Change Details

=Profile Name: Supreet Kaur GitHub: https://github.com/Supreetkaur1 Location: Punjab, India (IST – UTC+5:30) Timezone: UTC+5:30 Availability: 40-45 hours per week (Full Time) Availability Time Slots: 11:30 am - 8:30 pm IST (UTC+5:30) I am a backend-focused software engineer with professional experience building scalable and production-grade systems. I have worked on backend services involving API design, text processing, structured data processing, and system reliability at scale. My work emphasizes correctness, maintainability, and performance under real-world constraints. During my professional software engineering experience , I worked on backend systems dealing with large volumes of structured data and cross-service workflows. This included handling data consistency issues, debugging production-level pipelines, and ensuring reliable user-facing outputs. # Synopsis The Lusophone Technological Wishlist is a community-driven initiative aimed at improving Wikimedia editing workflows for Portuguese-language contributors by prioritizing impactful technical enhancements. For this internship, I propose to work on two wishlist items: Wishlist #3: Automatic Duplicate Reference Detection in VisualEditor Wishlist #8: Wikidata Integration for WikiScore These two features together improve both the editing experience (VisualEditor) and contribution inclusivity (WikiScore) by reducing redundancy in citations and expanding scoring support to structured data contributions. My goal is to design robust, performance-aware solutions that integrate cleanly into existing Wikimedia systems while improving usability for contributors. Selected Wishlist I aim to work and complete both Wishlist items during the internship duration, here is a little more about the wishlist items Wishlist #3: Automatic Duplicate Reference Detection in VisualEditor Editors often unintentionally add duplicate references when citing sources using URLs, DOIs, or ISBNs. Since VisualEditor does not actively compare new references against existing ones in real time, duplicate citations are created, leading to cluttered and less maintainable articles. The goal is to detect duplicate references during citation insertion and guide users toward reusing existing references instead of creating new ones. Wishlist #8: Wikidata Integration for WikiScore WikiScore currently focuses primarily on Wikipedia edits and does not fully account for Wikidata contributions. This limits its usefulness in edit-a-thons where structured data contributions are important. The goal is to extend WikiScore to: Fetch Wikidata contributions via APIs Normalize and process structured data Integrate Wikidata edits into scoring logic alongside Wikipedia edits This will make WikiScore more inclusive and accurate for modern Wikimedia contribution workflows. ## Technical Approach 1. Wishlist #3 – VisualEditor Duplicate Reference Detection Understanding the System While exploring VisualEditor’s architecture, I studied how citation workflows are handled, particularly how tools like Citoid generate references and how existing references are stored in the document model. This helped identify where duplicate detection logic can be introduced without disrupting the existing flow. ## Implementation Approach 1. Reference Extraction Extract existing references from the VisualEditor document model Identify structured identifiers: URL DOI ISBN 2. Normalization Layer To ensure accurate comparison across formats: URLs → normalize protocol, remove trailing slashes, standardize domain format DOIs → strip prefixes (https://doi.org ), normalize casing ISBNs → remove hyphens and whitespace This ensures consistent identifier comparison. 3. Duplicate Detection Engine Maintain an in-memory indexed structure of normalized identifiers Perform O(1)-style lookup using hash-based comparison Compare new citation input against existing references in real time 4. Integration into Citation Workflow Hook into VisualEditor citation insertion flow Run duplicate detection before final reference creation Avoid blocking user actions; instead trigger suggestions 5. User Experience Handling Display non-intrusive notification when duplicate is detected Highlight existing reference in the reuse panel Provide a “reuse existing reference” action Key Challenges Handling inconsistent or partial identifiers Avoiding performance overhead in large articles Ensuring non-disruptive UI behavior inside VisualEditor 2. Wishlist #8 – Wikidata Integration for WikiScore System Understanding I analyzed WikiScore’s existing architecture to understand how Wikipedia contributions are fetched and processed. The goal is to extend this pipeline to support Wikidata as an additional structured data source. ## Implementation Approach 1. Data Fetching Layer Use MediaWiki Action API and Wikidata endpoints Fetch user contributions from Wikidata Handle pagination, rate limiting, and API reliability 2. Data Processing Pipeline Normalize contribution data into a unified schema Filter relevant actions: Item creation Statement additions Label/description edits Reference