refactor: convert 6 skills to commands #3
@ -1,18 +1,9 @@
|
||||
---
|
||||
name: next
|
||||
description: Check Gitea for open issues and surface the next task to work on
|
||||
---
|
||||
|
||||
# Backlog - Find Next Task
|
||||
|
||||
## When to Activate This Skill
|
||||
- "/backlog"
|
||||
- "What should I work on?"
|
||||
- "Check for open issues"
|
||||
- "Any tasks to do?"
|
||||
- "What's next?"
|
||||
- "Show me the backlog"
|
||||
|
||||
## Core Workflow
|
||||
|
||||
### Step 1: Detect the current repo
|
||||
@ -1,5 +1,4 @@
|
||||
---
|
||||
name: generate
|
||||
description: Analyze codebase and generate comprehensive PROJECT_PLAN.json task files
|
||||
---
|
||||
|
||||
@ -10,7 +9,7 @@ Creates structured `PROJECT_PLAN.json` files for tracking project work.
|
||||
## Usage
|
||||
|
||||
```
|
||||
/project-plan [type]
|
||||
/project-plan:generate [type]
|
||||
```
|
||||
|
||||
**Types:**
|
||||
@ -175,13 +174,3 @@ Create `PROJECT_PLAN.json` in the project root or relevant subdirectory.
|
||||
- Default: `PROJECT_PLAN.json` in project root
|
||||
- For monorepos: `{subproject}/PROJECT_PLAN.json`
|
||||
- For focused work: `{directory}/PROJECT_PLAN.json`
|
||||
|
||||
## Example Invocations
|
||||
|
||||
```
|
||||
/project-plan # Default refactoring analysis
|
||||
/project-plan refactoring # Technical debt focus
|
||||
/project-plan feature # Feature implementation plan
|
||||
/project-plan audit # Security/a11y audit
|
||||
/project-plan --output=frontend # Save to frontend/PROJECT_PLAN.json
|
||||
```
|
||||
@ -1,8 +1,6 @@
|
||||
---
|
||||
name: save
|
||||
allowed-tools: Read,Write,Edit,Glob,Grep,Bash
|
||||
description: Save documentation to the knowledge base with proper frontmatter
|
||||
user-invocable: true
|
||||
allowed-tools: Read,Write,Edit,Glob,Grep,Bash
|
||||
---
|
||||
|
||||
Save learnings, fixes, release notes, and other documentation to the claude-home knowledge base. Files are auto-committed and pushed by the `sync-kb` systemd timer (every 2 hours), which triggers kb-rag reindexing.
|
||||
@ -70,7 +68,7 @@ Save to `/mnt/NV2/Development/claude-home/{domain}/`. The file will be auto-comm
|
||||
|
||||
## Examples
|
||||
|
||||
See `examples/` in this skill directory for templates of each document type:
|
||||
See `examples/` in this plugin directory for templates of each document type:
|
||||
- `examples/troubleshooting.md` — Bug fix / incident resolution
|
||||
- `examples/release-notes.md` — Deployment / release changelog
|
||||
- `examples/guide.md` — How-to / setup guide
|
||||
164
plugins/youtube-transcriber/commands/transcribe.md
Normal file
164
plugins/youtube-transcriber/commands/transcribe.md
Normal file
@ -0,0 +1,164 @@
|
||||
---
|
||||
description: Transcribe YouTube videos of any length using GPT-4o-mini-transcribe
|
||||
---
|
||||
|
||||
# YouTube Transcriber - High-Quality Video Transcription
|
||||
|
||||
## Script Location
|
||||
**Primary script**: `$YOUTUBE_TRANSCRIBER_DIR/transcribe.py`
|
||||
|
||||
## Key Features
|
||||
- **Parallel processing**: Multiple videos can be transcribed simultaneously
|
||||
- **Unlimited length**: Auto-chunks videos >10 minutes to prevent API limits
|
||||
- **Organized output**:
|
||||
- Transcripts → `output/` directory
|
||||
- Temp files → `temp/` directory (auto-cleaned)
|
||||
- **High quality**: Uses GPT-4o-mini-transcribe by default (OpenAI's recommended model)
|
||||
- **Cost options**: Can use `-m gpt-4o-transcribe` for the full-size model at 2x cost
|
||||
|
||||
## Basic Usage
|
||||
|
||||
### Single Video
|
||||
```bash
|
||||
cd $YOUTUBE_TRANSCRIBER_DIR
|
||||
uv run python transcribe.py "https://youtube.com/watch?v=VIDEO_ID"
