Agent skill

Trending Content Scout

by Affitor in Affitor/affiliate-skills

Scan social platforms for top-performing content by engagement before you create anything.

MITAuto-check passedMarketing & SEO

Install Trending Content Scout

skills CLI
$ npx skills add Affitor/affiliate-skills --skill trending-content-scout -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Affitor/affiliate-skills trending-content-scout --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Affitor/affiliate-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/trending-content-scout .claude/skills/trending-content-scout && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
trending-content-scout
GitHub stars
700
Token cost
~5.4k tokens
SKILL.md length
1,416 words
Files
1
Skills in repo
50
Repo updated
First seen
Licence
MIT

At a glance

Scan social platforms for top-performing content by engagement before you create anything.

  • Works in 6 steps: Determine Data Source → Collect and Normalize Data → Sort and Rank → …
  • The user wants to see what content is winning in a niche
  • SKILL.md covers Stage, When to Use, Input Schema and Workflow, plus 7 more sections
  • Reaches heygen.com

What it does

Trending Content Scout is an agent skill from Affitor/affiliate-skills. 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…

Its SKILL.md is about 5.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

It sits in Marketing & SEO, covering Social media marketing. It works with Reddit, TikTok and YouTube. The repository describes itself as: 50 AI agent skills for affiliate marketing. Research trending content, write data-backed posts, generate infographics, build landing pages, deploy — full flywheel with social… The licence is MIT.

When your agent uses it

  • The user wants to see what content is winning in a niche
  • Find viral content patterns
  • Research whats working on YouTube/TikTok/X/Reddit
  • Benchmark engagement

Example prompts

  • “what content is working for [topic]”
  • “show me top performing content about [keyword]”
  • “s trending in [niche]”
  • “/trending-content-scout”

Requirements

  • Compatibility (from SKILL.md): Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Determine Data Source
  2. Collect and Normalize Data
  3. Sort and Rank
  4. Analyze Patterns
  5. Calculate Engagement Benchmark
  6. Self-Validation

What it can do on your machine

Read from SKILL.md and the folder at commit e43bfae. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • heygen.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

    From compatibility in the SKILL.md frontmatter.

Context cost

Trending Content Scout loads about 5.4k tokens when it runs. Until then it costs about 208 tokens; SKILL.md has 1,416 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~208
When it runs · the whole SKILL.md, loaded when a task matches
~5.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Affitor/affiliate-skills at commit e43bfae, republished under its MIT licence (© Affitor). 1,416 words, ~5,384 tokens.

Download SKILL.mdSave it as .claude/skills/trending-content-scout/SKILL.md (or your agent's skills folder).
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".
compatibility
Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent
license
MIT
version
1.0.0
tags
affiliate-marketing, research, social-data, engagement, trending, content-intelligence
metadata.author
affitor
metadata.version
1.0
metadata.stage
S1-Research

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 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:

  1. Format gaps: If 90% of top content is reviews, comparisons are underserved
  2. Platform gaps: If YouTube is saturated but TikTok has few results → TikTok opportunity
  3. Angle gaps: Common user questions (visible in comments/replies) that no top content addresses
  4. Audience gaps: All content targets advanced users → beginner content is a gap
  5. Recency gaps: Top content is 6+ months old → fresh take on same topic is an opportunity
  6. 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:

yaml
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:

yaml
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

markdown
## 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
4. **[Gap 4]:** Top content is 4-6 months old — fresh 2024 take is an opportunity
5. **[Gap 5]:** Reddit has high engagement (avg score: 450) but no affiliate content → underserved platform

---

### 📏 Engagement Benchmark

| Metric | Median | Top 10% Threshold | Your Target |
|--------|--------|-------------------|-------------|
| Views | 12,000 | 85,000 | Beat median to start |
| Engagement Score | 18.5 | 45.0 | Aim for top 10% |

**Per Platform:**
| Platform | Median Views | Median Engagement |
|----------|-------------|-------------------|
| YouTube | 25,000 | 22.3 |
| TikTok | 45,000 | 16.8 |

---

### 🎯 Recommended Next Steps

Based on this data, the highest-opportunity path is:

1. **Run `content-angle-ranker`** — rank specific angles for [best platform]
2. **Create a [winning format]** using a [best hook] hook
   → Skill: `viral-post-writer` (format: [format], hook: [hook])
3. **Fill [Gap 1]** — this is the lowest-competition, highest-potential opportunity
   → Skill: `tiktok-script-writer` (if TikTok) or `comparison-post-writer` (if blog)
Show full SKILL.md (627 more words)Show less

