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name: comment-mining description: Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos. allowed-tools: Bash, Read, Write, WebFetch

version: 1.0.0 author: ScrapeCreators license: MIT homepage: https://scrapecreators.com repository: https://github.com/ScrapeCreators/social-media-research-skills metadata: openclaw: requires: env: - SCRAPECREATORS_API_KEY primaryEnv: SCRAPECREATORS_API_KEY homepage: https://scrapecreators.com tags: - social-media - research - scrapecreators

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

1. 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.

2. Clean lightly

   • Remove obvious spam/duplicates.
   • Keep slang, misspellings, and emotional wording if it is useful customer language.
   • Do not over-normalize exact quotes.

3. 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

4. Cluster themes

   • Group similar comments.
   • Score themes by frequency and intensity.
   • Highlight exact quotes for each theme.

5. 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

Output Format

# Comment Mining Report

## Summary
- Source(s): {urls}
- Comments analyzed: {count}
- Confidence: High/Medium/Low

## Top Themes
| Theme | Type | Frequency | Intensity | Representative quote |
|---|---|---:|---|---|

## Audience Questions
- "..."

## Objections and Concerns
- **Objection:** ...
  - Evidence: "..."
  - Response angle: ...

## Buying Intent / Demand Signals
- "..."

## Exact Language to Reuse
- "..."
- "..."

## Content Ideas From Comments
1. ...
2. ...

Quality Guardrails

• Label sample size and confidence.
• Separate one loud comment from a repeated pattern.
• Preserve exact quotes for useful language.
• Avoid claiming broad market sentiment from one post's comments.
• Call out moderation/platform bias when relevant.

Common Pitfalls

• Do not flatten comments into generic sentiment. The value is in questions, objections, and exact wording.
• Do not include personally identifying details unless they are already public and necessary.
• Do not treat bot/spam comments as audience signal.
• Do not skip Reddit post context. For Reddit, read both the original post and comments.