pair-selector

$npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/pair-selector

Selects optimal comparison pairs to reduce ranking uncertainty

  • Solves the problem of choosing informative pairs for pairwise comparisons
  • Uses current ratings and comparison history as input data
  • Applies information-theoretic criteria to maximize entropy reduction
  • Returns an ordered list of candidate pairs for next comparisons

SKILL.md

.github/skills/pair-selectorView on GitHub ↗
---
name: pair-selector
description: Select the next comparison pairs that maximize information gain given current ratings and comparison history.
execution: subagent
prompt: ./prompt.md
input: current_ratings(object), comparison_history(array)
used-by: pairwise-ranking
---

# Pair Selector

Selects the next set of comparison pairs that would most reduce ranking uncertainty. Uses information-theoretic criteria (maximum entropy reduction, uncertainty sampling, or boundary proximity) to prioritize which pairs to compare next.

## Execution

Runs as a subagent. Receives current ratings and comparison history, returns an ordered list of recommended next pairs.

## Why Subagent

Pair selection requires reasoning about the full rating landscape and comparison graph structure. Isolating this as a subagent allows focused computation without polluting the orchestrator's context with matrix calculations.

## HARD-GATE

Output MUST contain at least one pair. Each pair must reference exactly two distinct candidates that exist in the current_ratings input.

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