tiger-ai-lab/rewardharness/evaluating-thematic-and-setting-accuracy

Claude Code and agent skill by tiger-ai-lab/rewardharness

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Guidelines for evaluating full setting transformations and ensuring exact

Install

npx vskill@latest install tiger-ai-lab/rewardharness/evaluating-thematic-and-setting-accuracy

SKILL.md

When evaluating full setting transformations (e.g., 'elementary school cafeteria', 'sold out concert', 'underground New York subway'), look for specific distinguishing elements. A classroom with desks is NOT a cafeteria. A room with a few people is NOT a sold-out concert. A generic train station is NOT a New York subway. If the edited image retains too much of the original setting (e.g., keeping a restaurant table when asked for a school cafeteria), penalize its instruction score.

<!-- Release review: RewardHarness v0.4.1 (2026-08-26). -->

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Repository stars
100
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9
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