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 influencer-prospectinggit 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/influencer-prospecting)<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/influencer-prospecting"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/influencer-prospecting/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/influencer-prospecting"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/influencer-prospecting.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 | $0.00054 | $0.00596 |
| Opus 5 | $0.00027 | $0.00298 |
| Sonnet 5 | $0.00011 | $0.00119 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
influencer-prospecting 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
Influencer Prospecting
Overview
Build practical creator prospect lists from public social data. The goal is to find accounts that fit the niche, have enough reach, and show evidence of relevant content or audience fit.
When to Use
Use this skill when the user asks to:
- find influencers or creators in a niche
- build a UGC, affiliate, sponsorship, or seeding list
- find micro-influencers with strong engagement
- identify creators who already talk about a product category
- export prospects as CSV
Useful Sources
- TikTok search users, popular creators, profile, profile videos, audience demographics
- Instagram search profiles, profile, posts, reels
- YouTube search and channel details
- Link-in-bio services: Linktree, Komi, Pillar, Linkbio, Linkme
- Amazon Shop and TikTok Shop showcase where relevant
Scoring Framework
Score each prospect 1-5 on:
- Niche fit — content clearly matches the category
- Audience fit — country/language/platform fit when public data supports it
- Reach — followers/subscribers and recent views
- Engagement quality — comments look real and relevant
- Brand safety — obvious controversy or mismatch risk
- Contactability — public links/email/contact path available
Output Format
# Influencer Prospect List: {niche}
| Creator | Platform | Followers | Recent views | Fit | Contact path | Notes |
|---|---|---:|---:|---:|---|---|
## Best Fits
1. ...
## Outreach Angles
- ...
## CSV
```csv
creator,platform,profile_url,followers,recent_views,fit_score,contact_path,notes
## Common Pitfalls
- Do not equate large follower count with good fit.
- Do not scrape or expose private contact data. Use public links only.
- Do not hide uncertainty when profiles have limited public data.
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 · 84 lines · 54 tokens per session scan A 670e840f96ef
influencer-prospecting is a skill published in the GitHub repository ScrapeCreators/social-media-research-skills (2,234 stars, last pushed 15d ago), licensed MIT. It adds 54 tokens to every session and 596 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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