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

Type	Description	Key Extractions
leadgen	Sales/business development calls	Commitments, pain points, budget, timeline, decision makers, deal stage, sentiment
partnership	Collaboration/partnership exploration	Opportunity overview, value proposition, strategic alignment, technical needs, fit assessment
coaching	Coaching/mentoring sessions	Insights, decisions, action items, themes, emotional arc, techniques, session quality
internal	Internal team meetings	Coming 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:

•Script analyzes transcript and detects meeting type
•Extracts structured data via LLM
•Identifies missing/ambiguous fields
•Returns questions as JSON (exit code 2 signals interaction needed)
•Parse the JSON between __INTERACTIVE_QUESTIONS__ markers
•Use AskUserQuestion to collect answers for each question
•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