> ## 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. # TikTok Trend → Content Pipeline > Discover trending TikTok hashtags, check brand alignment via Mave, and generate ready-to-film TikTok-native scripts. ### Scenario TikTok trends move fast — a hashtag can peak and die in 72 hours. This job uses TikTok's Research API to discover trending hashtags, sends each to Mave for brand-alignment analysis ("Does this trend fit our brand?"), then feeds approved trends into Mavera's Generate endpoint to produce ready-to-film scripts. The result is a content pipeline that reacts to trends at platform speed. ### Architecture ```mermaid theme={"dark"} flowchart LR A["TikTok trending hashtags"] --> B["Mavera POST /mave/chat (brand alignment)"] B --> C["Filter aligned trends"] C --> D["POST /generations"] D --> E["TikTok-native scripts"] ``` ### Code ```python Python theme={"dark"} import os, requests, time TT = os.environ["TIKTOK_ACCESS_TOKEN"] ADV = os.environ["TIKTOK_ADVERTISER_ID"] MV = os.environ["MAVERA_API_KEY"] TT_BASE = "https://business-api.tiktok.com/open_api/v1.3" MV_BASE = "https://app.mavera.io/api/v1" TT_H = {"Access-Token": TT} MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"} BRAND_CONTEXT = """ Brand: [Your Brand] — B2C wellness/fitness product Audience: 18-34, health-conscious, active lifestyle Tone: Energetic, authentic, educational. Avoid: clinical, preachy, overly polished. Topics: Fitness routines, nutrition tips, mental health, product demos. Avoid topics: Politics, religion, controversy, competitor bashing. """ # 1. Discover trending hashtags via Research API trending = requests.post(f"{TT_BASE}/research/hashtag/trending/", headers=TT_H, json={ "advertiser_id": ADV, "country_code": "US", "period": 7, "limit": 20, }) if trending.status_code != 200 or trending.json().get("code") != 0: # Fallback: query top-performing hashtags from your own campaigns trending_data = requests.post(f"{TT_BASE}/reports/integrated/get/", headers=TT_H, json={ "advertiser_id": ADV, "report_type": "BASIC", "data_level": "AUCTION_AD", "dimensions": ["ad_id"], "metrics": ["ad_name", "clicks", "impressions"], "start_date": "2025-11-01", "end_date": "2025-12-31", "page_size": 10, "page": 1, }).json() hashtags = [{"name": f"trending_fallback_{i}", "views": 0} for i in range(5)] else: hashtags = [{"name": h.get("hashtag_name", ""), "views": h.get("video_views", 0)} for h in trending.json().get("data", {}).get("list", [])] # 2. Brand alignment check via Mave aligned_trends = [] for tag in hashtags[:10]: check = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={ "message": f"""Evaluate whether this TikTok trend aligns with our brand. TREND: #{tag['name']} ({tag['views']:,} views in 7 days) BRAND CONTEXT: {BRAND_CONTEXT} Score 1-10 for brand alignment. Explain why or why not. If aligned (7+): suggest an angle that connects the trend to our product. If not aligned: explain the risk.""" }).json() content = check.get("content", "") score_line = [l for l in content.split("\n") if "score" in l.lower() or "/10" in l] try: score = int("".join(c for c in (score_line[0] if score_line else "0") if c.isdigit())[:2]) except (ValueError, IndexError): score = 0 if score >= 7: aligned_trends.append({"tag": tag["name"], "views": tag["views"], "score": score, "angle": content[:300]}) print(f"Trends checked: {min(len(hashtags), 10)} | Aligned: {len(aligned_trends)}") # 3. Generate TikTok scripts for aligned trends for trend in aligned_trends[:5]: script = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={ "prompt": f"""Write a TikTok video script for the trend #{trend['tag']}. BRAND ANGLE: {trend['angle'][:200]} Requirements: - 15-30 second duration - Hook in first 1.5 seconds (text overlay + spoken) - Include trending audio suggestion - End with clear CTA - Write in a shooting-script format: [VISUAL] / [AUDIO] / [TEXT OVERLAY] Make it feel native to TikTok, not like an ad.""", }).json() print(f"\n{'='*50}") print(f"TREND: #{trend['tag']} | Alignment: {trend['score']}/10 | Views: {trend['views']:,}") print(f"{'='*50}") print(script.get("output", script.get("content", ""))[:500]) ``` ```javascript JavaScript theme={"dark"} const TT = process.env.TIKTOK_ACCESS_TOKEN; const ADV = process.env.TIKTOK_ADVERTISER_ID; const MV = process.env.MAVERA_API_KEY; const TT_BASE = "https://business-api.tiktok.com/open_api/v1.3"; const MV_BASE = "https://app.mavera.io/api/v1"; const TT_H = { "Access-Token": TT, "Content-Type": "application/json" }; const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" }; const BRAND_CONTEXT = `Brand: [Your Brand] — B2C wellness/fitness Audience: 18-34, health-conscious. Tone: Energetic, authentic, educational. Topics: Fitness, nutrition, mental health, product demos. Avoid: politics, controversy.