smithery/jeremylongshore

twinmind-core-workflow-a

Execute TwinMind primary workflow: Meeting transcription and summary generation. Use when implementing meeting capture, building transcription features, or automating meeting documentation. Trigger with phrases like "twinmind transcription workflow", "meeting transcription", "capture meeting with twinmind". '

Installation

$ npx skills add smithery/jeremylongshore --skill twinmind-core-workflow-a

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More details

Agent compatibility

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Skill metadata

Parsed from SKILL.md frontmatter.

Version1.13.0
LicenseMIT
CompatibilityDesigned for Claude Code
Allowed toolsRead, Write, Edit, Bash(npm:*), Grep
Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,570 B
  • docs SUMMARY.md 344 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

TwinMind Core Workflow A: Meeting Transcription & Summary

Contents

  • [Overview](#overview)
  • [Prerequisites](#prerequisites)
  • [Instructions](#instructions)
  • [Output](#output)
  • [Error Handling](#error-handling)
  • [Examples](#examples)
  • [Resources](#resources)

Overview

Primary workflow for capturing meetings, generating transcripts with speaker diarization, and creating AI summaries with action items.

Prerequisites

  • Completed twinmind-install-auth setup
  • TwinMind Pro/Enterprise for API access
  • Valid API credentials configured
  • Audio source available (live or file)

Instructions

Step 1: Initialize Meeting Capture

Build a MeetingCapture class with startLiveCapture() for real-time recording and transcribeRecording() for file-based transcription. Use Ear-3 model with auto language detection and speaker diarization.

Step 2: Generate AI Summary

Create a SummaryGenerator with generateSummary() (brief/detailed/bullet-points formats), generateFollowUpEmail(), and generateMeetingNotes() methods.

Step 3: Handle Speaker Identification

Build a SpeakerManager that extracts speakers from transcript segments, calculates speaking time per speaker, and optionally matches speakers to calendar attendees.

Step 4: Orchestrate Complete Workflow

Wire everything together in processMeeting(): transcribe audio, then generate summary and identify speakers in parallel, optionally produce follow-up email and meeting notes.

See detailed implementation for complete MeetingCapture, SummaryGenerator, SpeakerManager, and orchestration code.

Output

  • Complete meeting transcript with timestamps
  • Speaker-labeled segments
  • AI-generated summary
  • Extracted action items with assignees
  • Optional follow-up email draft
  • Optional formatted meeting notes

Error Handling

Error Cause Solution
Transcription timeout Large audio file Increase maxWaitMs or use async callback
Speaker match failed No calendar data Provide attendees list manually
Summary generation failed Transcript too short Ensure minimum 30s of audio
Audio format unsupported Wrong codec Convert to MP3/WAV/M4A
Rate limit exceeded Too many requests Implement queue-based processing

Examples

Basic usage: Apply twinmind core workflow a to a standard project setup with default configuration options.

Advanced scenario: Customize twinmind core workflow a for production environments with multiple constraints and team-specific requirements.

Audio Format Support

Format Supported Notes
MP3 Yes Recommended
WAV Yes Best quality
M4A Yes iOS recordings
WebM Yes Browser recordings

Resources

  • TwinMind Transcription API
  • Ear-3 Model Details
  • Audio Format Guide

Next Steps

For action item extraction and follow-up automation, see twinmind-core-workflow-b.