Skip to content
Automation
Skill

/aggregate-ranking

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

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/aggregate-ranking

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

aggregate-ranking.SKILL.md
name: aggregate-ranking
description: "Aggregate criterion or comparison results into an ordered recommendation under an explicit rule."

aggregate-ranking

Purpose

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

Input contract

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

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

| source | criterion | |---|---| | priority-synthesis | All scoring dimensions are present for every gap. | | priority-synthesis | Weight vector sums to 1.0 within +/-0.001. | | priority-synthesis | Priority list is sorted descending; ties use feasibility sub-score. | | priority-synthesis | Top N is N=min(3,total gaps) and includes attack-path suggestions. | | scoring-synthesis | Final recommendation includes recommended alternative, confidence, key assumptions, and risk warnings. |

Read more
Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

Get the whole plugin
Stats
499
Stars
41
Forks
Active
Maintenance
Python
Language
Apache-2.0
License
53m ago
Last commit
7mo ago
Created

Repo: yogsoth-ai/de-anthropocentric-research-engine