unb-mds/2025-1-NoFluxoUNB
Apr 2025 – Jul 2025
4 Months active
Languages Used
CSVMarkdownPythonJavaScriptTypeScriptJSONPlain TextText
Technical Skills
Agile MethodologiesCSV ParsingCode OrganizationData CleaningData CollectionData Engineering
staan
PROFILE
Staan
Gustavo Chaves developed and maintained core data engineering and AI integration features for the unb-mds/2025-1-NoFluxoUNB repository over four months. He built end-to-end pipelines for university course data scraping, PDF transcript extraction, and AI-assisted class search, using Python, Flask, and Streamlit. His work included migrating an AI agent to a Flask API, implementing robust error handling, and refactoring code for maintainability. Gustavo focused on automating data ingestion, normalizing outputs, and improving onboarding through documentation and workflow enhancements. The solutions delivered reliable, AI-ready data flows and a testable backend, supporting faster student-facing analytics and streamlined academic data management.
Overall Statistics
Feature vs Bugs
94%Features
Repository Contributions
67Total
Bugs
1
Commits
67
Features
16
Lines of code
170,131
Activity Months4
Your Network
13 people
Work History
July 2025
12 Commits • 3 Features
Jul 1, 2025July 2025 performance summary for unb-mds/2025-1-NoFluxoUNB: Achieved key data engineering and AI tooling improvements by delivering an end-to-end UNB course data scraping pipeline, migrating the AI agent to a robust Flask API, and cleaning up repository structure to boost maintainability and deployment readiness. These changes establish AI-ready data flows, a centralized and testable AI service, and a cleaner codebase, enabling faster AI model provisioning and reliable data pipelines.
12 Commits • 3 Features
Jul 1, 2025July 2025 performance summary for unb-mds/2025-1-NoFluxoUNB: Achieved key data engineering and AI tooling improvements by delivering an end-to-end UNB course data scraping pipeline, migrating the AI agent to a robust Flask API, and cleaning up repository structure to boost maintainability and deployment readiness. These changes establish AI-ready data flows, a centralized and testable AI service, and a cleaner codebase, enabling faster AI model provisioning and reliable data pipelines.
July 2025
June 2025
19 Commits • 7 Features
Jun 1, 2025June 2025 focused on delivering AI-assisted class discovery, reliable data extraction from the Ragflow pipeline, and a beginner-friendly analytics UI to scale student adoption. Key outcomes include launching and tuning AI prompts for the student-facing class search, strengthening RAGFlow result handling (parsing Disciplina, Unidade responsavel, and Ementa; de-duplication; top-4 results; robust error handling), and delivering user-facing analytics via a Streamlit app. Additional progress includes building reusable tooling with a Ragflow API client and a JSON ranking parser to accelerate future work, and providing AI-agent testing data to ensure robust JSON outputs. These efforts increased search accuracy, reduced manual data wrangling, and established a repeatable framework for data formatting and testing. Technologies demonstrated include Python, Streamlit, API client design, robust JSON parsing, error handling, and data normalization.
June 2025
19 Commits • 7 Features
Jun 1, 2025June 2025 focused on delivering AI-assisted class discovery, reliable data extraction from the Ragflow pipeline, and a beginner-friendly analytics UI to scale student adoption. Key outcomes include launching and tuning AI prompts for the student-facing class search, strengthening RAGFlow result handling (parsing Disciplina, Unidade responsavel, and Ementa; de-duplication; top-4 results; robust error handling), and delivering user-facing analytics via a Streamlit app. Additional progress includes building reusable tooling with a Ragflow API client and a JSON ranking parser to accelerate future work, and providing AI-agent testing data to ensure robust JSON outputs. These efforts increased search accuracy, reduced manual data wrangling, and established a repeatable framework for data formatting and testing. Technologies demonstrated include Python, Streamlit, API client design, robust JSON parsing, error handling, and data normalization.
May 2025
16 Commits • 2 Features
May 1, 2025May 2025 monthly summary for unb-mds/2025-1-NoFluxoUNB: Delivered automation enhancements for academic records processing with a focus on PDF transcript extraction and export, alongside targeted maintenance and cleanup to improve maintainability and workflow reliability. The work emphasizes business value through reliable data ingestion and faster downstream reporting.
16 Commits • 2 Features
May 1, 2025May 2025 monthly summary for unb-mds/2025-1-NoFluxoUNB: Delivered automation enhancements for academic records processing with a focus on PDF transcript extraction and export, alongside targeted maintenance and cleanup to improve maintainability and workflow reliability. The work emphasizes business value through reliable data ingestion and faster downstream reporting.
May 2025
April 2025
20 Commits • 4 Features
Apr 1, 2025April 2025 (2025-04) in unb-mds/2025-1-NoFluxoUNB focused on delivering foundational documentation, governance improvements, data ingestion enhancements, and codebase stability. Key work spanned four feature areas with a total of 20 commits. No major defects were reported; minor stability and readability improvements were applied through refactoring and cleanup. The work lays groundwork for faster onboarding, clearer cross-team coordination, and broader data coverage.
April 2025
20 Commits • 4 Features
Apr 1, 2025April 2025 (2025-04) in unb-mds/2025-1-NoFluxoUNB focused on delivering foundational documentation, governance improvements, data ingestion enhancements, and codebase stability. Key work spanned four feature areas with a total of 20 commits. No major defects were reported; minor stability and readability improvements were applied through refactoring and cleanup. The work lays groundwork for faster onboarding, clearer cross-team coordination, and broader data coverage.
Activity
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Quality Metrics
Correctness88.6%
Maintainability89.2%
Architecture84.8%
Performance81.4%
AI Usage27.4%
Skills & Technologies
Programming Languages
CSSCSVHTMLJSONJavaScriptMarkdownPlain TextPythonTextTypeScript
Technical Skills
AI Agent IntegrationAI Assistant DevelopmentAI ConfigurationAI IntegrationAI Prompt EngineeringAPI DevelopmentAPI IntegrationAgile MethodologiesBackend DevelopmentCI/CDCSV ParsingCode CleanupCode OrganizationConcurrencyConfiguration Management
Repositories Contributed To
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