'Build a meeting analytics pipeline with Fathom transcripts and summaries.
Scanned 9/2/2026
Install to Claude Code
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---
name: fathom-core-workflow-a
description: 'Build a meeting analytics pipeline with Fathom transcripts and summaries.
Use when extracting insights from meetings, building CRM sync,
or creating automated meeting follow-up workflows.
Trigger with phrases like "fathom analytics", "fathom meeting pipeline",
"fathom transcript analysis", "fathom action items sync".
'
allowed-tools: Read, Write, Edit, Bash(python3:*), Bash(curl:*), Grep
version: 1.6.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- meeting-intelligence
- ai-notes
- fathom
compatibility: Designed for Claude Code
---
# Fathom Core Workflow: Meeting Analytics
## Prerequisites
- A meeting-recording/consent policy, data owner, scoped access, retention rules, and synthetic fixture for validation.
## Output
- A consent-aware meeting analytics workflow with approved access, redacted reporting, owner, and a safe disable/rollback action.
## Error Handling
| Condition | Safe response |
|---|---|
| Meeting access or consent is uncertain | Stop processing/sharing and verify with the designated owner. |
| Summary/action item is wrong | Mark it for human review; do not auto-send it as a source of record. |
| Content is exposed incorrectly | Restrict access and follow the incident/data procedure. |
## Examples
Use a synthetic meeting record to validate analytics fields, summary workflow, access restriction, and retention outcome. Record only opaque IDs and aggregate status; do not use participant recordings, transcripts, or personal data for testing.
## Overview
Build automated meeting analytics: extract action items, sync to project management tools, analyze meeting patterns, and create follow-up workflows.
## Instructions
### Step 1: Batch Meeting Export
```python
from fathom_client import FathomClient
from datetime import datetime, timedelta
client = FathomClient()
# Get all meetings from last 7 days
week_ago = (datetime.utcnow() - timedelta(days=7)).isoformat() + "Z"
meetings = client.list_meetings(
limit=50,
created_after=week_ago,
include_summary="true",
)
for meeting in meetings:
print(f"Meeting: {meeting['title']}")
print(f" Date: {meeting['created_at']}")
print(f" Summary: {meeting.get('summary', 'N/A')[:100]}...")
for item in meeting.get("action_items", []):
print(f" Action: {item['text']} -> {item.get('assignee', 'unassigned')}")
print()
```
### Step 2: Action Item Extraction Pipeline
```python
def extract_action_items(meetings: list[dict]) -> list[dict]:
items = []
for meeting in meetings:
for action in meeting.get("action_items", []):
items.append({
"meeting_title": meeting["title"],
"meeting_date": meeting["created_at"],
"action_text": action["text"],
"assignee": action.get("assignee", "unassigned"),
"meeting_id": meeting["id"],
})
return items
# Sync to task tracker
def sync_to_linear(items: list[dict], api_key: str):
for item in items:
# Create Linear issue from action item
pass
```
### Step 3: Meeting Pattern Analysis
```python
def analyze_meeting_patterns(meetings: list[dict]) -> dict:
total = len(meetings)
total_duration = sum(m.get("duration_seconds", 0) for m in meetings)
total_actions = sum(len(m.get("action_items", [])) for m in meetings)
return {
"total_meetings": total,
"total_hours": round(total_duration / 3600, 1),
"avg_duration_min": round(total_duration / total / 60, 1) if total else 0,
"total_action_items": total_actions,
"avg_actions_per_meeting": round(total_actions / total, 1) if total else 0,
}
```
## Resources
- [Fathom API Reference](https://developers.fathom.ai/api-reference)
## Next Steps
For CRM sync and webhook automation, see `fathom-core-workflow-b`.
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