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 comment-mininggit 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/comment-mining)<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/comment-mining"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/comment-mining/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/comment-mining"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/comment-mining.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 | $0.00048 | $0.00949 |
| Opus 5.5 | $0.00019 | $0.00380 |
| Sonnet 5.5 | $0.00010 | $0.00190 |
| Haiku 4.5 | $0.00005 | $0.00095 |
Grade A, and why
comment-mining 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comment Mining
Overview
Mine public comments for what people actually ask, complain about, want, misunderstand, or repeat. The output should help with product research, content ideas, copywriting, objection handling, and audience understanding.
When to Use
Use this skill when the user asks to:
- analyze comments on a TikTok, YouTube video, Instagram Reel, Facebook post, Reddit post, or Rumble video
- find audience questions, objections, complaints, or buying intent
- extract voice-of-customer language
- find content ideas from comments
- understand sentiment around a post, creator, product, or topic
Comment Sources
| Platform | Endpoint |
|---|---|
| TikTok comments | /v1/tiktok/video/comments |
| TikTok replies | /v1/tiktok/video/comment/replies |
| YouTube comments | /v1/youtube/video/comments |
| YouTube replies | /v1/youtube/video/comment/replies |
| Instagram comments | /v2/instagram/post/comments |
| Facebook comments | /v1/facebook/post/comments |
| Facebook replies | /v1/facebook/post/comment/replies |
| Reddit comments | /v1/reddit/post/comments |
| Rumble comments | /v1/rumble/video/comments |
Workflow
-
Fetch comments
- Use the post/video URL whenever possible.
- Paginate when the endpoint supports it and the user wants depth.
- Preserve comment text, author if public, like/upvote count, timestamp, and source URL.
-
Clean lightly
- Remove obvious spam/duplicates.
- Keep slang, misspellings, and emotional wording if it is useful customer language.
- Do not over-normalize exact quotes.
-
Classify each useful comment Use these buckets:
- questions
- objections
- complaints/pain points
- praise
- confusion
- requests/feature ideas
- buying intent
- controversy/debate
- jokes/memes/culture signals
-
Cluster themes
- Group similar comments.
- Score themes by frequency and intensity.
- Highlight exact quotes for each theme.
-
Turn insights into actions Depending on the user's goal, produce:
- content ideas
- FAQ ideas
- landing page copy angles
- product ideas
- objection-handling bullets
- sales/support notes
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 · 140 lines · 48 tokens per session scan A 8fb6527e7e73
comment-mining is a skill published in the GitHub repository ScrapeCreators/social-media-research-skills (4,948 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 949 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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