
During February 2026, Pow38533 developed a Marketing Text Extraction Audit Feature for the adobe/spacecat-audit-worker repository, focusing on extracting marketing text from images and generating semantic HTML to enhance large language model consumption. The solution integrated with the Mystique agent, processed its responses, and mapped opportunity data to enable structured downstream insights. Pow38533 implemented the feature using JavaScript and Node.js, emphasizing robust API development and full stack practices. Comprehensive unit tests with real Mystique output fixtures ensured reliability and coverage. This work improved data accessibility for LLM prompts and established a scalable foundation for automated marketing text extraction and opportunity tracking.
February 2026 delivered a new Marketing Text Extraction Audit Feature for adobe/spacecat-audit-worker, enabling semantic HTML generation of text embedded in images for improved LLM consumption. The feature integrates with the Mystique agent, processes responses, and maps opportunity data for structured downstream insights. It includes comprehensive unit tests with real Mystique output fixtures to ensure reliability and coverage. Handlers were registered in index.js to complete the end-to-end workflow from image analysis to opportunity suggestions. This work strengthens data visibility from marketing imagery, enabling better prompts, decision support, and automated opportunities tracking, with solid collaboration across the team as evidenced by co-authored commits. Impact highlights include improved data accessibility for LLM prompts, a foundation for scalable marketing text extraction, and high-quality test coverage across the feature set.
February 2026 delivered a new Marketing Text Extraction Audit Feature for adobe/spacecat-audit-worker, enabling semantic HTML generation of text embedded in images for improved LLM consumption. The feature integrates with the Mystique agent, processes responses, and maps opportunity data for structured downstream insights. It includes comprehensive unit tests with real Mystique output fixtures to ensure reliability and coverage. Handlers were registered in index.js to complete the end-to-end workflow from image analysis to opportunity suggestions. This work strengthens data visibility from marketing imagery, enabling better prompts, decision support, and automated opportunities tracking, with solid collaboration across the team as evidenced by co-authored commits. Impact highlights include improved data accessibility for LLM prompts, a foundation for scalable marketing text extraction, and high-quality test coverage across the feature set.

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