YouTube Inauthentic Content Checker
Review one script for signals that resemble examples in YouTube's public inauthentic-content policy. Free, no signup.
This is an independent heuristic tool and is not affiliated with YouTube. It cannot reproduce YouTube's channel review or decide monetization eligibility. Scores are internal heuristic prompts.
What is YouTube's inauthentic content policy?
YouTube's inauthentic content policy is part of the YouTube Partner Program monetization rules. Its official examples focus on repetitive or mass-produced content, template-like videos, and work that lacks meaningful original commentary or value. Read the current YouTube channel monetization policy for the complete, changing list.
The policy page says its July 15, 2025 update renamed and clarified the existing repetitious-content policy to cover repetitive or mass-produced content. It also says this content was already ineligible under the existing rules and that reused-content policy did not change.
A script-only checker cannot decide whether a channel qualifies. A YouTube review can consider the channel's theme, videos, metadata, and About section; this tool sees only the text you paste.
What changed in July 2025 (and what didn't)
YouTube describes the July 2025 change as a minor clarification and rename, not a new restriction. The current policy page remains the source of truth for examples and review scope.
Three common misconceptions worth killing right now:
- Reaction videos are still allowed. YouTube lists reaction videos with commentary among examples that can be monetized under its reused-content policy. Other policies, including copyright, still apply.
- AI voiceover is not banned. YouTube's altered-content guidance lists cloning your own voice for voiceovers among production uses that generally do not require disclosure. That does not decide monetization eligibility.
- Reused content and inauthentic content are different policies. YouTube describes reused content as republishing material without significant original commentary, modification, or value. It describes inauthentic content as repetitive or mass-produced.
What this checker can and cannot tell you
The score is an internal rubric over script signals. It can point to repetition, generic scaffolding, missing evidence, and similar text patterns worth checking against the official policy.
It cannot see the finished video, the rest of the channel, ownership rights, production process, or YouTube's internal systems and human review. A low score is not approval; a high score is not a prediction of demonetization.
Use the output as a checklist, then make the decision from YouTube's live policy and the complete channel context.
How we score your script
Our checker runs 16 internal signals across three categories. They are review prompts informed by YouTube's public examples. They do not reproduce YouTube's internal detection systems or eligibility review.
- Template reuse. Repeated sentence scaffolds or identical paragraph structures.
- Hook recycling. Opening lines that match common clickbait templates.
- Filler density. Percentage of the script that carries no information.
- List-dump pacing. Long runs of bullet-style beats with no transitions.
- Outro boilerplate. Stock calls to subscribe with no tie-back to the content.
- Syntax monotony. Low variance in sentence length and sentence openings.
- LLM phrase markers. Hedging patterns common in generic AI prose.
- Stock transitions. Overuse of phrases like "but here is the thing" or "let me explain."
- Voice flatness. Absence of first-person opinion, anecdote, or judgment.
- Numeric precision drift. Vague figures where a human would cite a source.
- Topic thinness. Surface coverage of a topic with no argument or angle.
- Evidence gap. Claims without examples, sources, or reasoning.
- Editorial absence. No clear point of view, stake, or personal framing.
- Context collapse. Loss of connection between paragraphs and the premise.
- Genre mismatch. Tone and structure that fit a farm template, not the topic.
- Novelty signal. Presence (or absence) of original research or framing.
The asymmetric scoring rule
The 0-to-100 result is PrePublish's internal rubric, not a score supplied or calibrated by YouTube. Deterministic text checks provide the initial signals, and a language-model pass adds script context and suggested revisions. Read the evidence behind each signal rather than treating the number as an eligibility threshold.
Frequently Asked Questions
Want the full picture?
This tool scores one script for inauthentic-content risk. PrePublish also maps script-level attention risks, fixes pacing, and rewrites your hook. All in one place.