YouTube Comment Miner

Mine YouTube comments to extract content ideas, audience questions, pain points, and monetization signals.

Usage

/youtube-comment-miner https://youtube.com/watch?v=VIDEO_ID
/youtube-comment-miner @ChannelHandle --top 5
/youtube-comment-miner --topic "meditation for beginners" --top 10
/youtube-comment-miner VIDEO_ID1 VIDEO_ID2 VIDEO_ID3

Instructions

Step 1: Parse Arguments

Input mode (one of):

• Video URL(s) or ID(s): specific videos to mine
• Channel (@handle, URL, or ID) + --top N: mine the channel's top N videos by views (default: 5)
• Topic (--topic "keyword") + --top N: search for videos on the topic, mine the top N (default: 10)

Also:

• --max-comments N (optional): max comments per video (default: 100, max: 500)
• --scan-limit N (optional, channel mode): cap how many uploads get scanned (default: whole channel)

Step 2: Get the API Key

Check the user's Claude memory for a YouTube Data API v3 key. If not found, ask:

"I need a YouTube Data API v3 key to mine comments. You can get one from the Google Cloud Console. Please paste your key."

Step 3: Run the Bundled Script

Run scripts/mine_comments.py — resolve the path relative to this skill's own directory:

# Specific videos
YT_API_KEY=API_KEY python3 <skill-dir>/scripts/mine_comments.py --videos VIDEO_ID1 VIDEO_ID2 --max-comments 100

# Channel top videos
YT_API_KEY=API_KEY python3 <skill-dir>/scripts/mine_comments.py --channel "@handle" --top 5 --max-comments 100

# Topic search
YT_API_KEY=API_KEY python3 <skill-dir>/scripts/mine_comments.py --topic "topic keyword" --top 10 --max-comments 100

Dependency: pip3 install google-api-python-client.

The script fetches relevance-ordered top-level comments, tags each one into categories (question, contentrequest, painpoint, praise, criticism, suggestion, monetizationsignal, personalstory), flags "gold nuggets" (5+ likes on a question or request), and builds an audience-language word frequency list.

Channel mode note: the script finds top videos by paging the uploads playlist and ranking by actual view count, which is both cheaper and more accurate than search.list(order=viewCount) — that endpoint only ever sees a truncated slice of a channel.

Step 4: Read the Data

reports/data/comment-mine-<slug>-<YYYY-MM-DD>.json

Step 5: Write the Report

Write to the path the script printed:

reports/comment-mine-<slug>-<YYYY-MM-DD>.md

# Comment Mining Report: [Source]
*Analyzed [date] | [N] comments across [N] videos*

## Executive Summary
- 3-4 bullet points: Key findings from comment analysis
- What the audience wants, worries about, and loves

## Videos Analyzed
| # | Title | Views | Comments Mined |
|---|-------|-------|----------------|

## Content Requests (What Your Audience Wants)
Top requests ranked by likes. Include exact quotes.
- "Can you make a video about..." patterns
- Specific topics requested multiple times

## Frequently Asked Questions
Questions ranked by frequency/likes.
Group similar questions together.
These are potential video topics.

## Pain Points & Struggles
What viewers are struggling with.
Each pain point = potential video solving that problem.

## Audience Language Patterns
- Words and phrases viewers use repeatedly
- This is the language to mirror in titles, descriptions, thumbnails
- Common vocabulary table

## Praise Patterns (What Works)
What viewers love -- tells you what to do MORE of.

## Criticism Patterns (What to Fix)
What viewers complain about -- tells you what to avoid/improve.

## Gold Nugget Comments
High-engagement comments with questions or requests.
Each one is a validated content idea.

## Monetization Signals
Comments asking about courses, products, tools, etc.
Revenue diversification opportunities.

## Suggestions & Tips from Viewers
Viewer-suggested improvements and ideas.

## Actionable Content Ideas
5-8 specific video ideas derived from comment data:
- Idea title
- Source (which comments inspired it)
- Why it would work
- Priority (based on frequency/engagement)

## Quota Usage
| Operation | Calls | Units |
|-----------|-------|-------|
Use the `quota_used.breakdown` block from the JSON.

Step 6: Report Completion

Tell the user the report path, total comments mined, the top finding, the number of content ideas generated, and the quota consumed.

Quota Estimate

commentThreads.list costs 1 unit per 100 comments.

Channel mode previously cost ~107 units because it used search.list; the uploads-playlist route cuts that to ~26 and scans the whole channel instead of a partial slice. Daily default allowance is 10,000 units, resetting at midnight Pacific Time.