The bait, then the rug-pull.
A creator with 700 subscribers published a video about Claude context limits and watched it hit 120,000 views. The idea did not come from scrolling trends or hiring a strategist. It came from a single repeating pattern in the comment sections of competitor posts, surfaced automatically by a Claude Code skill he built himself.
Where the time goes.
01 · Results
Channel growth from zero to 10k subscribers in three months; three videos hitting 70k, 95k, and 120k views each traced back to the same skill.
02 · Demo: running /content-ideas live
Live walkthrough of the For You page: competitor posts scored by outlier rating, filtered by platform, and an Ideas tab with up to ten video starting points including hook and format recommendations.
03 · How it finds angles
The comment-mining mechanic: the skill reads comments on every analyzed post because that is where the question the video did not answer lives. The 120k-view context-limits video traced to a single comment trend.
04 · Anti-cannibalization
Before every run the skill scrapes the creator's own channel, builds a record of what has been covered, and prioritizes ideas the creator's own audience is explicitly requesting in comments.
05 · Setup
Install via Claude marketplace plugin or two GitHub commands; first-run setup asks for ScrapeCreators API key and content goals. Works in Claude Code, Claude Chat, Cursor, and Codex. 100 free ScrapeCreators credits; $50 for 25k credits (~160 runs).
06 · Self-improvement
Thumbs-up and thumbs-down buttons on every card feed Claude auto-memory. The skill reads all past reactions before each new run and builds a taste profile learning format preferences, topic angles, and voice over time.
07 · Download and CTA
Free download link in description (GitHub repo). Offer: AI strategy calls. Teaser for autopilot scheduling follow-up video.
Named ideas worth stealing.
Outlier scoring
Rank posts by how far they exceeded the creator's own channel average rather than by raw view count. This surfaces genuine breakouts, not just posts from large channels.
Comment-to-angle pipeline
Every comment is a signal of something the viewer cared about but the original video did not fully address. Aggregating comments across hundreds of posts lets patterns emerge as unserved topic angles.
Taste-learning feedback loop
Thumbs-up/down plus optional notes on each content card trains an AI memory that learns format preferences and topic angles over multiple sessions without manual re-configuration.
Lines you could clip.
"The comments are a gold mine because that's where you find the angle the video itself didn't cover."
"I got 10,000 subscribers for under $5 in API calls, so I'll make that trade."
"The real edge here is that it knows what you like, your taste, and your angles."
Things they pointed at.
Word for word.
Comment sections hold the angles the video never covered.
The information you need to make a video that breaks out is already public, sitting in the replies of videos that performed well but left something unanswered.
- Scoring posts by how far they beat a creator's own channel average is more reliable than raw view count for identifying what actually broke out versus what was just popular on a large account.
- Comments are audience demand signals: each one represents something a viewer cared enough to type, making them a more reliable indicator of unserved interest than any trend tool topic list.
- Tracking your own past content before generating new ideas ensures suggestions stay genuinely fresh rather than recycling territory you have already covered.
- A feedback loop that teaches a tool your taste through explicit reactions rather than re-explanation compounds over time so suggestions start to reflect your voice instead of generic AI outputs.
- A research process that runs automatically before your workday starts removes decision cost from the creative process and converts a weekly drain into a pre-scored morning inbox.








































































