MCPInstall

meeting-processor

by glebisProductivity & Automation

This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis. Triggers on "process meeting", "analyze meeting", "meeting summary", or after syncing new Fathom/Granola transcripts.

Installs
190
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/glebis/claude-skills --skill meeting-processor
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads meeting-processor automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

Step 2: Auto-Link Prep Notes

After the meeting analysis is complete (Step 1), automatically link any matching meeting-prep notes to the session note. This replaces the need to manually run /meeting-prep link.

How It Works

  1. Derive the meetings directory from the processed session note's parent directory (do not hardcode paths).

  2. Extract session metadata from the processed note:

    • date from frontmatter (YYYYMMDD format)
    • participants from frontmatter (list of names)
    • If no participants field, extract names from the transcript header or attendee list

  3. Search for matching prep notes:

    find <MEETINGS_DIR> -name "YYYYMMDD-prep-*" -type f 2>/dev/null

    Where YYYYMMDD is the session date.

  4. Validate the match: For each candidate prep note, read its frontmatter and confirm:

    • The date field matches the session date
    • The participant field matches one of the session's participants (fuzzy: check both full name and first name, case-insensitive)
    • The session_note field is empty ("") — skip already-linked prep notes

  5. Update both files when a match is found:

    In the prep note:

    • Set session_note: "[[session-note-filename]]" (without .md extension)
    • Set status: done

    In the session note:

    • If a ## See also section exists, add - [[YYYYMMDD-prep-participant-slug]] to it
    • Otherwise, append a new section at the end: 
      ## Prep Note
- [[YYYYMMDD-prep-participant-slug]]
    • Never create duplicate links — check if the link already exists before adding

  6. Report in the processing output which prep notes were linked, skipped, or not found.

Rules

  • Derive MEETINGS_DIR from the session note path, not from hardcoded values
  • If the meeting-prep config.yaml is available, read prep_notes.prefix (default: prep) and prep_notes.type_tag (default: meeting-prep)
  • This step is non-blocking: if it fails or finds no prep notes, processing still succeeds

FAQ

What does the meeting-processor skill do?

This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis. Triggers on "process meeting", "analyze meeting", "meeting summary", or after syncing new Fathom/Granola transcripts.

How do I install meeting-processor?

Run npx skills add https://github.com/glebis/claude-skills --skill meeting-processor in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/meeting-processor/ (personal) or .claude/skills/meeting-processor/ (project).

Which AI agents can use meeting-processor?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is meeting-processor safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains meeting-processor?

It is published by glebis in glebis/claude-skills. It has 190 recorded installs.

More from glebis/claude-skills

View all →

google-image-search

glebis/claude-skills

Search and download images via Google Custom Search API with LLM-powered selection. This skill should be used when finding images for articles, presentations, research documents, or enriching Obsidian notes with relevant visuals. Supports simple queries, batch processing from JSON config, automatic config generation from terms, and full note enrichment with automatic image insertion below headings.

Media & Creative643 installs

pdf-generation

glebis/claude-skills

Professional PDF generation from markdown using Pandoc with Eisvogel template and EB Garamond fonts. Use when converting markdown to PDF, creating white papers, research documents, marketing materials, or technical documentation. Supports both English and Russian documents with professional typography and color-coded themes. Mobile-optimized layout (6x9) by default for Telegram bot context, desktop/print layout (A4) for other contexts.

Docs & Writing482 installs

health-data

glebis/claude-skills

Query Apple Health SQLite database for vitals, activity, sleep, and workouts. Supports Markdown, JSON, and FHIR R4 output formats. This skill should be used when analyzing health metrics, generating health reports, answering questions about fitness or sleep patterns, or exporting health data in standard formats.

Education & Science393 installs

firecrawl-research

glebis/claude-skills

This skill should be used when the user requests to research topics using FireCrawl, enrich notes with web sources, search and scrape information, or write scientific/academic papers. It extracts research topics from markdown files, creates research documents with scraped sources, generates BibTeX bibliographies from research results, and provides Pandoc/MyST templates for academic writing with citation management.

Productivity & Automation379 installs

deep-research

glebis/claude-skills

This skill should be used when conducting comprehensive research on any topic using the OpenAI Deep Research API. It automates prompt enhancement through interactive clarifying questions, saves research parameters, and executes deep research with web search capabilities. Use when the user asks for in-depth analysis, investigation, research summaries, or topic exploration.

Productivity & Automation324 installs

presentation-generator

glebis/claude-skills

Generate interactive HTML presentations with neobrutalism styling, ASCII art decorations, and Agency brand colors. Outputs HTML (interactive with navigation), PNG (individual slides via Playwright), and PDF. References brand-agency skill for colors and typography. Use when creating presentations, slide decks, pitch materials, or visual summaries.

Docs & Writing320 installs

Popular productivity & automation skills

View category →

agent-browser

vercel-labs/agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

OfficialProductivity & Automation1.3M installs

lark-doc

open.feishu.cn

飞书云文档（Docx / Wiki）内容操作：读取、创建、编辑文档，插入或下载图片附件，以及操作思维笔记。用户提供文档 URL/token（包括 doubao.com 的 /docx/、/wiki/）时使用；按 URL 路径/token 而非域名路由；/page/ 是妙搭应用，非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive；随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。

Productivity & Automation815.7K installs

lark-base

open.feishu.cn

飞书多维表格（Base）操作：建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式（BaseApp/AppMode 页面与组件）、Workspace 目录、workflow、角色权限、模板中心（多维表格模板分类/列表/搜索）；遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps；文件导入/导出转 lark-drive，认证/授权转 lark-shared。

Productivity & Automation815K installs

lark-drive

open.feishu.cn

飞书云空间（云盘/云存储）：管理 Drive 文件和文件夹，包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签（secure labels）和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题，或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用；doubao.com 云空间 URL/token 也按资源路径和 token 路由，不回退 WebFetch。不负责：文档内容编辑（走 lark-doc）、表格/Base 表内数据操作（走 lark-sheets/lark-base）、知识空间节点/成员管理（走 lark-wiki）、原生 Markdown 文件读写/patch/diff（走 lark-markdown）。

Productivity & Automation815K installs

lark-shared

open.feishu.cn

Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.

Productivity & Automation814.8K installs

lark-im

open.feishu.cn

飞书即时通讯：收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片（Interactive Card）、监听卡片按钮回调（card.action.trigger）。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶（添加/移除/查询置顶会话）、管理标签数据、处理卡片回调时使用。

Productivity & Automation814.1K installs