']}") videos = [] category_counts = {} all_tags = [] for item in trending.get("items", []): snippet = item["snippet"] stats = item.get("statistics", {}) category = snippet.get("categoryId", "0") tags = snippet.get("tags", []) category_counts[category] = category_counts.get(category, 0) + 1 all_tags.extend(tags[:10]) videos.append({ "title": snippet["title"], "channel": snippet["channelTitle"], "category": category, "tags": tags[:10], "views": int(stats.get("viewCount", 0)), "likes": int(stats.get("likeCount", 0)), "description": snippet.get("description", "")[:200], }) # 2. Resolve category IDs to names cat_ids = ",".join(set(category_counts.keys())) categories = requests.get(f"{YT_BASE}/videoCategories", params={ "key": YT, "id": cat_ids, "part": "snippet", }).json() cat_map = { item["id"]: item["snippet"]["title"] for item in categories.get("items", []) } # 3. Build trending summary trending_block = "\n".join( f"- \"{v['title']}\" by {v['channel']} | Category: {cat_map.get(v['category'], v['category'])} | " f"Views: {v['views']:,} | Tags: {', '.join(v['tags'][:5])}" for v in sorted(videos, key=lambda x: -x["views"])[:30] ) category_summary = "\n".join( f"- {cat_map.get(cid, cid)}: {count} videos" for cid, count in sorted(category_counts.items(), key=lambda x: -x[1]) ) # 4. Gap analysis via Mave gap = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={ "message": f"""Identify content gaps between what's trending on YouTube and what our brand covers. BRAND CONTEXT: {BRAND_CONTEXT} TRENDING NOW (top 30 by views): {trending_block} CATEGORY DISTRIBUTION: {category_summary} TOP TRENDING TAGS: {', '.join(set(all_tags)[:30])} Produce: 1. **Trending themes** our brand is NOT covering (ranked by opportunity size) 2. **Specific video concepts** for each gap (title, format, estimated audience size) 3. **Cross-category opportunities** — trends in non-fitness categories our fitness brand could adapt 4. **Timing urgency** — which gaps are time-sensitive (trend peaking) vs. evergreen 5. **Risk assessment** — which trends could backfire for our brand""", }).json() print("TRENDING CONTENT GAP ANALYSIS") print("=" * 60) print(f"Analyzed: {len(videos)} trending videos across {len(category_counts)} categories") print(f"Top categories: {', '.join(cat_map.get(c, c) for c, _ in sorted(category_counts.items(), key=lambda x: -x[1])[:5])}") print("\n" + gap.get("content", "")[:2500]) ``` ```javascript JavaScript theme={"dark"} const YT = process.env.YOUTUBE_API_KEY; const MV = process.env.MAVERA_API_KEY; const YT_BASE = "https://www.googleapis.com/youtube/v3"; const MV_BASE = "https://app.mavera.io/api/v1"; const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" }; const BRAND_CONTEXT = `Brand: [Your Brand] — DTC fitness/wellness Channel: Workout tutorials, nutrition science, product reviews Audience: 18-35, health-conscious, 60% female, US Suspected gaps: trending challenges, reaction content, collaborations`; // 1. Trending videos (1 quota unit) const trending = await fetch( `${YT_BASE}/videos?key=${YT}&chart=mostPopular&part=snippet,contentDetails,statistics,topicDetails&maxResults=50®ionCode=US` ).then(r => r.json()); if (trending.error) throw new Error(trending.error.message); const videos = []; const categoryCounts = {}; const allTags = []; for (const item of trending.items || []) { const s = item.snippet; const stats = item.statistics || {}; const cat = s.categoryId || "0"; const tags = (s.tags || []).slice(0, 10); categoryCounts[cat] = (categoryCounts[cat] || 0) + 1; allTags.push(...tags); videos.push({ title: s.title, channel: s.channelTitle, category: cat, tags, views: parseInt(stats.viewCount || "0", 10), likes: parseInt(stats.likeCount || "0", 10), description: (s.description || "").slice(0, 200), }); } // 2. Category names const catIds = [...new Set(Object.keys(categoryCounts))].join(","); const categories = await fetch( `${YT_BASE}/videoCategories?key=${YT}&id=${catIds}&part=snippet` ).then(r => r.json()); const catMap = Object.fromEntries( (categories.items || []).map(i => [i.id, i.snippet.title]) ); // 3. Build summary const trendingBlock = videos .sort((a, b) => b.views - a.views).slice(0, 30) .map(v => `- "${v.title}" by ${v.channel} | ${catMap[v.category] || v.category} | Views: ${v.views.toLocaleString()} | Tags: ${v.tags.slice(0, 5).join(", ")}`) .join("\n"); const catSummary = Object.entries(categoryCounts) .sort(([, a], [, b]) => b - a) .map(([cid, count]) => `- ${catMap[cid] || cid}: ${count} videos`) .join("\n"); // 4. Gap analysis const gap = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H, body: JSON.stringify({ message: `Identify content gaps between trending YouTube and our brand.\n\nBRAND:\n${BRAND_CONTEXT}\n\nTRENDING (top 30):\n${trendingBlock}\n\nCATEGORIES:\n${catSummary}\n\nTOP TAGS: ${[...new Set(allTags)].slice(0, 30).join(", ")}\n\nProduce: 1) Trending themes we miss 2) Specific video concepts 3) Cross-category opportunities 4) Timing urgency 5) Risk assessment`, }), }).then(r => r.json()); console.log("TRENDING CONTENT GAP ANALYSIS"); console.log("=".repeat(60)); console.log(`Analyzed: ${videos.length} videos, ${Object.keys(categoryCounts).length} categories`); console.log((gap.content || "").slice(0, 2500)); ``` ## Example Output ```text theme={"dark"} TRENDING CONTENT GAP ANALYSIS ============================================================ Analyzed: 50 trending videos across 12 categories Top categories: Entertainment, Music, Sports, Gaming, People & Blogs ## Content Gaps (Ranked by Opportunity) ### 1. "Get Ready With Me" Fitness Edition (HIGH URGENCY) - 4 trending GRWM videos, none fitness-focused - Concept: "GRWM for a 5AM Workout" — morning routine + product integration - Format: 8-12 min, vlog-style, trending audio - Audience: 2.4M views on average for GRWM content this week ### 2. Reaction/Commentary on Fitness Trends (MEDIUM URGENCY) - 6 trending reaction videos, fitness commentary absent - Concept: "Trainer Reacts to Viral Workout Hacks" — debunk + educate - Format: 10-15 min, split-screen reaction format - Audience: Commentary format averages 1.8M views ### 3. Challenge Format — Adapted (EVERGREEN) - 3 trending challenge videos, zero fitness challenges this week - Concept: "30-Day Morning Routine Challenge" — serialized, daily uploads - Risk: Challenge fatigue — must feel fresh, not derivative ### 4. Cross-Category: Gaming × Fitness (EXPERIMENTAL) - Gaming dominates trending (8 videos). No gaming-fitness crossover exists - Concept: "I Trained Like a Pro Gamer for 7 Days" — posture, nutrition, endurance - Risk: Low — unique angle, high shareability ## Risk Assessment - Avoid: Political commentary trending videos — brand misalignment - Caution: Music trends — licensing complexity for brand channels ``` ## Error Handling Without `regionCode`, results default to the US. Set this explicitly for non-US brands. Not all regions return 50 results. YouTube uses numeric category IDs (e.g., 17 = Sports). The `videoCategories` endpoint translates these. Some categories are region-specific. This entire job uses \~2 quota units (1 for `videos.list`, 1 for `videoCategories`). It's the most quota-efficient job in this guide. ***