Read your TikTok niche and audience from real data

TikTok Audience Insights

Turn real TikTok data into a read on what is working: which videos in your niche are exploding and why, what a competitor is posting, and what your commenters keep saying. The read layer sees actual play counts and comments instead of guessing.

One honest limit: TikTok keeps the list of who liked a video private (only counts). The engagement signal here is commenters + video performance, which is where the niche's questions and objections live.

When to use

• "What is trending in [niche] on TikTok / analyze this hashtag"
• "What is [competitor] posting / analyze their profile"
• "Read the comments on this video"
• "Which sounds and hooks are working right now"

Not for writing a script (use tt-hook-scripter) or a caption (use tt-caption-writer).

Setup (optional)

The read layer uses Apify (no login). Free token at https://console.apify.com/account/integrations, set APIFY_TOKEN. Video scanning is about $1.70 per 1,000, comments about $0.50 per 1,000. No token? Paste the videos or comments and the skill runs the same analysis.

Input

• A hashtag (niche scan), a profile (self or competitor), or a video URL (comments)
• Optional: the goal (trend scan / competitor read / community management)

Output

1. Niche pulse - top videos for the hashtag ranked by plays, the hooks and sounds behind the winners
2. Competitor read - a profile's recent videos ranked, the pattern in their hits
3. Comment read - what commenters keep asking, superfans, comments to reply to or pin
4. Action list - the hook/sound/format to try, who to engage, which comment to answer on camera

Steps

1. Pull the data. Niche: lib.ApifyClient().fetch_niche_videos(hashtag, max_items=20). Profile: fetch_profile_videos(username). Comments: fetch_video_comments(video_url). Falls back to pasted data if no token.
2. Rank by performance. Sort by plays, then engagement rate (likes+comments over plays). Normalize against the author's follower count so a small creator's breakout is not buried under a big account.
3. Extract the pattern. For the top videos, name the shared hook (first-second shape), the sound, the format (talking-head, text-overlay, skit), and the length. That is the repeatable part.
4. Note the sounds. Recurring sounds across the top videos are a trend to ride; flag them for tt-trend-mapper.
5. Read the comments. Cluster into questions, agreement, pushback. Recurring questions become video ideas (answer on camera); pinned/high-like comments show what the room cares about.
6. Build the action list. Try-this (winning hook + sound), engage (specific commenters), answer-on-camera (a recurring question). Route to tt-hook-scripter / tt-caption-writer.
7. Deliver the report in the Output shape, with the raw ranked videos attached.

What the read layer exposes

Method	Returns
fetch_niche_videos(hashtag, max_items)	top videos for a hashtag: text, plays, likes, comments, shares, author fans, sound
fetch_profile_videos(username, max_items)	a profile's recent videos with the same fields
fetch_video_comments(video_url, max_items)	comments (the engagement signal, since likers are private)

Hard rules

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

• Be honest that this reads commenters, not likers (TikTok keeps likers private). Do not imply a full liker roster.
• Normalize by follower count before calling a video a winner, or you extract "big account" effects, not "good video" effects.
• Never invent a video, a count, a sound, or a comment. If the data is thin, say so.
• A pattern (hook or sound) is only a trend if it recurs across several top videos.

Related skills

• tt-hook-scripter - script a video around the winning hook the data found
• tt-trend-mapper - ride a sound or format the scan surfaced
• tt-caption-writer - caption it
• tt-content-planner - feed the winning patterns into a plan