Trending Content Scout

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 2515ce9634da · 15 Oct 2026Report an issue or request a re-scan

• PublisherAffitor GitHub organizationOwner of the repository this skill is read from
• Source folderAffitor/affiliate-skills/skills/research/trending-content-scoutThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict2515ce9634daA new audit runs when the source changes
• Repository statusNot archivedLast push 2 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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…

---
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

```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: "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:

```yaml
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 For

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-15
   ✓SAFE B 89 / 100

   first audit · source 2515ce9634da

Questions about Trending Content Scout

• github.com/Affitor/affiliate-skills/tree/main/skills/research/tr — the audited source at commit 2515ce9634da and every finding aboveObserved · 15 Oct 2026

Spotted something wrong? Report a correction · Methodology