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benchmark

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Use when choosing between engines or models for an agent pipeline and the answer must come from measurement on your own data, not from marketing pages: speech recognition for meeting recordings, the model that turns a transcript into notes, the critic model that checks it. Runs every variant in an isolated container, measures time, cost per hour of input (USD and RUB, with price source and date) and quality (WER/CER and course terms for ASR; code checks plus a judge of a different model for LLM steps), writes results.json/csv and one static analytics page. Triggers on "бенчмарк", "сравни движки", "сколько стоит час записи", "какую модель взять для пайплайна", "benchmark the pipeline", "compare ASR engines".

skills/benchmark/SKILL.md

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Nov 3, 2026
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# Benchmark

Measure a pipeline on your own input before you pick an engine. One task file lists the variants; the scripts run them one by one in an isolated container and write numbers you can defend: time, cost per hour of input with a dated price source, quality against a reference.

Shape of a pipeline this skill knows:

`input (recording) → ASR engine → transcript → author model (notes, chapters) → code checks → judge model`

Each arrow is a slot. A variant fills one slot and keeps the rest fixed.

## When to use

- «ElevenLabs, Whisper or a Russian engine: what does an hour of our meetings cost and who makes fewer mistakes»;
- «move the notes step from Claude to GLM: does quality hold»;
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