updates 3. Scoring System Integration Extend existing WikiScore scoring logic Assign weighted scores for different Wikidata actions Ensure consistency with Wikipedia contribution scoring 4. Performance Optimization Batch API requests to reduce load Introduce caching for repeated queries Optimize processing for large edit-a-thons ## Key Challenges Differences in structure between Wikipedia and Wikidata edits Handling API limitations and rate constraints Ensuring consistent scoring across platforms Timeline The internship runs from May 18, 2026 to August 17, 2026 (13 weeks). I will work full-time (40 hours/week) and adjust execution based on mentor feedback. Weeks 1–8: VisualEditor (Wishlist #3) Week 1–2: Setup & Codebase Understanding Set up MediaWiki + VisualEditor environment Study citation workflow and reference model Identify integration points for duplicate detection Week 3–4: Core Implementation Build normalization utilities (URL, DOI, ISBN) Implement duplicate detection engine Week 5–6: Integration Integrate detection into citation insertion flow Add reuse suggestion UI Week 7: Performance & Edge Cases Optimize for large articles Handle inconsistent metadata cases Week 8: Finalization Testing, bug fixes, documentation, and patch submission Weeks 9–13: WikiScore (Wishlist #8) Week 9–10: API Integration Implement Wikidata data fetching layer Normalize contribution data Week 11: Scoring Logic Extend WikiScore scoring system Integrate Wikidata contributions Week 12: Testing & Optimization Test with real edit-a-thon datasets Optimize performance and reliability Week 13: Finalization Documentation, cleanup, and final submission ## Why I Am a Good Fit My background as a backend engineer at Amazon has given me experience in building and maintaining production systems that handle structured data, API integrations, and performance-critical workflows. This project aligns directly with my experience in: Designing scalable backend systems Handling structured data pipelines Debugging and improving system reliability Working with API-driven architectures Additionally, I am comfortable working across both backend and integration layers, which is important for contributing to both VisualEditor and WikiScore. I am particularly motivated by systems that improve collaboration and data quality at scale, which aligns strongly with Wikimedia’s mission. Post-Internship Contribution After the internship, I plan to: Continue maintaining and improving implemented features Support new contributors in understanding the codebase Contribute further to Wikimedia tools focused on structured data and editor experience Stay active in the Wikimedia technical community

Profile Name: Supreet Kaur GitHub: https://github.com/Supreetkaur1 Location: Punjab, India (UTC+5:30) Availability: 40–45 hours/week (Full-time) Working Hours: 11:30 AM – 8:30 PM IST I am a Software Development Engineer with ~1 year of professional experience at Amazon, where I worked on backend systems involving API design, large-scale data processing, and reliability of user-facing pipelines. My work has involved handling structured and semi-structured data, debugging production issues, and ensuring consistency across distributed systems. Synopsis The Lusophone Technological Wishlist is a community-driven initiative aimed at improving Wikimedia editing workflows and contributor experience. This project focuses on two key areas: a. Wishlist #3: Improving the citation workflow in VisualEditor by detecting duplicate references and encouraging reuse b. Wishlist #8: Extending WikiScore to include Wikidata contributions, enabling better recognition of structured data work Together, these improvements enhance both editor efficiency and fair evaluation of contributions in modern Wikimedia workflows. I plan to prioritize Wishlist #8 (Wikidata integration for WikiScore) as my primary focus, and contribute to Wishlist #3 as a secondary goal depending on progress and mentor guidance. Understanding of Wishlist Items Wishlist #3: Duplicate Reference Detection in VisualEditor Editors often unintentionally add duplicate references due to variations in formats (URL, DOI, ISBN). Since VisualEditor does not actively compare references during insertion, this leads to redundant citations and reduced maintainability. The goal is to: a. Detect duplicate references during citation insertion b. Normalize identifiers for accurate comparison c. Suggest reuse of existing references in a non-intrusive way Wishlist #8: Wikidata Integration for WikiScore (Primary Focus) WikiScore currently focuses on Wikipedia edits and does not fully account for Wikidata contributions. This creates a gap in edit-a-thons and contests where structured data contributions are significant. The goal is to: a. Fetch Wikidata contributions using APIs b. Normalize and process structured edits c. Integrate them into WikiScore’s scoring system Technical Approach Wishlist #8 – Wikidata Integration for WikiScore (Primary) System Understanding I will begin by analyzing WikiScore’s current architecture to understand how Wikipedia contributions are