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

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:

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:

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