yogsoth-ai/de-anthropocentric-research-engine

coverage-white-space-search

Map known methods against problem/parameter space and generate candidates from uncovered and weakly covered intersections.

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Cómo usar

  1. Copia el comando y revisa el paquete antes de instalarlo en tu entorno de agente.
  2. Consulta las herramientas, credenciales y entornos necesarios en el documento original.
  3. Describe las entradas y el resultado deseado; contrasta la salida con las fuentes.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill coverage-white-space-search

Antes de empezar

  • La instalación no incluye suscripción al modelo, cuota API ni permisos sobre tus archivos.
  • Las estrellas e instalaciones reflejan actividad, no una prueba independiente de calidad o seguridad.
Documento original del Skill

Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.

coverage-white-space-search

Purpose

Map known methods against a problem space and generate candidates from intersections that are uncovered or covered without being closed.

Input contract

yaml
required: [reference_items, coverage_dimensions]
optional: [performance_records, eligibility_rules]
constraints: [reference items and dimension values must be identifiable]

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

  1. You MUST load skill inventory-reference-items to inventory references.
  2. You MUST load skill map-coverage-space to map coverage and grade its strength.
  3. You MUST load skill detect-coverage-gap to detect uncovered and weakly covered regions.
  4. You MUST load skill synthesize-idea to synthesize candidates.

If a candidate gap is ready to become a testable explanation, consider formulate-hypotheses. If the gap itself still requires evidence validation, consider validate-research-gap. Deviation: acquisition is runtime-selected; do not infer coverage from missing records.

Output contract

yaml
produces: [reference_inventory, coverage_map, white_space_gaps, candidate_ideas]
delta_fields: [findings, evidence_updates, hypothesis_updates, decisions, open_questions]

Thresholds and quality gates

  • B: every reported gap is either an explicit uncovered intersection over the declared dimensions or an occupied intersection graded below full support, with its covering items and specific shortfall named; duplicate or ineligible regions are excluded.

Failure and counterexamples

Do not label a region white space when it is merely unsearched, incomparable, or outside the declared universe, and do not treat a region as closed because items occupy it.

Provenance map

  • systematic-enumeration, benchmark-sweep, coverage-analysis, white-space-identification, benchmark-inventory, coverage-gap-detection, white-space-detection: resolved/concept by exact lookup.
  • Status: all except benchmark-inventory resolved; benchmark-inventory concept (only package-prefixed variant found).

Preserved source criteria ledger

  • Preserve inventory -> coverage crossing -> gap detection -> candidate synthesis.

Context checkpoint / Delta notes

Append inventory additions, occupied cells with strength grades, gap definitions, exclusions, and generated candidates.

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