trending-content-scout
installsforks
How to install trending-content-scout?
affitor/affiliate-skills/trending-content-scout
$npx -y skills add Affitor/affiliate-skills --skill trending-content-scoutScan 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
SKILL.md
| 1 | # Trending Content Scout |
| 2 | |
| 3 | Scan YouTube, TikTok, X, and Reddit for top-performing content by real engagement data. |
| 4 | Find winning formats, hooks, and content gaps — **before** you create anything. Stop |
| 5 | guessing what works. See what's already winning, then build on proven patterns. |
| 6 | |
| 7 | This skill is the **data foundation** for the entire content pipeline. Run it first, |
| 8 | then feed its output into `content-angle-ranker`, `viral-post-writer`, `tiktok-script-writer`, |
| 9 | or any S2/S3 content skill. |
| 10 | |
| 11 | ## Stage |
| 12 | |
| 13 | This skill belongs to Stage S1: Research |
| 14 | |
| 15 | ## When to Use |
| 16 | |
| 17 | - Before creating any content for a keyword or niche |
| 18 | - When entering a new niche and need to understand what content works |
| 19 | - When comparing engagement across platforms for a topic |
| 20 | - When looking for content gaps competitors haven't filled |
| 21 | - When benchmarking your existing content against what's performing |
| 22 | - As the first step in any content creation workflow (before S2 skills) |
| 23 | |
| 24 | ## Input Schema |
| 25 | |
| 26 | ```yaml |
| 27 | keyword: string # (required) Search keyword — "AI video tools", "email marketing tips" |
| 28 | platforms: string[] # (optional, default: ["youtube", "tiktok"]) |
| 29 | # Options: "youtube" | "tiktok" | "x" | "reddit" |
| 30 | sort_by: string # (optional, default: "engagement_score") |
| 31 | # Options: "views" | "likes" | "engagement_score" | "recency" |
| 32 | time_range: string # (optional, default: "30d") "7d" | "30d" | "90d" | "all" |
| 33 | limit: number # (optional, default: 20) Max content pieces to analyze |
| 34 | product: object # (optional) Specific product to focus on |
| 35 | name: string # "HeyGen" |
| 36 | url: string # "https://heygen.com" |
| 37 | ``` |
| 38 | |
| 39 | No `api_config` needed in input — skills auto-detect configuration from conversation |
| 40 | context, project settings, or CLAUDE.md. See `shared/references/social-data-providers.md` |
| 41 | for setup instructions. |
| 42 | |
| 43 | ## Workflow |
| 44 | |
| 45 | ### Step 1: Determine Data Source |
| 46 | |
| 47 | Check if the user has API configuration available: |
| 48 | |
| 49 | ``` |
| 50 | IF social_data_config exists in context/settings for a platform: |
| 51 | → Use configured API for that platform |
| 52 | → Structured data: exact views, likes, comments, shares |
| 53 | |
| 54 | ELSE (default — no API): |
| 55 | → Use web_search + web_fetch |
| 56 | → Still effective — see fallback methods below |
| 57 | ``` |
| 58 | |
| 59 | **API mode** (when configured): |
| 60 | |
| 61 | For each platform in `platforms`: |
| 62 | - YouTube: Search API → get video list → Details API → get statistics (views, likes, comments) |
| 63 | - TikTok: Search API → get video list with stats (playCount, diggCount, commentCount, shareCount) |
| 64 | - X: Search API → get tweets with public_metrics (impressions, likes, retweets, replies) |
| 65 | - Reddit: Search API → get posts with score and comment count |
| 66 | |
| 67 | See `shared/references/social-data-providers.md` for specific API endpoints and config. |
| 68 | |
| 69 | **web_search fallback** (no API — default): |
| 70 | |
| 71 | ``` |
| 72 | For YouTube: |
| 73 | web_search "[keyword] site:youtube.com" → top 10-15 video results |
| 74 | For each result: extract title, channel, view count from search snippet |
| 75 | Optional: web_fetch individual video pages for likes/comments (slower) |
| 76 | |
| 77 | For TikTok: |
| 78 | web_search "[keyword] tiktok" → find popular TikTok content |
| 79 | web_search "[keyword] site:tiktok.com" → direct TikTok results |
| 80 | Extract: titles, creators, approximate view counts from snippets |
| 81 | |
| 82 | For X: |
| 83 | web_search "[keyword] site:x.com" OR "[keyword] site:twitter.com" → top tweets |
| 84 | Extract: tweet text, author, engagement signals from snippets |
| 85 | |
| 86 | For Reddit: |
| 87 | web_search "[keyword] site:reddit.com" → top Reddit discussions |
| 88 | web_fetch top results → extract upvotes, comments from page |
| 89 | web_search "reddit [keyword] top upvoted" → find popular threads |
| 90 | ``` |
| 91 | |
| 92 | Note which data source was used — include in output for transparency. |
| 93 | |
| 94 | ### Step 2: Collect and Normalize Data |
| 95 | |
| 96 | For each content piece found, extract and norm |
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