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Meeting Quality Scorer

读取会议转写文本,调用用户配置的 OpenAI 兼容 LLM 端点,从决议明确度(40%)、时间效率(30%)、参与均衡度(30%,基于发言字数基尼系数)三个维度评分,生成 Markdown 报告与 HTML 可视化;没有说话人标签时跳过参与维度,并把权重调整为 60/40。

ucsdzehualiu
ucsdzehualiu
热度 15.99
最近更新 2026-06-24 09:16:05
分类:协作依赖:代码执行 / 读写文件 / 第三方 API / 内部调用大模型应用场景:会议纪要 / 报告撰写 / 摘要总结

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add meeting-quality-scorer
Metadata
name
meeting-quality-scorer
description
Objectively score meeting quality from a transcript. Three dimensions: decision clarity, time efficiency, participation balance. Outputs a Markdown report and an HTML visualization. Works with any OpenAI-compatible LLM endpoint (Ollama, DeepSeek, OpenAI, etc.). Pairs with meeting_whisper for a transcribe-then-score pipeline.
version
1.0.0
author
ucsdzehualiu
license
MIT
trigger_keywords
["meeting-quality-scorer","meeting score","会议评分","会议质量","会议有效性","score this meeting","rate the meeting"]

meeting-quality-scorer

Score meeting quality from a transcript.

Quick Start

pip install -r requirements.txt

# Score a meeting transcript
python scripts/score_meeting.py --input meeting.txt

# Or with custom output paths
python scripts/score_meeting.py --input meeting.txt --out-md my-report.md --out-html my-report.html

Configuration

Set LLM backend via environment variables:

export MQS_BASE_URL=http://localhost:11434/v1  # Ollama
export MQS_API_KEY=ollama
export MQS_MODEL=qwen2.5:72b

Or create ~/.config/meeting-quality-scorer/config.yaml:

base_url: https://api.openai.com/v1
api_key: sk-...
model: gpt-4o-mini

Three Dimensions

Dimension Weight How scored
决议明确度 40% LLM detects topics + decisions + owners
时间效率 30% LLM identifies off-topic / filler segments
参与均衡度 30% Gini coefficient on speaker word counts

If no speaker labels → participation skipped, weights redistribute to 60/40.

Privacy

Transcript is sent only to your configured LLM endpoint. No telemetry.