whisper-transcription▌
guia-matthieu/clawfu-skills · updated Apr 8, 2026
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Transcribe any audio or video to text using OpenAI's Whisper model - the same technology powering ChatGPT voice features.
Whisper Transcription
Transcribe any audio or video to text using OpenAI's Whisper model - the same technology powering ChatGPT voice features.
When to Use This Skill
- Podcast repurposing - Convert episodes to blog posts, show notes, social snippets
- Video subtitles - Generate SRT/VTT files for YouTube, social media
- Interview extraction - Pull quotes and insights from recorded calls
- Content audit - Make audio/video libraries searchable
- Translation - Transcribe and translate foreign language content
What Claude Does vs What You Decide
| 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 |
Dependencies
pip install openai-whisper torch ffmpeg-python click
# Also requires ffmpeg installed on system
# macOS: brew install ffmpeg
# Ubuntu: sudo apt install ffmpeg
Commands
Transcribe Single File
python scripts/main.py transcribe audio.mp3 --model medium --output transcript.txt
python scripts/main.py transcribe video.mp4 --format srt --output subtitles.srt
Batch Transcription
python scripts/main.py batch ./recordings/ --format txt --output ./transcripts/
Transcribe + Translate
python scripts/main.py translate foreign-audio.mp3 --to en
Extract Timestamps
python scripts/main.py timestamps podcast.mp3 --format json
Examples
Example 1: Podcast to Blog Post
# 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
Example 2: YouTube Subtitles
# Generate SRT for video upload
python scripts/main.py transcribe marketing-video.mp4 --format srt
# Output: marketing-video.srt
# Upload directly to YouTube/Vimeo
Example 3: Batch Process Interview Library
# 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 Selection Guide
| 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.
Output Formats
| 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 |
Performance Tips
- GPU acceleration - 10x faster with CUDA GPU
- Audio extraction - Script auto-extracts audio from video
- Chunking - Long files auto-split for memory efficiency
- Language detection - Automatic, or specify with
--language
Skill Boundaries
What This Skill Does Well
- Structuring audio production workflows
- Providing technical guidance
- Creating quality checklists
- Suggesting creative approaches
What This Skill Cannot Do
- Replace audio engineering expertise
- Make subjective creative decisions
- Access or edit audio files directly
- Guarantee commercial success
Related Skills
- video-processing - Extract audio from video
- youtube-downloader - Download videos to transcribe
- content-repurposer - Transform transcripts to content
- podcast-production - Create podcasts
Skill Metadata
- Mode: cyborg
category: automation
subcategory: audio-processing
dependencies: [openai-whisper, torch, ffmpeg-python]
difficulty: beginner
time_saved: 10+ hours/week
How to use whisper-transcription on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add whisper-transcription
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches whisper-transcription from GitHub repository guia-matthieu/clawfu-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate whisper-transcription. Access the skill through slash commands (e.g., /whisper-transcription) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★71 reviews- ★★★★★Benjamin Smith· Dec 28, 2024
whisper-transcription fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Alexander Kapoor· Dec 28, 2024
We added whisper-transcription from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kabir Dixit· Dec 24, 2024
whisper-transcription reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Charlotte Nasser· Dec 24, 2024
whisper-transcription is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Dhruvi Jain· Dec 20, 2024
Registry listing for whisper-transcription matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Diya White· Dec 16, 2024
Keeps context tight: whisper-transcription is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Henry Haddad· Dec 12, 2024
whisper-transcription has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Hana Kim· Dec 12, 2024
whisper-transcription reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Charlotte Khanna· Dec 8, 2024
whisper-transcription fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Dev Khanna· Nov 19, 2024
whisper-transcription reduced setup friction for our internal harness; good balance of opinion and flexibility.
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