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name: echoloop description: > Real-time AI meeting copilot. Dual audio capture (system + mic), live transcription (faster-whisper local or Deepgram cloud), LLM coaching loop (Claude/GPT every ~35s), always-on-top overlay, session logging with markdown export. Use when the user says "meeting recap", "meeting copilot", "live transcription", "meeting insights", "coaching during meeting", or "record this call". version: 1.0.0 author: Pete Woodbridge (EchoLoop) tags:

• meetings
• transcription
• audio
• coaching
• real-time
• whisper
• deepgram env_vars:
• ANTHROPIC_API_KEY
• OPENAI_API_KEY
• DEEPGRAM_API_KEY
• ECHOLOOP_SYSTEM_DEVICE
• ECHOLOOP_MIC_DEVICE

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EchoLoop — Real-Time Meeting Copilot

Live meeting transcription + AI coaching. Captures dual audio (system speakers + microphone), transcribes in real-time, and feeds rolling transcript to Claude or GPT for tactical insights every ~35 seconds.

When to Use This Skill

• Live meeting coaching: Real-time tactical advice during calls
• Meeting transcription: Dual-stream (you + them) labelled transcript
• Meeting recap: Session logs with markdown export
• Negotiation support: Reads the room and suggests responses
• Multi-user audio: Works with our VNC desktop + virtual audio cables

When Not to Use

• For pre-recorded audio/video processing — use ffmpeg-processing
• For generating podcasts from text — use notebooklm (audio overviews)
• For video production — use open-montage
• For browser-based meeting automation — use playwright

Architecture

┌───────────────┐     ┌──────────────┐     ┌──────────────┐     ┌────────────┐
│ Audio Capture  │────>│  Transcriber │────>│   EchoLoop   │────>│     UI     │
│ (2 threads)    │     │ (thread pool)│     │   Engine     │     │  (Tkinter) │
│                │     │              │     │  (asyncio)   │     │            │
│ system ──┐     │     │faster-whisper│     │              │     │ Always-on- │
│ mic ─────┤     │     │  or Deepgram │     │ Claude / GPT │     │ top overlay│
│          v     │     │              │     │ every ~35s   │     │            │
│  chunk queue   │     │  seg queue   │     │ + on silence │     │ insight log│
└───────────────┘     └──────────────┘     └──────────────┘     └────────────┘

Quick Start

# Install dependencies
pip install -r requirements.txt   # from the echoloop skill directory

# Set API key (uses existing container keys)
export ANTHROPIC_API_KEY="$ANTHROPIC_API_KEY"  # Already set for devuser

# Configure audio devices (container VNC)
export ECHOLOOP_SYSTEM_DEVICE="pulse"     # PulseAudio virtual cable
export ECHOLOOP_MIC_DEVICE=""             # Default mic

# Launch on VNC Display :1
DISPLAY=:1 python3 -m echoloop

Container Audio Routing

Our container has PulseAudio on VNC Display :1. For meeting capture:

1. Virtual audio cable: Route system audio through PulseAudio monitor
2. Set device: ECHOLOOP_SYSTEM_DEVICE=<pulse monitor name>
3. Launch: DISPLAY=:1 python3 main.py # from the echoloop skill directory

For multi-user scenarios, each user can run their own EchoLoop instance with different audio routing.

Environment Variables

Variable	Default	Description
ANTHROPIC_API_KEY	(required)	Claude API key (already set in container)
OPENAI_API_KEY		GPT API key (for OpenAI provider)
DEEPGRAM_API_KEY		Deepgram key (for cloud transcription)
ECHOLOOP_LLM_PROVIDER	anthropic	LLM provider: anthropic or openai
ECHOLOOP_TRANSCRIBER	local	Transcription: local (faster-whisper) or deepgram
ECHOLOOP_WHISPER_MODEL	base.en	Whisper model size
ECHOLOOP_WHISPER_DEVICE	cpu	cpu or cuda for GPU transcription
ECHOLOOP_PUSH_INTERVAL	35	Seconds between LLM coaching calls
ECHOLOOP_SILENCE_TRIGGER	4.0	Seconds of silence before early push
ECHOLOOP_SYSTEM_DEVICE		System audio device name substring
ECHOLOOP_MIC_DEVICE		Microphone device (None = default)
ECHOLOOP_MEETING_CONTEXT		One-line meeting briefing for sharper advice
ECHOLOOP_LOG_DIR		Directory for session logs (empty = disabled)
ECHOLOOP_OPACITY	0.88	Overlay window opacity

Features

• Dual audio capture: System audio + microphone, labelled [THEM] and [ME]
• Transcription: Local faster-whisper (CPU/CUDA) or Deepgram cloud
• LLM coaching: Claude or GPT every ~35s with nudge button for instant advice
• Silence detection: Skips dead air, saves CPU and API calls
• Session logging: Transcript + insights saved to disk, markdown export
• Auto-reconnect: Audio streams recover if device disconnects
• Pause/Resume: Click or Ctrl+Shift+E global hotkey
• Live stats: Insight count, session duration, talk-time percentage

Integration with Other Skills

Skill	Relationship
notebooklm	Feed meeting transcripts as sources, generate audio overviews
report-builder	Generate meeting reports from session logs
perplexity-research	Research topics mentioned in meetings in real-time
codex-companion	Delegate technical questions from meetings to Codex

Attribution

EchoLoop by Pete Woodbridge. MIT License.