trending-content-scout
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
npx skills add Affitor/affiliate-skills --skill trending-content-scout --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Scans YouTube, TikTok, X, and Reddit for top-performing content by real engagement data to identify winning formats, hooks, and content gaps before creating anything.
How it works
- Accepts input with fields: keyword, platforms (default includes YouTube and TikTok), sort_by (default engagement_score), time_range (default 30d), limit (default 20), and optional product focus.
- Determines data source: uses configured APIs if social_data_config exists; otherwise uses web_search + web_fetch fallback.
- For each platform, collects content pieces and normalizes data into a standard ContentItem schema with title, url, platform, creator, views, likes, comments, shares, published_date, duration, engagement_score, content_format, hook_type.
- Calculates engagement_score via the standard formula: engagement_score = (likes × 2 + comments × 3 + shares × 5) / max(views, 1) × 1000.
- Classifies content_format from title/description (e.g., comparison, tutorial, listicle, review, etc.) and hook_type from first sentence/title (e.g., question, shock, bold_claim, demo_first, relatable, contrarian).
- Sorts results by the chosen sort_by (default engagement_score) and returns the top limit pieces.
- Analyzes patterns across top content for formats and hooks, analyzes duration distributions, identifies top creators, and performs a gap analysis to surface actionable opportunities.
- Computes an engagement benchmark from the collected data, including sample_size, medians, and top-10% thresholds per platform where available.
- Performs a self-validation to ensure data source disclosure, consistent engagement_scores, valid classifications, and at least 3 actionable gaps.
When to use it
- Before creating any content for a keyword or niche.
- When entering a new niche to understand what content works.
- When comparing engagement across platforms for a topic.
- When looking for content gaps competitors haven’t filled.
- As the first step in a content creation workflow (before S2 skills).
What it can touch
- Platforms: YouTube, TikTok, X, Reddit (via API or web methods depending on configuration).
- Data fields: title, url, platform, creator, views, likes, comments, shares, published_date, duration, engagement_score, content_format, hook_type.
Caveats
- Behavior depends on whether an API is configured; fallback uses web_search methods if API config is absent.
- Engagement_score is computed from available metrics; some platforms may have limited fields (e.g., Reddit shares not used).
- Classifications rely on textual signals from titles and descriptions and may be imperfect without full page parsing.
# 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 ```yaml 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: "engage
- Stage
- When to Use
- Input Schema
- Workflow
- Step 1: Determine Data Source
- Step 2: Collect and Normalize Data
- Step 3: Sort and Rank
- Step 4: Analyze Patterns
- Step 5: Calculate Engagement Benchmark
- Step 6: Self-Validation
- Output Schema
- Output Format
- Error Handling
- Examples
What does the trending-content-scout skill do?
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
How do I install it?
Run `npx skills add Affitor/affiliate-skills --skill trending-content-scout --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Affitor/affiliate-skills, a repository with 586 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.