|
||||
```
|
||||
|
||||
**Output**: `output/Video_Title_2025-11-10.txt`
|
||||
|
||||
### Multiple Videos in Parallel
|
||||
```bash
|
||||
cd $YOUTUBE_TRANSCRIBER_DIR
|
||||
|
||||
# Launch all in background simultaneously
|
||||
uv run python transcribe.py "URL1" &
|
||||
uv run python transcribe.py "URL2" &
|
||||
uv run python transcribe.py "URL3" &
|
||||
wait
|
||||
```
|
||||
|
||||
**Why parallel works**: Each transcription uses unique temp files (UUID-based) in `temp/` directory.
|
||||
|
||||
### Higher Quality Mode
|
||||
```bash
|
||||
cd $YOUTUBE_TRANSCRIBER_DIR
|
||||
uv run python transcribe.py "URL" -m gpt-4o-transcribe
|
||||
```
|
||||
|
||||
**When to use full model**: Noisy audio, critical accuracy requirements. Costs 2x more ($0.006/min vs $0.003/min).
|
||||
|
||||
## Command Options
|
||||
|
||||
```bash
|
||||
uv run python transcribe.py [URL] [OPTIONS]
|
||||
|
||||
Options:
|
||||
-o, --output PATH Custom output filename (default: auto-generated in output/)
|
||||
-m, --model MODEL Transcription model (default: gpt-4o-mini-transcribe)
|
||||
Options: gpt-4o-mini-transcribe, gpt-4o-transcribe, whisper-1
|
||||
-p, --prompt TEXT Context prompt for better accuracy
|
||||
--chunk-duration MINUTES Chunk size for long videos (default: 10 minutes)
|
||||
--keep-audio Keep temp audio files (default: auto-delete)
|
||||
```
|
||||
|
||||
## Workflow for User Requests
|
||||
|
||||
### Single Video Request
|
||||
1. Change to transcriber directory
|
||||
2. Run script with URL
|
||||
3. Report output file location in `output/` directory
|
||||
|
||||
### Multiple Video Request
|
||||
1. Change to transcriber directory
|
||||
2. Launch all transcriptions in parallel using background processes
|
||||
3. Wait for all to complete
|
||||
4. Report all output files in `output/` directory
|
||||
|
||||
### Testing/Cost-Conscious Request
|
||||
1. Default model (`gpt-4o-mini-transcribe`) is already the cheapest GPT-4o option
|
||||
2. For even cheaper: suggest Groq's Whisper API as an alternative
|
||||
3. Quality is excellent for most YouTube content
|
||||
|
||||
## Technical Details
|
||||
|
||||
**How it works**:
|
||||
1. Downloads audio from YouTube (via yt-dlp)
|
||||
2. Saves to unique temp file: `temp/download_{UUID}.mp3`
|
||||
3. Splits long videos (>10 min) into chunks automatically
|
||||
4. Transcribes with OpenAI API (GPT-4o-mini-transcribe)
|
||||
5. Saves transcript: `output/Video_Title_YYYY-MM-DD.txt`
|
||||
6. Cleans up temp files automatically
|
||||
|
||||
**Parallel safety**:
|
||||
- Each process uses UUID-based temp files
|
||||
- No file conflicts between parallel processes
|
||||
- Temp files auto-cleaned after completion
|
||||
|
||||
**Auto-chunking**:
|
||||
- Videos >10 minutes: Split into 10-minute chunks
|
||||
- Context preserved between chunks
|
||||
- Prevents API response truncation
|
||||
|
||||
## Requirements
|
||||
- OpenAI API key: `$OPENAI_API_KEY` environment variable
|
||||
- Python 3.10+ with uv package manager
|
||||
- FFmpeg (for audio processing)
|
||||
- yt-dlp (for YouTube downloads)
|
||||
|
||||
**Check requirements**:
|
||||
```bash
|
||||
echo $OPENAI_API_KEY # Should show API key
|
||||
which ffmpeg # Should show path
|
||||
```
|
||||
|
||||
## Cost Estimates
|
||||
|
||||
Default model (`gpt-4o-mini-transcribe` at $0.003/min):
|
||||
|
||||
- **5-minute video**: ~$0.015
|
||||
- **25-minute video**: ~$0.075
|
||||
- **60-minute video**: ~$0.18
|
||||
|
||||
Full model (`gpt-4o-transcribe` at $0.006/min):
|
||||
|
||||
- **5-minute video**: ~$0.03
|
||||
- **25-minute video**: ~$0.15
|
||||
- **60-minute video**: ~$0.36
|
||||
|
||||
## Quick Reference
|
||||
|
||||
```bash
|
||||
# Single video (default quality)
|
||||
uv run python transcribe.py "URL"
|
||||
|
||||
# Single video (higher quality, 2x cost)
|
||||
uv run python transcribe.py "URL" -m gpt-4o-transcribe
|
||||
|
||||
# Multiple videos in parallel
|
||||
