Individual skill
twinmind-performance-tuning
Optimize TwinMind transcription accuracy and speed with Ear-3 model configuration, audio quality tuning, and caching strategies. Use when implementing performance tuning, or managing TwinMind meeting AI operations. Trigger with phrases like "twinmind performance tuning", "twinmind performance tuning".
skills/.curated/twinmind-performance-tuning/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Last checked
- Nov 12, 2026
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- 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
- twinmind Declared skill tag
- performance Declared skill tag
- operations Derived category
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SKILL.mdcore · 3,729 bytesSKILL.mdcore · 3,729 bytesSKILL.mdcore · 3,729 bytesreferences/implementation.mddoc · 6,287 bytes
Read excerpt
# TwinMind Performance Tuning
## Overview
Optimize TwinMind transcription accuracy and speed with Ear-3 model configuration, audio quality tuning, and caching strategies. TwinMind uses the Ear-3 speech model (5.26% WER, 3.8% DER) for transcription, with GPT-4, Claude, and Gemini for AI summarization.
## Prerequisites
- TwinMind account (Free, Pro $10/mo, or Enterprise)
- Chrome extension installed and authenticated
- Understanding of TwinMind workflow
## Instructions
### Step 1: Setup