consensus-measurement

$npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/consensus-measurement

Computes a consensus score from collected judgments using appropriate statistical methods

  • Identifies and analyzes the type of data (continuous, categorical, rankings) to choose the right method for computing the consensus score.
  • Depends on subagent for performing the necessary computations and determining the best method based on input judgments array.
  • Decides whether the computed consensus score meets a predefined threshold by evaluating the results from the subagent.
  • Delivers the computed consensus score, chosen method, threshold met status, and interpretation to the user or agent in a structured JSON format.

SKILL.md

.github/skills/consensus-measurementView on GitHub ↗
---
name: consensus-measurement
description: Compute consensus score from collected judgments using the appropriate statistical method.
execution: subagent
prompt: ./prompt.md
input: judgments[]
used-by: structured-consensus
---

# Consensus Measurement

Compute a quantitative consensus score from the collected judgments. Automatically selects the appropriate measurement method based on data type (IQR for continuous, percentage agreement for categorical, Kendall's W for rankings).

## Execution

Spawn a subagent that analyzes the judgments array, determines the appropriate consensus metric, computes the score, and reports whether the consensus threshold is met.

## Why Subagent

- Statistical computation is a pure function with clear input/output
- Method selection logic is self-contained
- Result feeds directly into round-decision

## HARD-GATE

Output MUST contain: `consensus_score` (numeric), `method_used` (string), `threshold_met` (boolean), and `interpretation` (string). Score must be computed, not estimated.

More from yogsoth-ai/de-anthropocentric-research-engine

SkillDescription
abductive-hypothesis-generationStrategy: 面对异常的最佳解释推理
ablation-brainstormRemove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
ablation-component-mappingMap system architecture to ablatable units for ablation studies
ablation-designDesign ablation studies to isolate component contributions in ML systems
ablation-executionRemove components one by one from a system, record the response/impact of each removal.
abp-vulnerability-classificationClassify assumptions on 2 axes — load-bearing (how much conclusion depends on it) × vulnerable (how likely to be false). Focuses attention on High-Load × High-Vulnerable quadrant.
abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
abstraction-ladderPerform bisociation at multiple abstraction levels
abstraction-ladderingMove between concrete and abstract framings — 3 levels up (Why?) and 3 levels down (How?) to find the most productive research level.
abstraction-to-designAbstract biological principle to design principle. Bridge from biology to engineering.