voice-transcription
Record and transcribe voice input when user wants to speak instead of type, describe complex issues verbally, provide audio input, or dictate text. Use this when user says "record my voice", "let me speak", "voice input", "transcribe audio", or when verbal description would be clearer than typing.
What this skill does
# Voice Transcription Skill
This skill enables local voice transcription using whisper.cpp for privacy-preserving speech-to-text.
## When to Use This Skill
Use this skill when the user:
- Explicitly asks to record voice or use voice input
- Wants to describe something verbally instead of typing
- Needs to transcribe audio
- Says phrases like "let me speak", "record this", "voice input"
- Would benefit from speaking complex information rather than typing
## Automatic Setup
The transcription script now includes:
- **Installation detection** - Checks if VoiceType is properly installed
- **Auto-start** - Automatically starts whisper.cpp server if not running
If the script detects missing installation, it will return JSON with `"installation_needed": true`. When you see this:
1. **Offer to run installation:**
```
"It looks like VoiceType isn't fully installed. Would you like me to run the installer? I can do this with: /voicetype-install"
```
2. **If user agrees, run:**
```bash
bash install.sh
```
Or use the `/voicetype-install` command which provides guided installation.
## Prerequisites (Automatic)
The script automatically handles:
- ✅ **Checks for installation** - Verifies venv, whisper binary, and scripts exist
- ✅ **Starts whisper server** - Auto-starts from `.whisper/bin/` if not running
- ✅ **Downloads model** - First-time use downloads whisper model automatically
You don't need to manually check the server - the script does it!
## How to Transcribe Voice
1. **Run the transcription script:**
```bash
source venv/bin/activate && python skills/voice/scripts/transcribe.py --duration 5
```
The script automatically:
- ✅ Checks installation (offers /voicetype-install if needed)
- ✅ Starts whisper server if not running
- ✅ Records audio from microphone for specified duration (default 5 seconds)
- ✅ Transcribes via local whisper.cpp server (localhost:2022)
- ✅ Returns JSON with transcribed text
2. **Parse the output:**
- Success: `{"text": "transcribed speech", "duration": 5}`
- Installation needed: `{"error": "...", "installation_needed": true, "missing_components": [...], "help": [...]}`
- Transcription error: `{"error": "error message", "help": [...]}`
3. **Handle installation_needed:**
If JSON contains `"installation_needed": true`:
- Inform user: "VoiceType needs to be installed first."
- Offer: "Would you like me to run the installer? Use: /voicetype-install or I can run: bash install.sh"
- Wait for user confirmation before proceeding
## Example Usage Flows
### Scenario 1: Normal Transcription (Installed)
**User:** "Let me record a voice note about the bug I'm seeing"
**Assistant:**
1. Informs user: "I'll record for 5 seconds. Speak when ready..."
2. Runs transcription script (auto-starts server if needed)
3. Receives: `{"text": "The submit button isn't working when I click it on the checkout page"}`
4. Responds: "I transcribed: 'The submit button isn't working when I click it on the checkout page.' Let me help you investigate this issue..."
### Scenario 2: First-Time Use (Not Installed)
**User:** "Record my voice"
**Assistant:**
1. Runs transcription script
2. Receives: `{"error": "VoiceType is not fully installed", "installation_needed": true, "missing_components": ["Python venv", "whisper.cpp binary"]}`
3. Responds: "It looks like VoiceType isn't installed yet. Would you like me to run the installer? I can guide you through it with: /voicetype-install or directly run: bash install.sh"
4. User confirms
5. Runs `/voicetype-install` or `bash install.sh`
6. After installation: "Installation complete! Now let's try voice transcription..."
## Script Options
The transcription script accepts optional parameters:
- `--duration N` - Record for N seconds (1-30, default 5)
- Example: `python skills/voice/scripts/transcribe.py --duration 10`
## Troubleshooting
If transcription fails:
1. **Check microphone access:**
```bash
python -c "import sounddevice as sd; print(sd.query_devices())"
```
2. **Verify whisper server:**
```bash
systemctl --user status whisper-server
journalctl --user -u whisper-server -n 20
```
3. **Test the script directly:**
```bash
cd /path/to/voicetype
source venv/bin/activate
python skills/voice/scripts/transcribe.py
```
## Privacy Note
All voice processing happens locally:
- Audio recorded via sounddevice (local microphone)
- Transcription via whisper.cpp server (localhost only)
- No data sent to cloud services
- Audio files are temporary and deleted after transcription
Related in Image & Video
watch
IncludedWatch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
physical-ai-defect-image-generation
IncludedUse when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
accelint-react-best-practices
IncludedReact performance optimization and best practices. ALWAYS use this skill when working with any React code - writing components, hooks, JSX; refactoring; optimizing re-renders, memoization, state management; reviewing for performance; fixing hydration mismatches; debugging infinite re-renders, stale closures, input focus loss, animations restarting; preventing remounting; implementing transitions, lazy initialization, effect dependencies. Even simple React tasks benefit from these patterns. Covers React 19+ (useEffectEvent, Activity, ref props). Triggers - useEffect, useState, useMemo, useCallback, memo, inline components, nested components, components inside components, re-render, performance, hydration, SSR, Next.js, useDeferredValue, combined hooks.
elevenlabs-agents
IncludedBuild conversational AI voice agents with ElevenLabs Platform using React, JavaScript, React Native, or Swift SDKs. Configure agents, tools (client/server/MCP), RAG knowledge bases, multi-voice, and Scribe real-time STT. Use when: building voice chat interfaces, implementing AI phone agents with Twilio, configuring agent workflows or tools, adding RAG knowledge bases, testing with CLI "agents as code", or troubleshooting deprecated @11labs packages, Android audio cutoff, CSP violations, dynamic variables, or WebRTC config. Keywords: ElevenLabs Agents, ElevenLabs voice agents, AI voice agents, conversational AI, @elevenlabs/react, @elevenlabs/client, @elevenlabs/react-native, @elevenlabs/elevenlabs-js, @elevenlabs/agents-cli, elevenlabs SDK, voice AI, TTS, text-to-speech, ASR, speech recognition, turn-taking model, WebRTC voice, WebSocket voice, ElevenLabs conversation, agent system prompt, agent tools, agent knowledge base, RAG voice agents, multi-voice agents, pronunciation dictionary, voice speed control, elevenlabs scribe, @11labs deprecated, Android audio cutoff, CSP violation elevenlabs, dynamic variables elevenlabs, case-sensitive tool names, webhook authentication
humanizer
IncludedHumanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
generating-mermaid-diagrams
IncludedSalesforce architecture diagrams using Mermaid with ASCII fallback. Use this skill when generating text-based diagrams for Salesforce architecture, OAuth flows, ERDs, integration sequences, or Agentforce structure. TRIGGER when: user says "diagram", "visualize", "ERD", or asks for sequence diagrams, flowcharts, class diagrams, or architecture visualizations in Mermaid. DO NOT TRIGGER when: user wants PNG/SVG image output (use generating-visual-diagrams), or asks about non-Salesforce systems.