Error Handling

  • No API configured: Fall back to web_search. Include note: "Data from web_search is approximate. For exact engagement metrics, configure an API provider — see shared/references/social-data-providers.md"
  • API rate limited: Fall back to web_search for remaining platforms. Note which platforms used API vs web_search in output.
  • No content found for keyword: The keyword may be too niche or too new.
    1. Try broadening: "[keyword]" → "[parent category]"
    2. Try removing platform filter: search across all platforms
    3. If still empty, this is itself a signal — could be a gap opportunity. Report: "No existing content found — this keyword is either too new or too niche. This could be a first-mover opportunity."
  • Platform blocked/unavailable: Skip that platform, continue with others. Note: "[Platform] was unavailable. Results are from [remaining platforms] only."
  • web_search returns only a few results: Present what's available. Note: "Limited data available ([X] results). Patterns may not be representative. Consider configuring an API for better coverage."
  • Engagement data partially available: If only views are available (no likes/comments), sort by views and estimate engagement. Note: "Engagement scores are estimated — only view counts were available."

Examples

Example 1: User: "What content is working about HeyGen on TikTok?" → keyword: "HeyGen", platforms: ["tiktok"] → web_search "HeyGen tiktok" + "HeyGen site:tiktok.com" + "HeyGen TikTok viral" → Find 15 TikTok videos, extract view counts and creators → Pattern: demo_first hooks dominate (60%), 30-45s duration optimal → Gap: nobody doing "HeyGen for [specific profession]" content → Recommend: tiktok-script-writer with demo_first hook, 30s, angle: "HeyGen for real estate agents"

Example 2: User: "I want to create content about email marketing tools. What's performing well?" → keyword: "email marketing tools", platforms: ["youtube", "tiktok", "reddit"] → Scout all 3 platforms → YouTube: dominated by listicles ("Top 10 email marketing tools 2024") — avg 45K views → TikTok: very few results — gap opportunity → Reddit: high engagement on comparison threads in r/emailmarketing → Recommend: Fill TikTok gap with comparison format, or target Reddit with authentic discussion

Example 3: User: "Scout trending content about AI writing tools, I have RapidAPI configured" → Use configured APIs for YouTube + TikTok → Get exact engagement data: views, likes, comments, shares → Engagement scores calculated precisely → Pattern: "I replaced my copywriter with AI" hook has 3x average engagement → Output includes exact benchmark: median 18K views, top 10% needs 120K+ → Recommend: content-angle-ranker to pick best angle, then viral-post-writer

Feedback & Issue Reporting

When this skill produces unexpected, incomplete, or incorrect output, generate a skill_feedback block (see shared/references/feedback-protocol.md for full schema).

Skill-specific failure modes:

  • Empty results on all platforms: Keyword too niche or all APIs/web_search returned nothing. Report as data_quality with the keyword used.
  • Engagement scores all zero: Metrics unavailable — only titles retrieved. Report as data_quality, note which platforms had no metrics.
  • Format/hook classification wrong: Agent classified a tutorial as a review. Report as wrong_output with the misclassified content.

Auto-detect triggers:

  • top_content array has <5 items after scanning all platforms
  • engagement_benchmark.sample_size < 10
  • 50% of content_gaps are generic rather than specific

Report issues: GitHub Issues | Discussions

References

  • shared/references/social-data-providers.md — API configuration and provider options
  • shared/references/flywheel-connections.md — master flywheel connection map
  • shared/references/affiliate-glossary.md — affiliate marketing terminology
  • shared/references/feedback-protocol.md — issue detection and reporting standard

