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meeting-processor会议处理器

Agent Skill

meeting-processor 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

692

周安装

28

GitHub Stars

141

下载量

217
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:meeting-processor(会议处理器)
来源仓库:https://github.com/glebis/claude-skills
仓库路径:skills/meeting-processor
安装命令:
npx skills add https://github.com/glebis/claude-skills --skill meeting-processor
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/glebis/claude-skills --skill meeting-processor

简介

meeting-processor 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 它能自动提取仓库关键信息,协助分析代码变更和协作流程,提升开发效率。
  • 可通过 npx skills add 命令从 GitHub 仓库安装,具体用法建议参考原始 README 文件。
  • 安装前请确认权限范围和维护状态,注意可能涉及联网、命令执行或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Meeting Processor

Intelligent meeting transcript processor that auto-detects meeting type and applies type-specific extraction with optional interactive clarification.

When to Use

  • After syncing Fathom or Granola transcripts (/fathom --today, /granola export)
  • When asked to process, analyze, or summarize a meeting transcript
  • When a new meeting transcript appears in the vault root matching YYYYMMDD-*.md
  • For coaching sessions, delegate to coaching-session-summarizer skill instead

Prerequisites

pip install openai pyyaml

Requires CEREBRAS_API_KEY environment variable (uses Cerebras API with llama-3.3-70b).

Supported Meeting Types

TypeDescriptionKey Extractions
leadgenSales/business development callsCommitments, pain points, budget, timeline, decision makers, deal stage, sentiment
partnershipCollaboration/partnership explorationOpportunity overview, value proposition, strategic alignment, technical needs, fit assessment
coachingCoaching/mentoring sessionsInsights, decisions, action items, themes, emotional arc, techniques, session quality
internalInternal team meetingsComing soon

Usage

Interactive Mode (default)

Run the processor, which auto-detects meeting type and asks clarifying questions:

python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --mode interactive

Interactive flow:

  1. Script analyzes transcript and detects meeting type
  2. Extracts structured data via LLM
  3. Identifies missing/ambiguous fields
  4. Returns questions as JSON (exit code 2 signals interaction needed)
  5. Parse the JSON between __INTERACTIVE_QUESTIONS__ markers
  6. Use AskUserQuestion to collect answers for each question
  7. Save answers to a temp JSON file and re-run with process_with_answers.py

Handling interactive questions:

When the script exits with code 2, parse the output for questions JSON. Each question has:

  • question: The question text
  • header: Short label (used as answer key)
  • options: Array of {label, description} for AskUserQuestion

After collecting answers, create two temp files:

  • questions.json — the original questions context (includes partial_data, meeting_type, transcript_file)
  • answers.json — map of {header_lowercase: selected_label}

Then run:

python3 ~/.claude/skills/meeting-processor/scripts/process_with_answers.py questions.json answers.json

Batch Mode

Extract only high-confidence information without user interaction:

python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --mode batch

Force Meeting Type

Skip auto-detection:

python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --type leadgen
python3 ~/.claude/skills/meeting-processor/scripts/process.py <transcript-file> --type partnership

Output

Analysis is appended to the transcript file as a ## Meeting Analysis section. Frontmatter is updated with meeting_type, processed_date, and processing_mode.

Leadgen Output Structure

  • Commitments & Actions — with deadlines and owners
  • Follow-up — next meeting date if scheduled
  • Client Context — pain points, budget, timeline, decision makers
  • Deal Assessment — stage (cold/warm/hot), probability (1-5), blocker, sentiment

Partnership Output Structure

  • Opportunity — description and value proposition for both sides
  • Commitments & Actions — with deadlines and owners
  • Follow-up — next meeting date if scheduled
  • Partnership Context — strategic alignment, technical needs, resources, challenges
  • Opportunity Assessment — fit (strong/medium/weak), readiness, success factors, sentiment

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.32%
按下载量换算74

Claude

29%
按下载量换算63

Cursor

17.38%
按下载量换算38

Gemini CLI

9.56%
按下载量换算21

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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