What problem does it solve? Evaluating LLMs on factual questions requires structured benchmarks with ground-truth answers, extraction templates, and judge models. This Skill guides the full workflow of creating, running, and analyzing single-turn QA benchmarks with the karenina framework. ## Core Features & Use Cases - Benchmark Authoring: Define questions with ground-truth answers, import them in bulk from CSV/TSV/Excel, and auto-generate or manually author answer templates. - Verification Pipeline: Configure answering and judge models, attach rubrics, run verification, and save checkpoints in JSON-LD format. - Result Analysis: Inspect per-question results, failure categories, and export template or rubric scores to DataFrames and files. - Use Case: A researcher wants to test whether a model correctly identifies drug targets. They load 200 expert-authored questions from a CSV, auto-generate answer templates with Claude, run verification, and export per-question pass/fail results for analysis. ## Quick Start Ask the assistant to create a karenina QA benchmark from your questions CSV, generate answer templates, and run verification with your chosen answering and judge models.