fetched and scored, and identify extension points for integrating Wikidata. Data Fetching Layer a. Use the MediaWiki Action API (usercontribs) and Wikidata endpoints b. Fetch user contributions relevant to structured data edits c. Handle pagination, rate limits, and API reliability Data Processing Pipeline a. Normalize contributions into a unified schema b. Filter relevant actions such as: i. Item creation ii. Statement additions iii. Label/description edits iv. Reference updates Scoring Integration a. Extend existing scoring logic to include Wikidata edits b. Assign weights to different contribution types c. Ensure consistency with existing Wikipedia scoring Initial scoring will focus on clearly measurable actions, with weighting refined iteratively based on mentor feedback. Performance Considerations a. Batch API requests to reduce overhead b. Introduce caching for repeated queries c. Optimize processing for large-scale events Wishlist #3 – VisualEditor Duplicate Reference Detection (Secondary) Understanding the System While exploring VisualEditor’s architecture, I studied how citation workflows are handled (e.g., via tools like Citoid) and how references are represented in the document model. This helps identify safe integration points for duplicate detection. Reference Extraction & Normalization a. Extract references from the VisualEditor document model b. Normalize identifiers: i. URLs → standardize protocol, remove trailing slashes ii. DOIs → remove prefixes and normalize casing iii. ISBNs → remove formatting inconsistencies The initial implementation will focus on same-type matching (URL-URL, DOI-DOI, ISBN-ISBN), with cross-type matching explored later. Duplicate Detection Engine a. Maintain an in-memory index of normalized identifiers b. Use hash-based lookup for efficient comparison c. Detect duplicates in real time during citation insertion Integration & UX a. Integrate into the VisualEditor reference dialog (e.g., citation insertion flow) b. Provide non-intrusive feedback when duplicates are detected c. Suggest reuse of existing references instead of creating new ones Key Challenges a. Handling incomplete or inconsistent identifiers b. Maintaining performance in large articles c. Ensuring seamless integration without disrupting user workflows Timeline The internship runs from May 18, 2026 to August 17, 2026 (13 weeks). I will work full-time (~40 hours/week) and iterate based on mentor feedback. MVP Goal (Primary – Wishlist #8) Deliver a working integration that: a. Fetches Wikidata contributions b. Processes them into a structured format c. Integrates them into WikiScore with a basic scoring system Phase 1: Wikidata Integration for WikiScore (Weeks 1–8) Week 1–2: Setup & System Understanding a. Set up development environment b. Study WikiScore architecture and scoring flow c. Explore Wikidata APIs and contribution formats d. Validate approach with mentors Week 3–4: Data Fetching Layer a. Implement API integration for Wikidata contributions b. Handle pagination, rate limits, and failures c. Begin initial data normalization Week 5–6: Processing & Scoring Integration a. Normalize contribution data into a unified schema b. Implement scoring logic for key actions c. Integrate into WikiScore pipeline Week 7: Testing & Iteration a. Test with real-world datasets (edit-a-thons, multiple users) b. Handle edge cases (duplicates, reverted edits) c. Refine scoring logic Week 8: Stabilization & Documentation a. Optimize performance b. Clean up code and improve maintainability c. Document architecture and usage Phase 2: VisualEditor Duplicate Detection (Weeks 9–13) Week 9: Exploration & Design a. Study VisualEditor reference handling b. Identify integration points (e.g., reference dialog) Week 10–11: Core Implementation a. Implement normalization and duplicate detection logic b. Build in-memory indexing for references Week 12: Integration & UX a. Integrate into citation workflow b. Add non-intrusive duplicate suggestions Week 13: Testing & Finalization a. Test with complex articles b. Optimize performance c. Final documentation and submission Why I Am a Good Fit My experience aligns closely with the challenges of this project. At Amazon (Prime Video), I worked on backend systems involving subtitle processing, where I handled duplication issues, inconsistent formatting, and data integrity across large-scale text datasets. This directly relates to Wishlist #3, where similar challenges arise in detecting and managing duplicate references. Additionally, my work involved processing structured metadata and building API-driven workflows, which aligns with Wishlist #8’s requirement to fetch, process, and score structured Wikidata contributions. While my primary experience is in backend systems, I have collaborated closely with frontend teams, giving me a strong understanding of how backend logic impacts user-facing workflows—an important consideration when working with tools like VisualEditor. I aim to take a practical, iterative approach: focusing on delivering a correct and maintainable MVP first, and improving it based on feedback. Post-Internship Contribution After the internship, I plan to: a. Continue maintaining and improving implemented features b. Support new contributors in understanding the codebase c. Contribute further to Wikimedia tools related to structured data and editor experience d. Stay actively involved in the Wikimedia technical community