`; // 1. Trending hashtags let hashtags; const trending = await fetch(`${TT_BASE}/research/hashtag/trending/`, { method: "POST", headers: TT_H, body: JSON.stringify({ advertiser_id: ADV, country_code: "US", period: 7, limit: 20 }), }); const trendData = await trending.json(); if (trendData.code === 0) { hashtags = (trendData.data?.list || []).map(h => ({ name: h.hashtag_name, views: h.video_views || 0 })); } else { hashtags = Array.from({ length: 5 }, (_, i) => ({ name: `trending_fallback_${i}`, views: 0 })); } // 2. Brand alignment via Mave const alignedTrends = []; for (const tag of hashtags.slice(0, 10)) { const check = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H, body: JSON.stringify({ message: `Evaluate TikTok trend #${tag.name} (${tag.views.toLocaleString()} views) for brand alignment.\n\n${BRAND_CONTEXT}\n\nScore 1-10. If 7+: suggest angle. If not: explain risk.`, }), }).then(r => r.json()); const content = check.content || ""; const scoreLine = content.split("\n").find(l => l.toLowerCase().includes("score") || l.includes("/10")) || ""; const score = parseInt((scoreLine.match(/\d+/) || ["0"])[0], 10); if (score >= 7) { alignedTrends.push({ tag: tag.name, views: tag.views, score, angle: content.slice(0, 300) }); } } console.log(`Trends checked: ${Math.min(hashtags.length, 10)} | Aligned: ${alignedTrends.length}`); // 3. Generate scripts for (const trend of alignedTrends.slice(0, 5)) { const script = await fetch(`${MV_BASE}/generations`, { method: "POST", headers: MV_H, body: JSON.stringify({ prompt: `Write a TikTok script for #${trend.tag}.\n\nBRAND ANGLE: ${trend.angle.slice(0, 200)}\n\n15-30s duration. Hook in 1.5s. Trending audio suggestion. CTA. Shooting-script format: [VISUAL]/[AUDIO]/[TEXT OVERLAY]. Native TikTok feel.`, }), }).then(r => r.json()); console.log(`\n${"=".repeat(50)}`); console.log(`TREND: #${trend.tag} | Alignment: ${trend.score}/10 | Views: ${trend.views.toLocaleString()}`); console.log("=".repeat(50)); console.log((script.output || script.content || "").slice(0, 500)); } ``` ### Example Output ```text theme={"dark"} Trends checked: 10 | Aligned: 4 ================================================== TREND: #MorningRoutine | Alignment: 9/10 | Views: 48,200,000 ================================================== [0.0-1.5s] VISUAL: Close-up of alarm clock → hand reaches for product on nightstand AUDIO: Trending sound — "Espresso" by Sabrina Carpenter (instrumental) TEXT OVERLAY: "POV: You actually look forward to mornings now" [1.5-8.0s] VISUAL: Quick cuts — splash of water, product application, mirror check AUDIO: Beat drop syncs with each cut TEXT OVERLAY: "Step 1: [Product]. That's it. That's the routine." [8.0-15.0s] VISUAL: Side-by-side before/after (split screen, same lighting) AUDIO: Music continues, lower volume TEXT OVERLAY: "30 days. Zero filter." → "Link in bio for 20% off" ================================================== TREND: #GymTok | Alignment: 8/10 | Views: 31,500,000 ================================================== [0.0-1.5s] VISUAL: Walking into gym, camera low angle AUDIO: "I don't need a gym buddy, I need a gym therapist" sound TEXT OVERLAY: "Things I wish I knew before my first gym month" ``` ### Error Handling TikTok's Research API requires separate approval. If unavailable, fall back to your own campaign performance data to identify what's working. TikTok trends peak in 48-72 hours. Run this job daily and prioritize scripts for trends in their first 24 hours of acceleration. Mave's score is a starting point. Add human review for trends touching sensitive topics (health claims, cultural references) before filming. ***