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.

科研3.3kskills/comment-mining/SKILL.md

Install

Send this to Claude Code, Codex or Cursor. The agent checks the Skill for safety first and installs it only after you confirm.

读取 https://funcoding.ai/skills/scrapecreators/social-media-research-skills/comment-mining/install.md ，按里面的步骤帮我安装这个 Skill。

SKILL.md

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.

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