trending-content-scout

name trending-content-scout description Scan social platforms for top-performing content by engagement before you create anything. Use this skill when the user wants to see what content is winning in a niche, find viral content patterns, research what's working on YouTube/TikTok/X/Reddit, benchmark engagement, discover content gaps, or says "what content is working for [topic]", "show me top performing content about [keyword]", "what's trending in [niche]", "find viral content about [product]", "content research for [keyword]", "what gets views in [niche]", "engagement analysis for [topic]", "scout the competition", "what videos are getting the most views about [keyword]", "social listening for [topic]", "trending content in [niche]", "top content analysis", "what hooks work for [keyword]", "content intelligence", "find winning formats". license MIT version 1.0.0 tags ["affiliate-marketing","research","social-data","engagement","trending","content-intelligence"] compatibility Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent metadata {"author":"affitor","version":"1.0","stage":"S1-Research"} Trending Content Scout Scan YouTube, TikTok, X, and Reddit for top-performing content by real engagement data. Find winning formats, hooks, and content gaps — before you create anything. Stop guessing what works. See what's already winning, then build on proven patterns. This skill is the data foundation for the entire content pipeline. Run it first, then feed its output into content-angle-ranker , viral-post-writer , tiktok-script-writer , or any S2/S3 content skill. Stage This skill belongs to Stage S1: Research When to Use Before creating any content for a keyword or niche When entering a new niche and need to understand what content works When comparing engagement across platforms for a topic When looking for content gaps competitors haven't filled When benchmarking your existing content against what's performing As the first step in any content creation workflow (before S2 skills) Input Schema keyword: string # (required) Search keyword — "AI video tools", "email marketing tips" platforms: string[] # (optional, default: ["youtube", "tiktok"]) # Options: "youtube" | "tiktok" | "x" | "reddit" sort_by: string # (optional, default: "engagement_score") # Options: "views" | "likes" | "engagement_score" | "recency" time_range: string # (optional, default: "30d") "7d" | "30d" | "90d" | "all" limit: number # (optional, default: 20) Max content pieces to analyze product: object # (optional) Specific product to focus on name: string # "HeyGen" url: string # "https://heygen.com" No api_config needed in input — skills auto-detect configuration from conversation context, project settings, or CLAUDE.md. See shared/references/social-data-providers.md for setup instructions. Workflow Step 1: Determine Data Source Check if the user has API configuration available: IF social_data_config exists in context/settings for a platform: → Use configured API for that platform → Structured data: exact views, likes, comments, shares ELSE (default — no API): → Use web_search + web_fetch → Still effective — see fallback methods below API mode (when configured): For each platform in platforms : YouTube: Search API → get video list → Details API → get statistics (views, likes, comments) TikTok: Search API → get video list with stats (playCount, diggCount, commentCount, shareCount) X: Search API → get tweets with public_metrics (impressions, likes, retweets, replies) Reddit: Search API → get posts with score and comment count See shared/references/social-data-providers.md for specific API endpoints and config. web_search fallback (no API — default): For YouTube: web_search "[keyword] site:youtube.com" → top 10-15 video results For each result: extract title, channel, view count from search snippet Optional: web_fetch individual video pages for likes/comments (slower) For TikTok: web_search "[keyword] tiktok" → find popular TikTok content web_search "[keyword] site:tiktok.com" → direct TikTok results Extract: titles, creators, approximate view counts from snippets For X: web_search "[keyword] site:x.com" OR "[keyword] site:twitter.com" → top tweets Extract: tweet text, author, engagement signals from snippets For Reddit: web_search "[keyword] site:reddit.com" → top Reddit discussions web_fetch top results → extract upvotes, comments from page web_search "reddit [keyword] top upvoted" → find popular threads Note which data source was