---
name: viral-content-radar
description: 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.
license: MIT
metadata:
  version: 0.1.0
  author: Alptuğ Harun
title: viral-content-radar
canonical_url: https://skilld.dev/gh/alptugharun/ai-social-media-toolkit/viral-content-radar
last_updated: 2026-09-30T14:41:01.000Z
---

> **Skill from skilld.dev.** Follow the instructions below for this session. You do not need to install anything.
>
> If the user asked to install this Skill, run `npx skilld install alptugharun/ai-social-media-toolkit/viral-content-radar`. Install writes the Skill files into the project, so every session loads them.

# 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.