Extract structured contest edital data from PDFs into JSON.
npx skills add https://github.com/Nunopdmp/mvp_ai --skill m1-edital-parser
Please help me install this Agent Skill. Skill: m1-edital-parser Source: https://github.com/Nunopdmp/mvp_ai/tree/main/skills/m1-edital-parser Command: npx skills add https://github.com/Nunopdmp/mvp_ai --skill m1-edital-parser
This Skill isolates and structures essential data from contest editais (edital PDFs) to feed content production pipelines, reducing manual extraction errors and saving time for course design teams.
Analyze a provided edital PDF and request extraction for a given cargo and disciplina.
High-intent search queries and answers about installing and using this skill.
To extract structured data from a contest edital PDF, you need a parser that isolates cargo, disciplina, and conteudo programatico while preserving numbering. This produces a JSON object consolidating concurso metadata for course production workflows.
Converting edital content into a JSON schema requires parsing PDF text and structuring cargo details and programmatic content. The output is a standardized JSON file aligning with your specified schema, ready to feed automated curriculum design pipelines.
Automating contest metadata extraction for course design is best achieved by parsing edital PDFs to isolate cargo and disciplina data. This structured data extraction reduces manual errors and generates a ready-to-use curriculum plan JSON.
Yes, the edital parser preserves numbering from the original PDF document during text extraction. Maintaining original numbering ensures accurate mapping of conteudo programatico and cargo details into the final structured JSON output.
Yes, you can use this data extraction tool for specific cargos and disciplines within an edital. You simply provide the edital PDF and select your target cargo and disciplina to generate the structured JSON output.
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