Outlier Post Finder logo

Outlier Post Finder

OrganizationPopular
ScrapeCreators
outlier-post-finder

Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.

Overview

PublisherScrapeCreators
Repositorysocial-media-research-skills
Skill nameoutlier-post-finder
Stars
2.5K
Forks
27
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ScrapeCreators on GitHub. Read the source before you install it.

Installation

Install the Outlier Post Finder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ScrapeCreators/social-media-research-skills.git /tmp/social-media-research-skills
mkdir -p .claude/skills
cp -r /tmp/social-media-research-skills/skills/outlier-post-finder .claude/skills/outlier-post-finder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Outlier Post Finder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Outlier Post Finder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Outlier Post Finder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Outlier Post Finder

Overview

Find social posts that beat an account's normal performance. The goal is not just to sort by views. The goal is to identify posts that performed unusually well for that creator or brand, then explain the repeatable patterns.

Use ScrapeCreators as the data layer. Pull recent public posts, normalize engagement metrics, calculate each account's baseline, and produce an outlier report with source URLs and practical takeaways.

When to Use

Use this skill when the user asks to:

  • find outlier posts, viral posts, top posts, best reels, best shorts, best TikToks, or best tweets
  • analyze why a creator's content is working
  • find competitor posts worth copying or learning from
  • build a swipe file from high-performing social posts
  • compare performance across a creator's recent posts

Do not use this for raw endpoint lookup only. Use scrapecreators-api for direct API routing.

Data Sources

Prefer the platform-specific feed endpoint, then enrich individual posts only when needed.

PlatformFeed endpointDetail/enrichment endpoint
TikTok/v3/tiktok/profile/videos/v2/tiktok/video, /v1/tiktok/video/transcript
Instagram posts/v2/instagram/user/posts/v1/instagram/post, /v2/instagram/media/transcript
Instagram reels/v1/instagram/user/reels/v1/instagram/post, /v2/instagram/media/transcript
YouTube videos/v1/youtube/channel-videos/v1/youtube/video, /v1/youtube/video/transcript
YouTube Shorts/v1/youtube/channel/shorts/v1/youtube/video, /v1/youtube/video/transcript
Facebook/v1/facebook/profile/posts, /v1/facebook/profile/reels/v1/facebook/post, /v1/facebook/post/transcript
LinkedIn/v1/linkedin/company/posts/v1/linkedin/post, /v1/linkedin/post/transcript
X/Twitter/v1/twitter/user-tweets/v1/twitter/tweet, /v1/twitter/tweet/transcript
Threads/v1/threads/user/posts/v1/threads/post
Bluesky/v1/bluesky/user/posts/v1/bluesky/post

Before calling an endpoint, fetch its docs or per-endpoint OpenAPI spec if parameter names or response fields are uncertain.

Workflow

  1. Clarify scope only if needed

    • Platform(s)
    • Handles or URLs
    • Time/post count window
    • Whether to include transcript/comment analysis
  2. Fetch recent posts

    • Pull at least 20 posts when available. More is better for baseline confidence.
    • Paginate if the endpoint supports cursors and the user wants a larger window.
    • Keep source URLs for citations.
  3. Normalize metrics

    • Capture whatever exists: views, plays, likes, comments, shares, reposts, saves.
    • Build a combined engagement score only after preserving raw metrics.
    • For video-first platforms, views/play count is usually the primary metric.
    • For text-first platforms, likes + replies/comments + reposts/shares is usually better.
  4. Calculate the account baseline

    • Use median instead of mean so one viral post does not distort the baseline.
    • Calculate per-platform and per-account baselines separately.
    • If mixed formats exist, split by format when possible: reel vs carousel, short vs long video, text vs video.
  5. Score outliers

    • view_lift = post_views / median_views
    • engagement_lift = post_engagement / median_engagement
    • Label posts as:
      • Huge outlier: 5x+ baseline
      • Strong outlier: 2x-5x baseline
      • Mild outlier: 1.5x-2x baseline
    • If sample size is under 10 posts, call confidence low.
  6. Enrich the winners

    • Fetch post details for top outliers.
    • Fetch transcripts for video posts when useful.
    • Optionally fetch comments to understand audience reaction.
  7. Explain why they worked Look for:

    • hook style
    • topic/category
    • format
    • emotional trigger
    • novelty/timeliness
    • creator proof or authority
    • controversy or debate
    • comments showing confusion, desire, or buying intent

Output Format

markdown
# Outlier Posts Report: {creator_or_brand}

## Summary
- Sample: {n} posts from {platforms}
- Window: {window}
- Baseline: median {primary_metric} = {value}
- Confidence: High/Medium/Low

## Biggest Outliers
| Rank | Post | Platform | Date | Primary Metric | Lift | Why it likely worked |
|---:|---|---|---|---:|---:|---|
| 1 | [title/hook](url) | TikTok | 2026-01-01 | 1.2M views | 8.4x | Contrarian hook + clear before/after |

## Repeatable Patterns
1. **Pattern name** — evidence and examples.
2. **Pattern name** — evidence and examples.

## Hooks to Steal
- "Exact hook from caption or transcript"
- "Exact hook from caption or transcript"

## Content Ideas Based on the Outliers
1. ...
2. ...

## Notes and Caveats
- Public data only.
- Small samples are directional, not definitive.

Common Pitfalls

  • Do not call the highest raw-view post the best outlier if a huge account is being compared with a small one. Use lift versus each account's own baseline.
  • Do not average TikTok, Instagram, YouTube, and LinkedIn metrics into one baseline. Score each platform separately.
  • Do not invent transcript quotes. Fetch transcripts or quote only visible captions/text.
  • Do not overstate confidence from fewer than 10 posts.
  • Do not ignore old viral posts if the user asked for recent performance. Respect the requested window.

Frequently asked questions

What does the Outlier Post Finder AI skill do?

Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.

Why use Outlier Post Finder on TypingMind?

Because you install it once and use it with any model. Outlier Post Finder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Outlier Post Finder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ScrapeCreators/social-media-research-skills/tree/main/skills/outlier-post-finder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Outlier Post Finder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Outlier Post Finder?

As many as you like. As long as a model supports skills, you can use Outlier Post Finder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Outlier Post Finder AI skill free?

Yes. It is published on GitHub by ScrapeCreators under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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