
comment-mining
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.
- Installs
- 1.3K
- Last 24h
- +23
- Trending rank
- #5303
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/scrapecreators/social-media-research-skills --skill comment-mining- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads comment-mining automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
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.
FAQ
What does the comment-mining skill do?
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.
How do I install comment-mining?
Run npx skills add https://github.com/scrapecreators/social-media-research-skills --skill comment-mining in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/comment-mining/ (personal) or .claude/skills/comment-mining/ (project).
Which AI agents can use comment-mining?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is comment-mining safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains comment-mining?
It is published by scrapecreators in scrapecreators/social-media-research-skills. It has 1,285 recorded installs.
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