used — include in output for transparency. Step 2: Collect and Normalize Data For each content piece found, extract and normalize into a standard schema: ContentItem: title: string # Video title, tweet text (first line), post title url: string # Direct link to content platform: string # "youtube" | "tiktok" | "x" | "reddit" creator: string # Channel name, @handle, username views: number # View/impression count (0 if unavailable) likes: number # Like/upvote count (0 if unavailable) comments: number # Comment/reply count (0 if unavailable) shares: number # Share/retweet count (0 if unavailable) published_date: string # ISO date or relative ("3 days ago") duration: string # Video duration ("2:34") — video only engagement_score: number # Calculated — see formula below content_format: string # Detected format (see classification below) hook_type: string # Detected hook style (see classification below) Engagement Score Formula (consistent across all Affitor skills): engagement_score = (likes × 2 + comments × 3 + shares × 5) / max(views, 1) × 1000 Platform-specific adjustments: Reddit: (score × 2 + num_comments × 3) / max(score, 1) × 1000 (no share count) X: Use retweets as shares, replies as comments YouTube: Estimate shares as comments × 0.5 (not available via most APIs) web_search fallback: If only views are available, use views as the ranking signal and note that engagement_score is estimated See shared/references/social-data-providers.md for full formula documentation. Content Format Classification: Detect format from title and description: comparison: Contains "vs", "versus", "compared to", "X or Y", "better than" review: Contains "review", "honest review", "worth it", "my experience" tutorial: Contains "how to", "step by step", "guide", "tutorial", "walkthrough" listicle: Contains "top X", "best X", "X tools", "X ways", numbers in title reaction: Contains "I tried", "testing", "first time using", "is it worth" story: Contains "how I", "my journey", "I made $X", personal narrative demo: Contains "demo", "showing", "watch me use", "in action" explainer: Contains "what is", "explained", "why you need", "everything about" Hook Type Classification: Detect from first sentence/title: question: Starts with or contains a question shock: Contains surprising numbers, "you won't believe", extreme claims bold_claim: "This replaced X", "The only tool you need", definitive statements demo_first: Starts with showing a result or end product relatable: "POV:", "When you...", shared experience pattern contrarian: "Stop using X", "X is overrated", against conventional wisdom Step 3: Sort and Rank Sort all collected content by the chosen sort_by parameter: engagement_score (default): Best for finding content that resonates regardless of creator size views : Best for finding content with broadest reach likes : Best for finding content people actively endorse recency : Best for finding what's working RIGHT NOW Take top limit results after sorting. Step 4: Analyze Patterns From the top content, extract actionable patterns: Format Analysis: For each content_format in top results: count: how many of top 20 use this format avg_engagement: average engagement_score for this format best_example: highest engagement content in this format Hook Analysis: For each hook_type in top results: count: how many use this hook avg_engagement: average engagement_score best_example: highest engagement content with this hook Duration Analysis (video platforms only): Group videos by duration buckets: <30s, 30-60s, 60-120s, 2-5min, 5-10min, 10-20min, 20min+ For each bucket: count and average engagement → Identify optimal duration range Creator Analysis: For each unique creator in top results: content_count: how many pieces in top results avg_engagement: average engagement score platforms: which platforms they're on dominant_format: their most-used format Gap Analysis: This is the most strategically valuable output. Look for: Format gaps: If 90% of top content is reviews, comparisons are underserved Platform gaps: If YouTube is saturated but TikTok has few results → TikTok opportunity Angle gaps: Common user questions (visible in comments/replies) that no top content addresses Audience gaps: All content targets advanced users → beginner content is a gap Recency gaps: Top content is 6+ months old → fresh take on same topic is an opportunity Honesty gaps: All content is positive/promotional → honest cons/limitations review is a gap For gap analysis with web_search