Design statistically powered benchmarks for LLM prompts and configurations.
npx skills add https://github.com/andreasronge/ptc_runner --skill llm-benchmark
Please help me install this Agent Skill. Skill: llm-benchmark Source: https://github.com/andreasronge/ptc_runner/tree/main/.claude/skills/llm-benchmark Command: npx skills add https://github.com/andreasronge/ptc_runner --skill llm-benchmark
Design rigorous, statistically powered benchmarks to evaluate LLM prompts and configurations, ensuring experiments produce actionable, unbiased conclusions.
Run a baseline benchmark on your prompts and report observed pass rates, per-turn metrics, and recommended sample sizes.
High-intent search queries and answers about installing and using this skill.
To design statistically powered LLM prompt benchmarks, you use rigorous experimental templates that calculate recommended sample sizes and report pass rates with confidence intervals. This ensures evaluations produce actionable, unbiased conclusions.
Data leakage in LLM benchmarking occurs when test data influences prompt design. You prevent it by applying data-leakage checks that keep prompts domain-blind and ensure held-out test suites remain completely separate from experimentation phases.
To run an A/B test for LLM prompts, you apply per-turn metrics and statistical analysis guidance to interpret results across multiple models and domains. This allows you to compare configurations and execute prompt ablation studies effectively.
The best way to report LLM benchmark results is using standardized reporting of observed pass rates, per-turn metrics, and confidence intervals. This standardized reporting ensures your experimental design yields clear, actionable conclusions across models.
Yes, this approach supports prompt ablation studies across multiple models and domains. It provides policy versus mechanism benchmark templates and per-turn analysis to isolate how specific prompt configurations affect overall performance.
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