Structured prompts and workflows for AI vision analysis of video frames. Load this file only when the task involves using AI models to understand video frame co
Vision Analysis Prompts provide structured, detailed instructions for AI models to interpret video frames across various use cases such as content categorization, accessibility auditing, animation analysis, and UI component detection. This skill enables automated extraction of semantic insights and structured data from video content, helping marketers break down complex visual material into actionable chapters, evaluate accessibility compliance, and inventory UI elements efficiently.
The prompts guide AI to classify frames into categories like UI screens, presentations, or talking heads, analyze transitions with timing and CSS equivalents, and audit accessibility factors like color contrast and touch target size. This level of granular frame analysis supports informed decisions on video content strategy, compliance, and design optimization.
This skill is designed for performance marketers managing video ad campaigns who need precise content breakdowns to optimize messaging and targeting. Growth leads seeking to automate video content tagging and chaptering for better engagement metrics will also benefit. Additionally, agency strategists conducting accessibility audits or UI evaluations for client videos can leverage this skill to generate consistent, data-driven reports rapidly.
Practitioners typically begin by extracting key frames from video content at relevant intervals. They then run the content categorization prompts to label each frame with confidence levels and descriptive tags, enabling automated chapter generation that segments videos into meaningful parts. Next, accessibility auditing prompts assess UI screenshots within videos for compliance with standards like WCAG AA, flagging issues such as low contrast or insufficient touch target sizes. For animation-heavy videos, transition detection prompts analyze frame sequences to identify animation types, durations, and produce CSS equivalents, supporting detailed motion design reviews.
How accurate are the frame classifications? The confidence levels provided (high/medium/low) help gauge reliability but should be validated against domain knowledge. Can this skill handle multiple content types in one video? Yes, it supports multi-label classification and groups consecutive similar frames for chaptering. Is the accessibility audit compliant with recognized standards? The prompts check against WCAG AA criteria for contrast and sizing, offering specific fix recommendations aligned with best practices.
Attach this skill to a Metaflow agent task when you need AI-powered semantic analysis of video frames, such as content tagging or accessibility reviews. Once engaged, the agent will apply the structured prompts to extracted frames and return categorized, annotated outputs that support downstream workflows like chapter creation or design auditing. Expect detailed, confidence-scored classifications and actionable insights that integrate smoothly into your video marketing pipelines.
For broader context, see our roundup of claude skills marketing, and read common Claude Code content mistakes for related setup guidance.