yogsoth-ai/de-anthropocentric-research-engine

categorize-evidence

Cluster an evidence corpus by theme, method, chronology, mechanism, or another declared schema.

Voir la source

Mode d’emploi

  1. Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
  2. Consultez le document original pour les outils, identifiants et environnements requis.
  3. Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill categorize-evidence

Avant de commencer

  • L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
  • Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original

Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.

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

  1. Define category semantics and assignment rules.
  2. Code each record using the declared evidence fields.
  3. Review boundary cases and preserve multi-label or unresolved assignments.
  4. 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-papers
  • concept: knowledge-structuring/source-categorization-patterns
  • intermediate: Pass4/map-field-taxonomy

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