What problem does it solve? Reverse-engineering a faceless-video channel's style often leads to guesswork or outright copying of protected creative assets. This Skill builds an evidence-grounded profile of a channel's reusable content, illustration, layout, camera, and motion grammar while strictly separating observed, inferred, and unknown claims and enforcing originality guardrails. ## Core Features & Use Cases - Evidence-grounded analysis: Records every claim as observed, inferred, or unknown in a validated evidence.jsonl ledger with source IDs, locators, and acyclic inference chains. - Temporal motion profiling: Analyzes sample videos frame-by-frame to capture camera, subject, environment, parallax, and interaction motion as abstract, transferable grammar. - Originality enforcement: Blocks copying of channel names, logos, thumbnails, scripts, transcripts, and voiceprints, and validates the workspace with scripts/channel_workspace.py. - Use Case: A creator researching a competitor's faceless explainer channel supplies the channel URL and two sample videos, then receives a validated channel-profile.json plus renderer-neutral motion and visual-style briefs to plan an original video. ## Quick Start Ask the AI to analyze a public faceless channel by providing its URL or screenshots and a target language, then have it initialize and validate the analysis workspace.
