yogsoth-ai/de-anthropocentric-research-engine

aggregate-ranking

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

View source

How to use

  1. Copy the installation command and review the source package before installing it in your own agent environment.
  2. Check the source document for required tools, credentials and supported hosts.
  3. Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking

Before you start

  • Installation does not include a model subscription, API allowance or permission to access your files.
  • Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document

Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.

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.

Related by task

from this repository

More skills

All skills