Faceless YouTube Policy Risk Checker
Rate a planned faceless video format against YouTube inauthentic-content monetization policy and get prioritized fixes. Faceless YouTube Policy Risk Checker takes your planned video format (narration, cuts, subtitles, material source, template reuse, AI use) and rates it against YouTube's "inauthentic content" (formerly "repetitious content") monetization policy before you produce a batch of videos.
Example input
[HAND-WRITTEN FICTIONAL SAMPLE - invented for testing.] I'm planning a faceless English-language channel called "60-Second Money Moves": daily Shorts plus one 8-minute long-form per week, about personal finance news and tips. Plan: 3 Shorts a day and 1 long-form a week, mostly automated by a script pipeline. Narration: one AI text-to-speech voice for every upload, same pace and tone, no human voice. Visuals: a library of about 150 stock clips (offices, coins, phone screens) that the pipeline picks from by keyword; roughly 5 cuts per minute, mostly slow zooms. Some b-roll will be AI-generated images that look like realistic news scenes (for example a crowded bank branch). Subtitles: burned-in captions of exactly what the narrator says, no text-only slides. Template: every video has the same structure: 3-second hook line, "Here are 3 things", three bullet points, "Subscribe for more". Same intro sting and background music every time. Topics change but the skeleton, layout and music do not. Script: an LLM summarizes one finance news article per video; I do a quick spot check on a few of them but no rewriting and no added commentary of my own. …
Example output
> **Not legal advice.** This is an interpretation of YouTube's platform policy, not legal advice, and not a statement from YouTube. YouTube alone decides eligibility. A LOW rating does **not** guarantee monetization approval, and a HIGH rating does not guarantee rejection. Policies change. Policy text re-verified on 2026-10-18 from https://support.google.com/youtube/answer/1311392 and https://support.google.com/youtube/answer/14328491. ## Summary - **Overall rating: HIGH.** Driven by factors 1, 2, 3, 4 and 6 (five HIGH ratings, each of which alone triggers HIGH under this Skill's rules). The strongest driver is template reuse combined with an unedited, single-source LLM script and one identical synthetic voice, at 3 Shorts per day. - **Viewer test:** Yes. Someone watching several consecutive uploads would likely feel they are the same video with the topic swapped, because the hook, "3 things" skeleton, voice, music, intro sting and stock footage are identical. - Fetched policy text (retrieved 2026-10-18): the Help page, still headed "Generic or Repetitive Content", says it was renamed from "repetitious content" to "inauthentic content" on July 15, 2025, and describes content that "looks like it's made with a template" and that could feel repetitive to viewers watching several videos in a row. Its non-exhaustive examples include "AI-generated content made with generic or unoriginal templates giving the impression of mass production." The page's text matches this Skill's checklist; no adjustment was needed. - AI use is not itself the problem in the fetched text; the issue is mass-produced, templated output with little added value. ## Factor ratings | Factor | Rating | Reason | |---|---|---| | 1. Voice-over uniqueness | HIGH | "One AI text-to-speech voice for every upload, same pace and tone", reading text an LLM summarized from an article: identical delivery every upload. | | 2. Visual variety / cut count | HIGH | "A library of about 150 stock clips" picked by keyword, "roughly 5 cuts per minute, mostly slow zooms": limited shots, repeated footage across uploads, little motion beyond zooms. | | 3. Template reuse across uploads | HIGH | "Same structure: 3-second hook line, 'Here are 3 things', three bullet points, 'Subscribe for more'", same sting and music, "mostly automated", 3 Shorts a day: videos differ mainly by topic nouns, like filling a form. This is close to the fetched page's template language. | …
Real output from running the skill on a sample problem, shortened. The skill file itself is not published here.