YouTube Niche Validator
Validate YouTube niche viability using dual-slice sampling, hit-rate stratification, and concentration checks.
$16.99
Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
Muse
OpenClaw+21 moreClaude CodeCursorCodex CLIMuseOpenClaw
Manus
Grok
Grok Bot+18 agentsYouTube Niche Validator
Example session with this skill installed
Validate the YouTube niche "kids picture book read aloud" for a brand-new channel with no subscribers. Should I enter it?
- Read your context and instructions
- Compiled the youtube niche validator
Priors check (before scaling the pull)
- Caliber - the term returns long-form read-alouds, no Shorts. Type = video confirmed, proceed.
Extractability - publish date OK, channel follower count OK, comments EMPTY on every kids video. Control test: same call against a non-kids video returned comments, so collection works and the restriction is a platform rule.
3. Risk control - 3s delay between detail calls; 8 of 8 succeeded.
Step 1 - Dual-slice sampling (window: month, type: video)
- head slice (popularity): 150
- denominator slice (relevance): 168, above the 150-per-keyword floor
Step 2 - Enrichment
Exact publish dates and channel follower counts filled in. Resumable, no dropped rows.
Step 3 - Channel concentration (the step that changes the answer)
Top channel share of head slice: 73% (11 of 15 sampled)
-> MATRIX-DOMINATED. Hit rate recomputed deduplicated by channel.
Step 4 - Hit rate and size stratification
| Slice | Raw | Deduplicated |
|---|---|---|
| Overall | 31% | 12% |
| Large channels (100k+) | 44% | 18% |
| Small channels (<10k) | 9% | 4% |
Read the small-channel column, not the overall.
Step 5 - Verdict
KILL.
The raw 31% hit rate says "adopt," and it is wrong. 73% of the visible winners come from a single matrix operator; count each channel once and the rate falls to 12%. For a brand-new channel the only number that matters is the small-channel rate:
4%, far below the 15% kill line.
What would flip the call: a keyword in this space whose head slice is diffuse (top channel under 25%) and whose small-channel hit rate clears 15%.
Made-for-Kids fork (triggered)
- Comments are off, so demand mining moves to retailer reviews, book-community reviews, parenting forums, and competitor title/tag frequency.
- Ad RPM: expect $1-3, not $5-15. Pure ad revenue does not hold here; model on brand sponsorship, licensing, or off-platform product.
- Label honestly. Mislabeling carries a per-violation penalty ceiling of $53,088.
Illustrative: the figures above are placeholders this skill fills from your own pull. It does not invent numbers - when a field is unavailable it says so and names the decision that field blocks.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Standard YouTube research relies on survivorship bias, where you only see the top 20 viral hits and ignore the thousands of videos that died with zero views. This leads developers and creators to enter "open" niches that are actually saturated or dominated by a few monopoly accounts.
What it does
- Executes dual-slice sampling by comparing the "head slice" (popular winners) against the "relevance slice" (the true denominator of all recent uploads).
- Calculates a verified hit rate based on original qualifying videos rather than simple search results.
- Performs a channel-concentration check to detect matrix-account monopolies that occupy the top search results.
- Stratifies data by channel size to determine if a new, small channel can actually replicate the success of established giants.
- Applies a mandatory COPPA economics fork to adjust revenue and engagement expectations for kids-oriented niches.
Why this beats prompting it yourself
General AI prompts often hallucinate demand or fail to account for YouTube's specific channel-level concentration. This skill enforces a rigorous sampling design with a real denominator, ensuring you don't mistake a high-volume niche for a high-opportunity one.
Use cases
- Validating a new channel topic before investing in production resources.
- Auditing an existing niche to find out why your videos aren't breaking through despite high search volume.
- Comparing two candidate keywords to see which has a higher hit rate for small channels.
- Modeling revenue for kids-content niches while accounting for COPPA-induced RPM drops.
Known limitations
Requires approximately 150+ results per keyword for stable hit rates. Automated data pulls require a roughly 3-second delay to prevent request failure.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
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