
Developed a Marketing Text Extraction Audit Feature for the adobe/spacecat-audit-worker repository, enabling semantic HTML generation from marketing text embedded in images to enhance large language model consumption. Leveraged Node.js and JavaScript to integrate with the Mystique agent, process image analysis responses, and map opportunity data for structured insights. Registered new handlers in the application workflow to support end-to-end processing, from image input to opportunity suggestion output. Ensured reliability and maintainability by achieving 100% unit test coverage using real Mystique output fixtures. This work improved data accessibility for downstream LLM prompts and established a scalable foundation for marketing text extraction.
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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