=Profile Name: Supreet Kaur GitHub: https://github.com/Supreetkaur1 Location: Punjab, India (IST – UTC+5:30) Timezone: UTC+5:30Availability: 40–45 hours/week (Full-time) Availability: 40-45 hours per week (Full Time)Working Hours: 11:30 AM – 8:30 PM IST Availability Time Slots: 11:30 am - 8:30 pm IST (UTC+5:30) I am a backend-focused software engineer with professional experience building scalable and production-grade systems. I have worked on backend services involving API design, text processing, structured data processing, and system reliability at scale. My work emphasizes correctnessI am a Software Development Engineer with ~1 year of professional experience at Amazon, maintainabilitywhere I worked on backend systems involving API design, and performance under real-world constraints. During mylarge-scale data professional software engineering experience cessing, I worked on backend systems dealing with large volumesand reliability of structured data and cross-service workflowsuser-facing pipelines. ThiMy work has includvolved handling data consistency issuesstructured and semi-structured data, debugging production-level pipelines issues, and ensuring reliable user-facing outputs. # Synopsis The Lusophone Technological Wishlist is a community-driven initiative aimed at improving Wikimedia editing workflows for Portuguese-language contributors by prioritizing impactful technical enhancementconsistency across distributed systems. For this internship, I propose to work on two wishlist items: Wishlist #3: Automatic Duplicate Reference Detection in VisualEditorSynopsis Wishlist #8: Wikidata Integration for WikiScore These two features together improve both the editing experience (VisualEditor) and contribution inclusivity (WikiScore) by reducing redundancy in citations and expanding scoring support to structured data contributions. My goal is to design robust, performance-aware solutions that integrate cleanly into existing Wikimedia systems while improving usability for contributors. Selected WishlistThe Lusophone Technological Wishlist is a community-driven initiative aimed at improving Wikimedia editing workflows and contributor experience. I aim to work and complete both Wishlist items during the internship duration, here is a little more about the wishlist items Wishlist #3: Automatic Duplicate Reference Detection in VisualEditor Editors often unintentionally add duplicate references when citing sources using URLs, DOIs, or ISBNs. Since VisualEditor does not actively compare new references against existing ones in real time, duplicate citations are created, leading to cluttered and less maintainable articles. The goal is to detect duplicate references during citation insertion and guide users toward reusing existing references instead of creating new ones. Wishlist #8: Wikidata Integration for WikiScore WikiScore currently focuses primarily on Wikipedia edits and does not fully account for Wikidata contributions. This limits its usefulness in edit-a-thons where structured data contributions are important. The goal is to extend WikiScore to: Fetch Wikidata contributions via APIsThis project focuses on two key areas: Normalize and process structured dataa. Wishlist #3: Improving the citation workflow in VisualEditor by detecting duplicate references and encouraging reuse Integrate Wikidata edits into scoring logic alongside Wikipedia edits This will make WikiScore more inclusive and accurate for modern Wikimedia contribution workflows. ## Technical Approachb. Wishlist #8: Extending WikiScore to include Wikidata contributions, enabling better recognition of structured data work 1.Together, Wishlist #3 – VisualEthese improvements enhance both editor Duplicate Reference Detectionefficiency and fair evaluation of contributions in modern Wikimedia workflows. Understanding the System While exploring VisualEditor’s architecture, I studied how citation workflows are handled, particularly how tools like Citoid generate references and how existing references are stored in the document model.I plan to prioritize Wishlist #8 (Wikidata integration for WikiScore) as my primary focus, This helped identify where duplicate detection logic can be introduced without disrupting the existing flowand contribute to Wishlist #3 as a secondary goal depending on progress and mentor guidance. ## Implementation ApproachUnderstanding of Wishlist Items 1.Wishlist #3: Duplicate Reference ExtraDetectionn in VisualEditor Extract existing references from the VisualEditor document modelditors often unintentionally add duplicate references due to variations in formats (URL, DOI, ISBN). Since VisualEditor does not actively compare references during insertion, this leads to redundant citations and reduced maintainability. Identify structured