meeting-processor

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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.

4installs

Sourceglebis/claude-skills

Added on2026-04-30

NPX Install

npx skill4agent add glebis/claude-skills meeting-processor

SKILL.md Content

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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

meeting-processor

NPX Install

Tags

SKILL.md Content

Meeting Processor

When to Use

Prerequisites

Supported Meeting Types

Usage

Interactive Mode (default)

Batch Mode

Force Meeting Type

Output

Leadgen Output Structure

Partnership Output Structure