Come usare
- Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
- Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
- Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidencePrima di iniziare
- L’installazione non include abbonamenti, quote API o accesso ai file.
- Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale
Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.
categorize-evidence
Purpose
Cluster an evidence corpus by a declared thematic, methodological, chronological, mechanistic, or other schema.
Input contract
yaml
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
yaml
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
