Clean, parse, and normalize Brazilian court and administrative data with Python APIs.
npx skills add https://github.com/hsigstad/research-kit --skill diarios
Please help me install this Agent Skill. Skill: diarios Source: https://github.com/hsigstad/research-kit/tree/main/skills/diarios Command: npx skills add https://github.com/hsigstad/research-kit --skill diarios
The diarios skill addresses the complexity of processing Brazilian court and administrative data, offering a streamlined approach to manage court data, legal text parsing, and administrative data cleaning.
Utilize the diarios skill in your research project by importing it and utilizing the clean_text(), clean_cnj_number(), or clean_municipio() functions as needed for your Brazilian court data processing.
High-intent search queries and answers about installing and using this skill.
To normalize Brazilian court case numbers in Python, import the diarios module and call the clean_cnj_number() function. This processes raw docket strings into standardized formats for consistent legal record tracking.
Parsing Brazilian court and administrative data is best handled by using dedicated legal text parsing and data cleaning APIs. These tools automate the extraction and normalization of docket data, decisions, and party names.
To clean Brazilian municipality data, use the clean_municipio() function. It processes geographic locations by standardizing administrative text inputs, ensuring consistent formatting for regional legal and administrative datasets.
You do not need OCR and database capabilities to process legal texts, as Python is the only required environment. However, adding optional OCR and database tools enhances functionalities for advanced court data extraction.
Yes, you can customize how court cases and docket data are parsed. The module provides customizable tools and comprehensive APIs for handling decisions, party names, and cleaning operations to fit specific research needs.
Checking an existing module before developing new legal text parsing functions saves time and ensures quality. It provides pre-built APIs for common tasks like text cleaning and court case number normalization, preventing duplicated effort.
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