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Meeting Processor

Community
glebis
meeting-processor

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.

Overview

Publisherglebis
Repositoryclaude-skills
Skill namemeeting-processor
Stars
379
Forks
56
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by glebis on GitHub. Read the source before you install it.

Installation

Install the Meeting Processor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/glebis/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/meeting-processor .claude/skills/meeting-processor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Meeting Processor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Meeting Processor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Meeting Processor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
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:

bash
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:

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

Batch Mode

Extract only high-confidence information without user interaction:

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

Force Meeting Type

Skip auto-detection:

bash
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:

    bash
    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:
      markdown
      ## 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Meeting Processor AI 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.

Why use Meeting Processor on TypingMind?

Because you install it once and use it with any model. Meeting Processor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Meeting Processor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/glebis/claude-skills/tree/main/meeting-processor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Meeting Processor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Meeting Processor?

As many as you like. As long as a model supports skills, you can use Meeting Processor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Meeting Processor AI skill free?

Yes. It is published on GitHub by glebis under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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