for url in URL1 URL2 URL3; do
|
||||
uv run python transcribe.py "$url" &
|
||||
done
|
||||
wait
|
||||
|
||||
# With custom output
|
||||
uv run python transcribe.py "URL" -o custom_name.txt
|
||||
|
||||
# With context prompt
|
||||
uv run python transcribe.py "URL" -p "Context about video content"
|
||||
```
|
||||
|
||||
## Integration
|
||||
|
||||
**With fabric**: Process transcripts after generation
|
||||
```bash
|
||||
cat output/Video_Title_2025-11-10.txt | fabric -p extract_wisdom
|
||||
```
|
||||
|
||||
## Notes
|
||||
|
||||
- Script requires being in its directory to work correctly
|
||||
- Always change to `$YOUTUBE_TRANSCRIBER_DIR` first
|
||||
- Parallel execution is safe and recommended for multiple videos
|
||||
- Default model (gpt-4o-mini-transcribe) is recommended for most content
|
||||
- Output files automatically named with video title + date
|
||||
- Temp files automatically cleaned after transcription
|
||||
@ -1,263 +0,0 @@
|
||||
---
|
||||
name: transcribe
|
||||
description: Transcribe YouTube videos of any length using GPT-4o-transcribe
|
||||
---
|
||||
|
||||
# YouTube Transcriber - High-Quality Video Transcription
|
||||
|
||||
## When to Activate This Skill
|
||||
- "Transcribe this YouTube video"
|
||||
- "Get a transcript of [URL]"
|
||||
- "Transcribe these videos" (multiple URLs)
|
||||
- User provides YouTube URL(s) needing transcription
|
||||
- "Extract text from video"
|
||||
- Any request involving YouTube video transcription
|
||||
|
||||
## Script Location
|
||||
**Primary script**: `$YOUTUBE_TRANSCRIBER_DIR/transcribe.py`
|
||||
|
||||
## Key Features
|
||||
- **Parallel processing**: Multiple videos can be transcribed simultaneously
|
||||
- **Unlimited length**: Auto-chunks videos >10 minutes to prevent API limits
|
||||
- **Organized output**:
|
||||
- Transcripts → `output/` directory
|
||||
- Temp files → `temp/` directory (auto-cleaned)
|
||||
- **High quality**: Uses GPT-4o-transcribe by default (reduced hallucinations)
|
||||
- **Cost options**: Can use `-m gpt-4o-mini-transcribe` for 50% cost savings
|
||||
|
||||
## Basic Usage
|
||||
|
||||
### Single Video
|
||||
```bash
|
||||
cd $YOUTUBE_TRANSCRIBER_DIR
|
||||
uv run python transcribe.py "https://youtube.com/watch?v=VIDEO_ID"
|
||||
```
|
||||
|
||||
**Output**: `output/Video_Title_2025-11-10.txt`
|
||||
|
||||
### Multiple Videos in Parallel
|
||||
```bash
|
||||
cd $YOUTUBE_TRANSCRIBER_DIR
|
||||
|
||||
# Launch all in background simultaneously
|
||||
uv run python transcribe.py "URL1" &
|
||||
uv run python transcribe.py "URL2" &
|
||||
uv run python transcribe.py "URL3" &
|
||||
wait
|
||||
```
|
||||
|
||||
**Why parallel works**: Each transcription uses unique temp files (UUID-based) in `temp/` directory.
|
||||
|
||||
### Cost-Saving Mode
|
||||
```bash
|
||||
cd $YOUTUBE_TRANSCRIBER_DIR
|
||||
uv run python transcribe.py "URL" -m gpt-4o-mini-transcribe
|
||||
```
|
||||
|
||||
**When to use mini**: Testing, casual content, bulk processing. Quality is the same as gpt-4o-transcribe but ~50% cheaper.
|
||||
|
||||
## Command Options
|
||||
|
||||
```bash
|
||||
uv run python transcribe.py [URL] [OPTIONS]
|
||||
|
||||
Options:
|
||||
-o, --output PATH Custom output filename (default: auto-generated in output/)
|
||||
-m, --model MODEL Transcription model (default: gpt-4o-transcribe)
|
||||
Options: gpt-4o-transcribe, gpt-4o-mini-transcribe, whisper-1
|
||||
-p, --prompt TEXT Context prompt for better accuracy
|
||||
--chunk-duration MINUTES Chunk size for long videos (default: 10 minutes)
|
||||
--keep-audio Keep temp audio files (default: auto-delete)
|
||||
```
|
||||
|
||||
## Workflow for User Requests
|
||||
|
||||
### Single Video Request
|
||||
1. Change to transcriber directory
|
||||
2. Run script with URL
|
||||
3. Report output file location in `output/` directory
|
||||
|
||||
### Multiple Video Request
|
||||
1. Change to transcriber directory
|
||||
2. