yogsoth-ai/de-anthropocentric-research-engine

aggregate-ranking

Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

aggregate-ranking

Purpose

Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.

Input contract

yaml
required: [candidate_set, criterion_results, aggregation_rule]
optional: [tie_break_rule, missing_value_policy, uncertainty_annotations]
constraints: [criterion directions and scales must be declared; no silent imputation]

Procedure

  1. Align candidate identifiers, criterion directions, units, and validity flags.
  2. Apply the supplied aggregation rule without changing weights or directions.
  3. Propagate missingness and uncertainty; apply the declared tie-break only after aggregation.
  4. Return ordered candidates with component contributions and recommendation status.

Output contract

yaml
produces: [ordered_recommendation, aggregate_scores, contribution_table, unresolved_comparisons]
delta_fields: [findings, evidence_updates, decisions, uncertainties]

Quality gates

  • Every ranked candidate has a traceable value for each required criterion or an explicit unresolved marker.
  • Aggregation reproduces the supplied rule and preserves criterion direction.
  • Ties and sensitivity to tie-breaks are reported.

Parameterization

Caller supplies candidate schema, criterion scales/directions, weights or aggregation formula, tie-break rule, and missing/uncertainty policy.

Failure and counterexamples

Reject mixed units without normalization; reject a recommendation when a hard criterion is unresolved.

Provenance map

  • resolved: priority-synthesis
  • resolved: scoring-synthesis

Preserved source criteria ledger

sourcecriterion
priority-synthesisAll scoring dimensions are present for every gap.
priority-synthesisWeight vector sums to 1.0 within +/-0.001.
priority-synthesisPriority list is sorted descending; ties use feasibility sub-score.
priority-synthesisTop N is N=min(3,total gaps) and includes attack-path suggestions.
scoring-synthesisFinal recommendation includes recommended alternative, confidence, key assumptions, and risk warnings.

Verwandte Aufgaben

aus demselben Repository

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