Individual skill
assemblyai-performance-tuning
Analyze and tune AssemblyAI model choice, audio delivery, polling, Streaming v3 turns, concurrency, and downstream work. Use when pursuing measured latency or throughput goals. Trigger with "tune AssemblyAI" or "AssemblyAI latency".
plugins/saas-packs/assemblyai-pack/skills/assemblyai-performance-tuning/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Last checked
- Nov 12, 2026
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- Repository stars (not skill ratings)
- 2,916
- Repository last updated
- Nov 12, 2026
- License
- MIT
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- saas Declared skill tag
- assemblyai Declared skill tag
- engineering Derived category
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SKILL.mdcore · 4,249 bytesreferences/official-docs.mddoc · 1,386 bytes
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# AssemblyAI Latency and Throughput Tuning
## Overview
Tune latency and throughput with controlled, representative experiments. Preserve accuracy, privacy, credentials, spend, and rollback as separate gates.
## Prerequisites
- The target repository or integration path and the requested operator outcome.
- The AssemblyAI project, environment, region, data classification, and accountable owner.
- Current first-party documentation plus credentials only for a narrowly approved live check.
## Current Contract
Pre-recorded and streaming optimize different outcomes. Current models differ in languages, accuracy, latency, prompting, endpointing, and price. Benchmark representative approved fixtures rather than inherited labels such as Best or Nano, and keep privacy, quality, and spend as co-equal gates.