identifiers:The goal is to: URLa. Detect duplicate references during citation insertion DOIb. Normalize identifiers for accurate comparison ISBNc. Suggest reuse of existing references in a non-intrusive way 2. Normalization Layer To ensure accurate comparison across formats: URLs → normalize protocol, remove trailing slashes, standardize domain formatWishlist #8: Wikidata Integration for WikiScore (Primary Focus) DOIs → strip prefixes (https://doi.orgWikiScore currently focuses on Wikipedia edits and does not fully account for Wikidata contributions. This creates a gap in edit-a-thons and contests where structured data contributions are significant. ), normalize casingThe goal is to: ISBNs → remove hyphens and whitespace This ensures consistent identifier comparison.a. Fetch Wikidata contributions using APIs b. Normalize and process structured edits c. Integrate them into WikiScore’s scoring system 3. Duplicate Detection EngineTechnical Approach Maintain an in-memory indexed structure of normalized identifiersWishlist #8 – Wikidata Integration for WikiScore (Primary) Perform O(1)-style lookup using hash-based comparisonSystem Understanding Compare new citation input against existing references in real timeI will begin by analyzing WikiScore’s current architecture to understand how Wikipedia contributions are fetched and scored, and identify extension points for integrating Wikidata. 4. Integration into Citation WorkflowData Fetching Layer Hook into VisualEditor citation insertion flowa. Use the MediaWiki Action API (usercontribs) and Wikidata endpoints Run duplicate detecb. Fetch user contribution before final reference creations relevant to structured data edits Avoid blocking user actions;c. Handle pagination, rate limits, instead trigger suggestionsand API reliability 5. User Experience HandlingData Processing Pipeline Display non-intrusive notificaa. Normalize contribution when duplicate is detecteds into a unified schema Highlight existing reference in the reuse panelb. Filter relevant actions such as: Provide a “reuse existing reference” aci. Item creation Key Challengesii. Statement additions Handling inconsistent or partial identifiersiii. Label/description edits Avoiding performaiv. Reference overhead in large articlesupdates EnsuScoring non-disruptive UI behavior inside VisualEditorIntegration 2a. Wishlist #8 – Wikidata Integration forExtend existing scoring logic to include WikiScoredata edits System Understanding I analyzed WikiScore’s existing architecture to understand how Wikipedia contributions are fetched and processedb. The goal is to extend this pipeline to support Wikidata as an additional structured data source. ## ImplementaAssign weights to different contribution Approachtypes 1c. Data FetchEnsure consistency with existing LayerWikipedia scoring Use MediaWiki AInitial scoring will focus on clearly measurable action API and Wikidata endpointss, with weighting refined iteratively based on mentor feedback. Fetch user contributions from WikidataPerformance Considerations Handle pagination, rate limiting,a. andBatch API reliabilityquests to reduce overhead 2b. Data Processing PipelineIntroduce caching for repeated queries Normalc. Optimize contribution data into a unified schemaprocessing for large-scale events Wishlist #3 – VisualEditor Duplicate Reference Detection (Secondary) Filter relevant actions:Understanding the System Item creationWhile exploring VisualEditor’s architecture, I studied how citation workflows are handled (e.g., via tools like Citoid) and how references are represented in the document model. This helps identify safe integration points for duplicate detection. Statement addiReference Extraction & Normalizations Label/description editsa. Extract references from the VisualEditor document model Reference updatesb. Normalize identifiers: 3.i. URLs → standardize protocol, Scoring System Integrationremove trailing slashes Extend existing WikiScore scoring logicii. DOIs → remove prefixes and normalize casing Assign weighted scoresiii. ISBNs → remove for different Wikidata actionsmatting inconsistencies Ensure consistency with Wikipedia contribution scoringThe initial implementation will focus on same-type matching (URL-URL, DOI-DOI, ISBN-ISBN), with cross-type matching explored later. 4. Performance OptimizationDuplicate Detection Engine Batch API requests to reduce loada. Maintain an in-memory index of normalized identifiers Introduce cachingb. Use hash-based lookup for repeated queriesefficient comparison Optimize processing for large edit-a-thonsc. Detect duplicates in real time during citation insertion ## Key ChallengesIntegration & UX Differences in structure between Wikipedia and Wikidata editsa. Integrate into the VisualEditor reference dialog (e.g., citation insertion flow) Handling API limitations and rate constraintsb. Provide non-intrusive feedback when duplicates are detected Ensuring consistent scoring across platformsc. Suggest reuse of existing references instead of creating new ones Timeline The internship runs from May 18, 2026 to August 17, 2026 (13 weeks). I will work