fallback: web_search "[keyword] reddit questions" → find unanswered user questions web_search "[keyword] alternatives nobody talks about" → find underserved angles Step 5: Calculate Engagement Benchmark Set benchmark ranges so user knows what "good" looks like: engagement_benchmark: sample_size: number # how many content pieces analyzed median_views: number # 50th percentile views median_engagement_score: number top_10_percent_threshold: views: number # views needed to be in top 10% engagement_score: number # engagement_score needed for top 10% platform_averages: # per-platform breakdown youtube: median_views: number median_engagement: number tiktok: median_views: number median_engagement: number Step 6: Self-Validation Before presenting output, verify: Data source is clearly stated (API vs web_search) Engagement scores are calculated consistently using the standard formula Content format and hook classifications are based on actual title/description analysis, not guesses Gap analysis includes at least 3 specific, actionable gaps Benchmark numbers are derived from actual data, not made up Recommendations connect to specific downstream skills If any check fails, fix the output before delivering. Do not flag the checklist to the user. Output Schema Other skills can consume these fields from conversation context: output_schema_version: "1.0.0" keyword: string platforms_scanned: string[] data_source: "api" | "web_search" | "mixed" # transparency total_content_analyzed: number top_content: ContentItem[] # top results sorted by sort_by pattern_analysis: winning_formats: - format: string # "comparison" count: number # 9 percentage: number # 45 avg_engagement: number # 35.2 best_example: title: string url: string engagement_score: number winning_hooks: - hook_type: string count: number avg_engagement: number best_example: title: string url: string optimal_duration: range: string # "45-60 seconds" platform: string # "tiktok" avg_engagement: number top_creators: - name: string platform: string content_count: number avg_engagement: number dominant_format: string content_gaps: string[] # specific, actionable gaps engagement_benchmark: sample_size: number median_views: number median_engagement_score: number top_10_percent_threshold: views: number engagement_score: number platform_averages: object recommended_angles: string[] # top 3 content angles based on gaps + engagement recommended_next_skill: string # "content-angle-ranker" Output Format ## Trending Content Scout: [Keyword] ### Data Source 📊 **[API: YouTube Data API + RapidAPI TikTok | web _search (no API configured)]** Scanned: [X] content pieces across [Y] platforms Time range: [30 days] --- ### 🏆 Top Performing Content | # | Title | Platform | Creator | Views | Eng. Score | Format | Hook | |---|-------|----------|---------|-------|------------|--------|------| | 1 | [Title] | YouTube | @creator | 150K | 42.3 | comparison | bold_ claim | | 2 | [Title] | TikTok | @creator | 800K | 38.1 | demo | demo _first | | 3 | [Title] | YouTube | @creator | 95K | 35.7 | tutorial | question | | ... | ... | ... | ... | ... | ... | ... | ... | --- ### 📈 Pattern Analysis **Winning Formats:** | Format | Count | % of Top 20 | Avg Engagement | Verdict | |--------|-------|-------------|----------------|---------| | Comparison | 9 | 45% | 35.2 | 🔥 Dominant — proven winner | | Tutorial | 5 | 25% | 28.4 | ✅ Solid performer | | Review | 4 | 20% | 22.1 | ⚡ Works but competitive | | Listicle | 2 | 10% | 18.5 | ➖ Below average | **Best Hooks:** 1. 🥇 **Bold claim** — "This tool replaced my $5K/mo agency" (avg engagement: 41.3) 2. 🥈 **Demo first** — Show end result in first 3 seconds (avg: 36.8) 3. 🥉 **Contrarian** — "Stop using X, use this instead" (avg: 33.2) **Duration Sweet Spot:** - TikTok: 45-60 seconds (avg engagement: 34.2) - YouTube: 8-12 minutes (avg engagement: 31.5) **Top Creators in This Space:** | Creator | Platform | Pieces in Top 20 | Avg Engagement | Style | |---------|----------|-------------------|----------------|-------| | @creator1 | YouTube | 4 | 38.5 | In-depth comparisons | | @creator2 | TikTok | 3 | 35.2 | Quick demos | --- ### 🕳️ Content Gaps (Opportunities) 1. **[Gap 1]:** Nobody comparing [Product A] vs [Product B] on TikTok — YouTube has 5 comparisons, TikTok has zero 2. **[Gap 2]:** No "honest cons" content — all top content is positive. Authentic negative review = differentiation 3. **[Gap 3]:** Missing "[keyword] for [specific audience]" — all content targets general audience