Yt Audience Insights

YouTube Audience Insights

Turn a video's comment section into a read on your audience: who is showing up, what they keep asking, who your repeat fans are, which comments deserve a reply or a pin, and what is working in your niche right now.

One honest limit up front: YouTube keeps the list of who liked a video private (only the aggregate count is public). So the engagement signal here is commenters, not likers. On YouTube the comment section is the richer signal anyway: it carries the questions, the objections, and the content ideas.

When to use

• "Who commented on my video / analyze my comments"
• "What are people asking me / what should my next video be"
• "Who are my repeat viewers"
• "What is trending in [niche] on YouTube right now"

Not for drafting a community post (use yt-community-post-writer) or writing a title (use yt-title-optimizer).

Setup (free, optional)

The read layer uses the official YouTube Data API v3, which is free: enable it at https://console.cloud.google.com/apis/library/youtube.googleapis.com, create an API key, and set YOUTUBE_API_KEY. Free quota is 10,000 units/day (a comments or stats call is 1 unit) which covers ~100,000 comments a day.

No key yet? Paste the comments (or a CSV export) and the skill runs the same analysis on what you give it.

Input

• A video URL (or your channel, for a recent-videos sweep)
• Optional: the goal (content ideas / community management / competitor scan)

Output

1. Audience snapshot - commenter count, top comments by likes, reply-worthy questions
2. Themes - the recurring questions and topics, ranked (each is a next-video candidate)
3. Superfans - repeat commenters across your recent videos, worth a reply by name
4. Action list - which comments to reply to, which to pin, which to turn into a video
5. Niche pulse (optional) - what is trending in your category right now

Steps

1. Load the comments. Call lib.YouTubeClient().fetch_video_comments(video_url, max_results=100, order="relevance"). Falls back to pasted comments if no key.
2. Rank the top comments. Sort by like_count and reply_count. The high-like comments are what the room agrees with; the high-reply ones are where the conversation is.
3. Extract themes. Cluster the comments into recurring questions and topics. Each distinct question that shows up 3+ times is a content idea, name it as a potential title.
4. Find superfans. Pull comments across the channel's recent videos (fetch_channel_stats for the channel, then per-video comments) and flag authors who appear on multiple videos. These get a reply by name.
5. Read sentiment. Group into positive / question / critical. Critical-but-specific comments are the most useful; surface them, do not bury them.
6. Build the action list. For each: reply now (a real question with traction), pin (best social proof or a correction), or bank as a video idea. Route reply drafts through the relevant writer.
7. Optional niche pulse. fetch_trending(region_code, category_id) for what is currently most-popular in the category, as a format and topic scan.
8. Deliver the report in the Output shape above, with the raw top comments attached.

What the read layer exposes

Hard rules

Global voice rules: see root SKILL.md Voice rules. Additional skill-specific rules:

• Be honest that this reads commenters, not likers (likers are private on YouTube). Do not imply a full engager list.
• Never invent a comment, a count, or a theme. If the data is thin, say so.
• A theme is only a content idea if it actually recurs; do not promote a one-off comment to a trend.
• Respect the free quota: prefer mostPopular over search (search costs 100 units); batch comment pulls.

Related skills

• yt-community-post-writer - answer a recurring question as a community post
• yt-hook-scripter - turn a top question into a video hook
• yt-content-planner - feed the extracted themes into a content plan
• yt-channel-optimizer - fix the channel the new viewers will land on