full-time (40 hours/week) and adjust execution based on mentor feedback. Weeks 1–8: VisualEditor (Wishlist #3) Week 1–2: Setup & Codebase Understanding Set up MediaWiki + VisualEditor environmentKey Challenges Study citation workflow and reference modela. Handling incomplete or inconsistent identifiers Identify integration points for duplicate detection Week 3–4: Core Implementation Build normalization utilities (URL, DOI,b. ISBN)Maintaining performance in large articles Implement duplicate detecc. Ensuring seamless integration engine Week 5–6: Integrationwithout disrupting user workflows Integrate detection into citation insertion flowTimeline Add reuse suggestion UI Week 7: Performance & Edge Cases Optimize for large articlesThe internship runs from May 18, 2026 to August 17, 2026 (13 weeks). I will work full-time (~40 hours/week) and iterate based on mentor feedback. Handle inconsistent metadata cases Week 8: Finalization Testing, bug fixes, documentation, and patch submissionMVP Goal (Primary – Wishlist #8) Weeks 9–13: WikiScore (Wishlist #8) Week 9–10: API IDeliver a working integration Implement Wikidata data fetching layern that: Normalize contribution a. Fetches Wikidata Week 11: Scoring Logic Extend WikiScore scoring system contributions Integrate Wikidata contributions Week 12: Testing & Optimization Test with real edit-a-thon datasetsb. Processes them into a structured format Optimize performance and reliability Week 13: Finalizationc. Integrates them into WikiScore with a basic scoring system Documentation, cleanup, and final submissionPhase 1: Wikidata Integration for WikiScore (Weeks 1–8) ## Why I Am a Good Fit My background as a backend engineer at Amazon has given me experience in building and maintaining production systems that handle structured data,Week 1–2: Setup & System Understanding a. Set up development environment b. Study WikiScore architecture and scoring flow c. Explore Wikidata APIs and contribution formats d. Validate approach with mentors Week 3–4: Data Fetching Layer a. Implement API integration for Wikidata contributions b. Handle pagination, rate limits, and failures c. Begin initial data normalization Week 5–6: Processing & Scoring Integration a. Normalize contribution data into a unified schema b. Implement scoring logic for key actions c. Integrate into WikiScore pipeline Week 7: Testing & Iteration a. Test with real-world datasets (edit-a-thons, multiple users) b. API integrationsHandle edge cases (duplicates, and performance-critical workflows. This project aligns directly with my experience in: Designreverted edits) c. Refine scoring scalable backend systemslogic Handling structured data pipelinesWeek 8: Stabilization & Documentation Debugging and improving system reliabilitya. Optimize performance Working with API-drivenb. Clean up code and improve maintainability c. Document architectures and usage Additionally,Phase 2: VisualEditor Duplicate Detection (Weeks 9–13) Week 9: Exploration & Design a. Study VisualEditor reference handling b. Identify integration points (e.g., reference dialog) Week 10–11: Core Implementation a. Implement normalization and duplicate detection logic b. Build in-memory indexing for references Week 12: Integration & UX a. Integrate into citation workflow b. Add non-intrusive duplicate suggestions Week 13: Testing & Finalization a. Test with complex articles b. I am comfortable working across both backend and integration layers,Optimize performance c. which is important for contributing to both VisualEditorFinal documentation and WikiScore.submission I am particularly motivated by systems that improve collaboration and data quality at scaleWhy I Am a Good Fit My experience aligns closely with the challenges of this project. At Amazon (Prime Video), I worked on backend systems involving subtitle processing, where I handled duplication issues, inconsistent formatting, and data integrity across large-scale text datasets. This directly relates to Wishlist #3, where similar challenges arise in detecting and managing duplicate references. Additionally, my work involved processing structured metadata and building API-driven workflows, which aligns with Wishlist #8’s requirement to fetch, process, and score structured Wikidata contributions. While my primary experience is in backend systems, I have collaborated closely with frontend teams, giving me a strong understanding of how backend logic impacts user-facing workflows—an important consideration when working with tools like VisualEditor. I aim to take a practical, iterative approach: focusing on delivering a correct and maintainable MVP first, which aligns strongly with Wikimedia’s missionand improving it based on feedback. Post-Internship Contribution After the internship, I plan to: Continue maintaining and improving implemented features a. Continue maintaining and improving implemented features b. Support new contributors in understanding the codebase c. Contribute further to Wikimedia tools focused onrelated to structured data and editor experience d. Stay activeively involved in the Wikimedia technical community