Transcribe any audio or video to text using OpenAI's Whisper model - the same technology powering ChatGPT voice features.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionwhisper-transcriptionExecute the skills CLI command in your project's root directory to begin installation:
Fetches whisper-transcription from guia-matthieu/clawfu-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate whisper-transcription. Access via /whisper-transcription in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
61
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
61
stars
Transcribe any audio or video to text using OpenAI's Whisper model - the same technology powering ChatGPT voice features.
| Claude Does | You Decide |
|---|---|
| Structures production workflow | Final creative direction |
| Suggests technical approaches | Equipment and tool choices |
| Creates templates and checklists | Quality standards |
| Identifies best practices | Brand/voice decisions |
| Generates script outlines | Final script approval |
pip install openai-whisper torch ffmpeg-python click
# Also requires ffmpeg installed on system
# macOS: brew install ffmpeg
# Ubuntu: sudo apt install ffmpeg
python scripts/main.py transcribe audio.mp3 --model medium --output transcript.txt
python scripts/main.py transcribe video.mp4 --format srt --output subtitles.srt
python scripts/main.py batch ./recordings/ --format txt --output ./transcripts/
python scripts/main.py translate foreign-audio.mp3 --to en
python scripts/main.py timestamps podcast.mp3 --format json
# Transcribe 1-hour podcast
python scripts/main.py transcribe episode-42.mp3 --model medium
# Output: episode-42.txt (full transcript with timestamps)
# Processing time: ~5 min for 1 hour audio on M1 Mac
# Generate SRT for video upload
python scripts/main.py transcribe marketing-video.mp4 --format srt
# Output: marketing-video.srt
# Upload directly to YouTube/Vimeo
# Transcribe all recordings in folder
python scripts/main.py batch ./customer-interviews/ --model small --format txt
# Output: ./customer-interviews/*.txt (one per audio file)
| Model | Speed | Accuracy | VRAM | Best For |
|---|---|---|---|---|
tiny |
Fastest | ~70% | 1GB | Quick drafts, short clips |
base |
Fast | ~80% | 1GB | Social media clips |
small |
Medium | ~85% | 2GB | Podcasts, interviews |
medium |
Slow | ~90% | 5GB | Professional transcripts |
large |
Slowest | ~95% | 10GB | Critical accuracy needs |
Recommendation: Start with small for most marketing content. Use medium for client deliverables.
| Format | Extension | Use Case |
|---|---|---|
txt |
.txt | Blog posts, analysis |
srt |
.srt | Video subtitles (YouTube) |
vtt |
.vtt | Web video subtitles |
json |
.json | Programmatic access |
tsv |
.tsv | Spreadsheet analysis |
--languagecategory: automation
subcategory: audio-processing
dependencies: [openai-whisper, torch, ffmpeg-python]
difficulty: beginner
time_saved: 10+ hours/week
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
whisper-transcription fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added whisper-transcription from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
whisper-transcription reduced setup friction for our internal harness; good balance of opinion and flexibility.
whisper-transcription is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for whisper-transcription matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: whisper-transcription is the kind of skill you can hand to a new teammate without a long onboarding doc.
whisper-transcription has been reliable in day-to-day use. Documentation quality is above average for community skills.
whisper-transcription reduced setup friction for our internal harness; good balance of opinion and flexibility.
whisper-transcription fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
whisper-transcription reduced setup friction for our internal harness; good balance of opinion and flexibility.
showing 1-10 of 71