Flywheel Connections

Feeds Into
  • content-angle-ranker (S1) — full scout output for angle scoring
  • viral-post-writer (S2) — pattern_analysis (winning formats, hooks, benchmark)
  • tiktok-script-writer (S2) — top TikTok content + engagement data
  • twitter-thread-writer (S2) — top X threads + engagement data
  • reddit-post-writer (S2) — top Reddit posts + engagement data
  • content-pillar-atomizer (S2) — platform performance data for allocation
  • competitor-spy (S1) — top_creators data (who's dominating this keyword)
  • keyword-cluster-architect (S3) — engagement data per keyword for cluster prioritization
  • affiliate-blog-builder (S3) — winning formats and gaps for blog content angles
Fed By
  • competitor-spy (S1) — competitor URLs/channels to analyze specifically
  • niche-opportunity-finder (S1) — niche keywords to scout
  • performance-report (S6) — your content metrics to compare against benchmark
Feedback Loop
  • S6 performance-report provides your actual content metrics → compare against engagement_benchmark from this skill → identify where you're beating or trailing the benchmark → refine content strategy in the next scout run
yaml
chain_metadata:
  skill_slug: "trending-content-scout"
  stage: "research"
  timestamp: string
  suggested_next:
    - "content-angle-ranker"
    - "viral-post-writer"
    - "tiktok-script-writer"

© Affitor, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/research/trending-content-scout of Affitor/affiliate-skills.

Open the folder on GitHubat commit e43bfae

Compare with similar skills

Trending Content Scout next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Trending Content Scout compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Trending Content Scout this skillAffitor/affiliate-skills700—~5.4kAutomated safety check: PassMIT
Social Listening Briefunifapi-agent/agents589—~2.7kAutomated safety check: PassMIT
Paid Ads AuditAgriciDaniel/claude-ads9.8k—~1.5kAutomated safety check: PassMIT
Influencer Discoverytigerless-labs/influencer-discovery212—~2.5kAutomated safety check: NotesNone
Comment MiningScrapeCreators/social-media-research-skills3.4k—~1kAutomated safety check: NotesMIT
Competitor Social ResearchScrapeCreators/social-media-research-skills3.4k—~1.1kAutomated safety check: NotesMIT

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    ScrapeCreators/social-media-research-skills

    A skill your agent uses when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a…

    3.4k GitHub stars~1.1k tokensUpdated 1 mo ago
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  • Finds a company's official website and social profiles from its name, or collects social links from a website URL, using BrowserAct templates run by a Python script.

    6.1k GitHub starsUsed in 1 repo~1.6k tokens
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Categories

Questions about Trending Content Scout

What does Trending Content Scout do?

Scan social platforms for top-performing content by engagement before you create anything. Trending Content Scout is an agent skill from Affitor/affiliate-skills. Scan social platforms for top-performing content by engagement before you create anything.

When should I use Trending Content Scout?

Trending Content Scout fits situations like: the user wants to see what content is winning in a niche; find viral content patterns; research whats working on YouTube/TikTok/X/Reddit; benchmark engagement.

How do I install Trending Content Scout in Claude Code?

Run `npx skills add Affitor/affiliate-skills --skill trending-content-scout -a claude-code`. Or copy the skill folder (skills/research/trending-content-scout in Affitor/affiliate-skills) into .claude/skills/trending-content-scout in your project. Claude Code loads it when a task matches its description.

How do I install Trending Content Scout in Codex?

Run `npx skills add Affitor/affiliate-skills --skill trending-content-scout -a codex`. Or copy the skill folder (skills/research/trending-content-scout in Affitor/affiliate-skills) into .agents/skills/trending-content-scout in your project. Codex loads it when a task matches its description.

Can I use Trending Content Scout in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Affitor/affiliate-skills --skill trending-content-scout -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/trending-content-scout, .gemini/skills/trending-content-scout, .github/skills/trending-content-scout and .opencode/skills/trending-content-scout in your project.

What does Trending Content Scout need to run?

SKILL.md names no scripts, command-line tools or credentials: Trending Content Scout is instructions for the agent only. Compatibility (from SKILL.md): Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent.

Does Trending Content Scout access the network?

SKILL.md names 1 domain. In commands or code: heygen.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Trending Content Scout safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Trending Content Scout use?

Trending Content Scout is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Trending Content Scout use?

About 5.4k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Trending Content Scout?

Skills that share tags, products or a category with Trending Content Scout: Social Listening Brief (unifapi-agent/agents, 589 stars), Paid Ads Audit (AgriciDaniel/claude-ads, 9.8k stars), Influencer Discovery (tigerless-labs/influencer-discovery, 212 stars) and Comment Mining (ScrapeCreators/social-media-research-skills, 3.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Trending Content Scout?

Affitor (a GitHub organization) maintains it in Affitor/affiliate-skills, which has 700 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on September 15, 2026.

Source: Affitor/affiliate-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.