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audience-research

audience-research

Use when the user wants to evaluate a creator, influencer, or brand audience using public profile signals, TikTok audience demographics, follower/following data, comments, geography, language, and content fit. Helps judge sponsorship and market fit.

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name: audience-research description: Use when the user wants to evaluate a creator, influencer, or brand audience using public profile signals, TikTok audience demographics, follower/following data, comments, geography, language, and content fit. Helps judge sponsorship and market fit. allowed-tools: Bash, Read, Write, WebFetch

version: 1.0.0 author: ScrapeCreators license: MIT homepage: https://scrapecreators.com repository: https://github.com/ScrapeCreators/social-media-research-skills metadata: openclaw: requires: env: - SCRAPECREATORS_API_KEY primaryEnv: SCRAPECREATORS_API_KEY homepage: https://scrapecreators.com tags: - social-media - research - scrapecreators

Audience Research

Overview

Evaluate whether a creator or social account reaches the right audience. This skill combines available public profile metrics, TikTok audience demographics, regional signals, follower/following data when available, comments, language, and content topics.

When to Use

Use this skill when the user asks to:

  • check if a creator's audience fits a market
  • compare audience fit across creators
  • find US-heavy, country-specific, or niche-specific creators
  • evaluate sponsorship/influencer opportunities
  • understand who appears to engage with an account

Useful Sources

  • /v1/tiktok/user/audience
  • /v1/tiktok/profile/region
  • profile endpoints across platforms
  • follower/following endpoints where available
  • comments on recent posts
  • link-in-bio pages and creator shops for niche signals

Workflow

  1. Pull profile and available audience/demographic data.
  2. Pull recent content and comments if audience intent matters.
  3. Extract region, language, niche, product/category, and community signals.
  4. Score audience fit against the user's target market.
  5. Label confidence based on the strength of public data.

Output Format

# Audience Research: {creator}

## Fit Summary
- Target market:
- Fit score: High/Medium/Low
- Confidence: High/Medium/Low

## Evidence
| Signal | Evidence | Source |
|---|---|---|

## Audience Notes
- Geography:
- Language:
- Niche/content fit:
- Comment quality:

## Sponsorship Recommendation
- Good fit / Maybe / Poor fit
- Why:

Common Pitfalls

  • Do not infer exact demographics from vibes. Use available evidence and label assumptions.
  • Do not overpromise audience details for platforms that do not expose them publicly.
  • Do not ignore mismatch between creator location and audience location.
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