Social Media Research Skills is a collection of workflows that let AI coding agents research public social-media data across platforms such as TikTok, Instagram, YouTube, Reddit, and LinkedIn. Marketers and researchers use it to find unusually successful posts, mine comments, study competitors, analyze ads, and extract trends into business outputs. The catalogue skills and plugin package these workflows for supported AI agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ScrapeCreators/social-media-research-skills --skill audience-researchgit clone --depth 1 https://github.com/ScrapeCreators/social-media-research-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/scrapecreators/social-media-research-skills/audience-research)<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/audience-research"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/audience-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/audience-research"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/audience-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.00564 |
| Opus 5 | $0.00026 | $0.00282 |
| Sonnet 5 | $0.00011 | $0.00113 |
| Haiku 4.5 | $0.00005 | $0.00056 |
Grade A, and why
audience-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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
- Pull profile and available audience/demographic data.
- Pull recent content and comments if audience intent matters.
- Extract region, language, niche, product/category, and community signals.
- Score audience fit against the user's target market.
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 87 lines · 53 tokens per session scan A 3ced7d3268b8
audience-research is a skill published in the GitHub repository ScrapeCreators/social-media-research-skills (2,234 stars, last pushed 15d ago), licensed MIT. It adds 53 tokens to every session and 564 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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