Registry indexed
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.
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.
Source documentation, not instructions for this website. Review permissions before running any commands.
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.
Use this skill when the user asks to:
| 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 |
Fetch comments
Clean lightly
Classify each useful comment Use these buckets:
Cluster themes
Turn insights into actions Depending on the user's goal, produce:
# 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. ...
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---
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
```markdown
# 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.
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
80/100
Strong
Trust
60/100
Sandbox only
Audit
79/100
Needs review
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"documentation": "Usable metadata, review docs",
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"label": "No agent outcome data yet"
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"Financial research output is not financial advice; require human review before any live investment decision.",
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"High-risk permission hints: Shell or command execution, Secrets or environment access",
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"Financial research output is not financial advice; require human review before any live investment decision",
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"Audit: 79/100 Needs review",
"Safety: 39/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
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}
}Listing source
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