Execute predefined benchmark prompts across specified LLMs in parallel.
npx skills add https://github.com/pshirshov/llm-bench-pi-oneshot --skill bench-run
Please help me install this Agent Skill. Skill: bench-run Source: https://github.com/pshirshov/llm-bench-pi-oneshot/tree/main/.claude/skills/bench-run Command: npx skills add https://github.com/pshirshov/llm-bench-pi-oneshot --skill bench-run
This Skill automates the benchmarking process for Language Learning Models (LLMs) by executing benchmark prompts across a set of predefined models in parallel, monitoring progress, and reporting on completion.
Use the bench-run skill to execute a benchmark run for the prompt with ID '001-rts-wfc' using the default models and parallelism level.
High-intent search queries and answers about installing and using this skill.
To automate LLM benchmark execution, use this Skill to run predefined prompts across specified models in parallel, monitor progress, and report completion. It requires the scripts/run_bench.py script to execute the benchmark runs.
Yes, you can run multiple benchmark prompts simultaneously. The Skill supports parallel execution, allowing you to specify a parallelism level to run prompts across different LLMs concurrently and save time.
You can customize LLM benchmarking by specifying prompt IDs and providing model overrides. This allows you to target specific models during the parallel execution of your benchmark prompts.
Yes, you need the scripts/run_bench.py script to execute LLM benchmarking tasks. This script is a required dependency for the Skill to run predefined prompts and monitor progress across models.
The best way to track progress during parallel LLM benchmarking is to use this Skill, which includes built-in progress monitoring to track each run and report on completion automatically.
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