Como usar
- Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
- Consulte no original as ferramentas, credenciais e ambientes necessários.
- Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidenceAntes de começar
- A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
- Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
categorize-evidence
Purpose
Cluster an evidence corpus by a declared thematic, methodological, chronological, mechanistic, or other schema.
Input contract
required: [evidence_records, category_schema]
optional: [coding_rules, multi_label_policy, seed_categories]
constraints: [category assignments require record evidence and allow explicit multi-label or unknown states]Procedure
- Define category semantics and assignment rules.
- Code each record using the declared evidence fields.
- Review boundary cases and preserve multi-label or unresolved assignments.
- Summarize category coverage and representative records.
If categorized sources now need claim-level structured records, consider extract-evidence-record as the next tactic.
Output contract
produces: [categorized_corpus, category_definitions, boundary_cases, coverage_summary]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]Quality gates
- Categories are mutually interpretable even when not mutually exclusive.
- Every assignment is traceable to record fields.
Failure and counterexamples
Do not force records into categories whose definitions do not fit, and do not confuse frequency with evidential importance.
Provenance map
resolved: knowledge-acquisition-categorize-papersconcept: knowledge-structuring/source-categorization-patternsintermediate: Pass4/map-field-taxonomy


