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 trend-discoverygit 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/trend-discovery)<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/trend-discovery"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/trend-discovery/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/trend-discovery"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/trend-discovery.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 | $0.00051 | $0.00793 |
| Opus 5 | $0.00026 | $0.00396 |
| Sonnet 5 | $0.00010 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
trend-discovery 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.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trend Discovery
Overview
Find trends worth acting on. This skill is for social media trend research across TikTok, Instagram, YouTube, Reddit, Pinterest, and other public sources. The output should separate real evidence from vague "this is trending" claims.
When to Use
Use this skill when the user asks to:
- find trends in a niche or category
- discover trending TikTok sounds, hashtags, creators, or videos
- find trending Instagram Reels or YouTube Shorts ideas
- build a weekly trend brief
- decide what content formats or angles to jump on
Useful Sources
| Signal | Endpoint examples |
|---|---|
| TikTok trending feed | /v1/tiktok/get-trending-feed |
| TikTok popular hashtags/songs/videos/creators | /v1/tiktok/hashtags/popular, /v1/tiktok/songs/popular, /v1/tiktok/videos/popular, /v1/tiktok/creators/popular |
| TikTok search | /v1/tiktok/search/top, /v1/tiktok/search/keyword, /v1/tiktok/search/hashtag |
| Instagram trends/search | /v1/instagram/reels/trending, /v2/instagram/reels/search, /v1/instagram/search/hashtag |
| YouTube | /v1/youtube/shorts/trending, /v1/youtube/search, /v1/youtube/search/hashtag |
/v1/reddit/search, /v1/reddit/subreddit, /v1/reddit/subreddit/search |
|
/v1/pinterest/search |
Workflow
- Define niche, country/region, platform, and time sensitivity.
- Pull trend/discovery/search results from 2-4 relevant sources.
- Normalize evidence: post URL, creator, metric, date, topic, sound/hashtag if present.
- Cluster into trends by topic, format, hook, sound, meme, or audience problem.
- Rank trends by evidence strength and relevance, not raw views alone.
- Translate each trend into content ideas the user can actually make.
Output Format
# Trend Discovery Brief: {niche}
## Trends Worth Acting On
| Trend | Platforms | Evidence | Why it matters | Content angle |
|---|---|---|---|---|
## Sounds / Hashtags / Formats
- ...
## Example Posts to Study
- [title/hook](url) — why it matters
## Content Ideas
1. ...
2. ...
## Caveats
- Public data only.
- Trend evidence is directional unless repeated across sources.
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 · 90 lines · 51 tokens per session scan A b22f5d14d01b
trend-discovery is a skill published in the GitHub repository ScrapeCreators/social-media-research-skills (2,234 stars, last pushed 15d ago), licensed MIT. It adds 51 tokens to every session and 793 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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