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/viral-content-radar

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Finds and deconstructs recent high-performing social content, outliers, formats, hooks, audience reactions, and emerging opportunities. Use when the user asks what is trending, viral, overperforming, worth copying structurally, or likely to become a content opportunity.

  • 1 file
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  • MIT
  • Updated last week
  • GitHub

Use this Skill: https://skilld.dev/gh/alptugharun/ai-social-media-toolkit/viral-content-radar

This session only. Nothing lands on disk.

SKILL.md

≈73 tokens always: the name and description. ≈607 when used: this file.

Viral Content Radar

Find content patterns worth acting on.

The goal is not to return a random list of popular posts.

The goal is to identify repeatable mechanics behind recent high-performing content and translate them into original content tests.

Evidence Requirement

Use references/EVIDENCE-POLICY.md.

If web or platform data is unavailable, ask for URLs, screenshots, exports, or source data instead of inventing trends.

Research Scope

Capture when available:

  • Platform
  • Creator / brand
  • Post URL
  • Publish date
  • Format
  • Hook
  • Topic
  • Views / plays
  • Likes
  • Comments
  • Shares / reposts
  • Saves
  • Follower count or account baseline
  • Audience reaction
  • Visual pattern
  • CTA

Outlier Logic

Prefer performance relative to the creator's normal baseline.

When enough comparable posts exist:

outlier_lift = post_primary_metric / median_primary_metric

Suggested labels:

  • 5x+ baseline: huge outlier
  • 2x–5x: strong outlier
  • 1.5x–2x: mild outlier

These thresholds are heuristics, not universal truths.

Do not combine incomparable platform baselines.

Pattern Extraction

Look for:

  • Hook mechanism
  • Opening visual
  • Emotional trigger
  • Surprise / novelty
  • Proof
  • Transformation
  • Format
  • Editing rhythm
  • Comment trigger
  • Save trigger
  • Share trigger
  • Search intent
  • Timing
  • Production difficulty
  • Saturation

Opportunity Score

Use a transparent heuristic when ranking ideas:

  • 30% evidence strength
  • 25% audience fit
  • 20% freshness
  • 15% repeatability
  • 10% production ease

Apply a saturation penalty when the exact execution is already overused.

Always label this as a heuristic.

Output

Viral Content Radar: {niche}

Evidence Summary

  • Platforms:
  • Window:
  • Sample:
  • Confidence:

Biggest Outliers

Post Platform Signal Lift / evidence Repeatable mechanic

Patterns Worth Reusing

Pattern Evidence Why it worked Saturation risk

What Not to Copy

Identify unique wording, copyrighted creative, personal storytelling, or brand-specific elements that should not be duplicated.

Original Content Tests

For each test provide:

  • Hook
  • Format
  • Visual direction
  • Why now
  • Production difficulty
  • Success metric

Best Next Test

Choose the strongest evidence-backed experiment.

Core Principle

Copy the mechanic, not the creator's unique expression.

Source: SKILL.md on GitHub

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Signed by skilld at 4a79ca5. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

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Activeupdated last week
metadata
{
  "version": "0.1.0",
  "author": "Alptuğ Harun"
}

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