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 competitor-social-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/competitor-social-research)<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/competitor-social-research"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/competitor-social-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/competitor-social-research"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/competitor-social-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 | $0.00049 | $0.00990 |
| Opus 5.5 | $0.00020 | $0.00396 |
| Sonnet 5.5 | $0.00010 | $0.00198 |
| Haiku 4.5 | $0.00005 | $0.00099 |
Grade A, and why
competitor-social-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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Social Research
Overview
Analyze what competitors are doing on social and what appears to be working. This skill combines profile data, recent posts, outlier analysis, transcripts, and optionally comments to produce a practical competitor brief.
When to Use
Use this skill when the user asks to:
- compare competitors on TikTok, Instagram, YouTube, LinkedIn, Facebook, X, Threads, or other social platforms
- find what content is working in a niche
- benchmark posting frequency, formats, topics, and engagement
- identify content gaps or opportunities
- build a social strategy from competitor research
Workflow
-
Define competitors and platforms
- Use provided handles/URLs.
- If only company names are provided, search profiles first and confirm likely matches when ambiguity matters.
-
Fetch profile snapshots
- Followers/subscribers
- Bio/positioning
- Links
- Verification/public metadata
-
Fetch recent content
- Pull comparable recent windows per competitor.
- Track source URLs, dates, captions, formats, and metrics.
-
Find outliers per competitor
- Use each account's own median baseline.
- Do not compare raw views between a huge brand and a small brand without context.
-
Analyze content strategy
- Content pillars
- Formats
- Hook styles
- Posting cadence
- Offers/CTAs
- Use of founder/creator personality
- Community/comment patterns
-
Find gaps and opportunities Look for:
- topics competitors avoid
- formats that overperform but few competitors use
- unanswered audience questions
- weak hooks or repetitive content
- platform whitespace
Useful ScrapeCreators Endpoints
Use the relevant profile/feed/detail/transcript/comment endpoints from scrapecreators-api. Common routes include:
- TikTok:
/v1/tiktok/profile,/v3/tiktok/profile/videos - Instagram:
/v1/instagram/profile,/v2/instagram/user/posts,/v1/instagram/user/reels - YouTube:
/v1/youtube/channel,/v1/youtube/channel-videos,/v1/youtube/channel/shorts - LinkedIn:
/v1/linkedin/company,/v1/linkedin/company/posts - Facebook:
/v1/facebook/profile,/v1/facebook/profile/posts,/v1/facebook/profile/reels - X/Twitter:
/v1/twitter/profile,/v1/twitter/user-tweets - Threads:
/v1/threads/profile,/v1/threads/user/posts - Bluesky:
/v1/bluesky/profile,/v1/bluesky/user/posts
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.
- 1mo ago First seen · 133 lines · 49 tokens per session scan A 3a5179df374d
competitor-social-research is a skill published in the GitHub repository ScrapeCreators/social-media-research-skills (4,948 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 990 once invoked, about $0.0002 per session on Opus 5.5. 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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