ballot-collection

$npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/ballot-collection

Collects independent ranking ballots from judges for candidate set

  • Identifies and configures perspectives to ensure independence of ballot generation.
  • Deploys a prompt template to guide judges in producing ranked ballots without seeing others' inputs.
  • Uses logic to validate each ballot, ensuring no duplicates unless perspectives are indistinguishable.
  • Returns the collected ballots structured for further processing or analysis.

SKILL.md

.github/skills/ballot-collectionView on GitHub ↗
---
name: ballot-collection
description: Gather independent ranking ballots from multiple judges or perspectives for a given candidate set.
execution: subagent
prompt: ./prompt.md
input: candidates(array), perspectives(array)
used-by: pairwise-ranking
---

# Ballot Collection

Collects independent ranking ballots from multiple judges or evaluation perspectives. Each judge produces a complete or partial ranking of the candidates without seeing other judges' rankings.

## Execution

Runs as a subagent. Receives candidates and perspective definitions, returns structured ballots.

## Why Subagent

Each ballot must be generated independently to prevent anchoring. The subagent evaluates from a single perspective without access to other judges' outputs, ensuring genuine independence.

## HARD-GATE

Output MUST contain one ballot per perspective. Each ballot MUST rank all candidates (complete ranking) or explicitly mark unranked candidates. No two ballots may be identical unless perspectives are genuinely indistinguishable.

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.