adobe/spacecat-audit-worker
Nov 2025 – Jan 2026
3 Months active
Languages Used
JavaScriptJSONSQL
Technical Skills
AI integrationCheerioJavaScriptNode.jsbackend developmentfront end development
Alberto Di Cagno
PROFILE
Alberto Di Cagno
Over a three-month period, Andrea Di Cagno developed and enhanced audit and analytics features for the adobe/spacecat-audit-worker repository, focusing on readability analysis, metadata optimization, and data reliability. Andrea implemented robust CSS selector generation and improved text extraction using JavaScript and Cheerio, enabling more accurate content audits. By integrating AWS Athena and SQL, Andrea streamlined data retrieval and introduced fallback mechanisms for agentic URL sourcing. The work included refining audit data models, normalizing URL handling, and expanding test coverage to enforce quality gates. These efforts resulted in deeper audit accuracy, improved onboarding, and more actionable analytics for downstream content quality assessments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
11
Lines of code
8,835
Activity Months3
Your Network
479 peopleSame Organization
@adobe.com425
Work History
January 2026
8 Commits • 6 Features
Jan 1, 2026January 2026 — Spacecat Audit Worker achieved significant reliability and analytics improvements. Key features delivered include robust CSS selector generation for auto-optimization, enhanced readability analysis with non-content tag exclusion and analytics enablement, top agentic URLs retrieval with Ahrefs fallback, audit data model simplification by removing the isLLMO flag, and URL handling/normalization improvements. Major bug fixes address URL resolution/redirect prevention, URL format normalization with base URL fallback, and robustness of readability analysis. Overall impact: improved audit accuracy, onboarding experience, and customer analytics capabilities. Technologies demonstrated: advanced CSS/URL handling, HTML content analysis, data-model refactoring, and resilient data retrieval.
8 Commits • 6 Features
Jan 1, 2026January 2026 — Spacecat Audit Worker achieved significant reliability and analytics improvements. Key features delivered include robust CSS selector generation for auto-optimization, enhanced readability analysis with non-content tag exclusion and analytics enablement, top agentic URLs retrieval with Ahrefs fallback, audit data model simplification by removing the isLLMO flag, and URL handling/normalization improvements. Major bug fixes address URL resolution/redirect prevention, URL format normalization with base URL fallback, and robustness of readability analysis. Overall impact: improved audit accuracy, onboarding experience, and customer analytics capabilities. Technologies demonstrated: advanced CSS/URL handling, HTML content analysis, data-model refactoring, and resilient data retrieval.
January 2026
December 2025
4 Commits • 3 Features
Dec 1, 2025December 2025: Focused on reliability, data quality, and quality gates for adobe/spacecat-audit-worker. Delivered three key enhancements that strengthen the scraping pipeline, broaden text extraction, and enforce testing standards, delivering more robust audit results and faster time-to-value for downstream analytics.
December 2025
4 Commits • 3 Features
Dec 1, 2025December 2025: Focused on reliability, data quality, and quality gates for adobe/spacecat-audit-worker. Delivered three key enhancements that strengthen the scraping pipeline, broaden text extraction, and enforce testing standards, delivering more robust audit results and faster time-to-value for downstream analytics.
November 2025
4 Commits • 2 Features
Nov 1, 2025November 2025 (2025-11) focused on delivering robust improvements to readability auditing and metadata optimization in adobe/spacecat-audit-worker, with a clear emphasis on business value and data quality. Key features delivered include Enhanced Readability Analysis with targeted item selector generation, improved text extraction using Cheerio, and a noop URL resolver to stabilize readability audits. In parallel, Metadata Auto-Optimization was introduced to prune invalid suggestions and enrich valid ones with necessary fields, aligning with the data structures used for LLM-driven opportunities. These efforts collectively improved audit accuracy, reduced noise, and accelerated downstream decision-making for content quality assessments.
4 Commits • 2 Features
Nov 1, 2025November 2025 (2025-11) focused on delivering robust improvements to readability auditing and metadata optimization in adobe/spacecat-audit-worker, with a clear emphasis on business value and data quality. Key features delivered include Enhanced Readability Analysis with targeted item selector generation, improved text extraction using Cheerio, and a noop URL resolver to stabilize readability audits. In parallel, Metadata Auto-Optimization was introduced to prune invalid suggestions and enrich valid ones with necessary fields, aligning with the data structures used for LLM-driven opportunities. These efforts collectively improved audit accuracy, reduced noise, and accelerated downstream decision-making for content quality assessments.
November 2025
Activity
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Quality Metrics
Correctness93.8%
Maintainability88.8%
Architecture88.8%
Performance87.6%
AI Usage26.4%
Skills & Technologies
Programming Languages
JSONJavaScriptSQL
Technical Skills
AI integrationAPI integrationAWSAWS AthenaCheerioJavaScriptNode.jsSQLaccessibility checksaudit functionalitybackend developmentcode quality assuranceconfiguration managementdata retrievaldata scraping
Repositories Contributed To
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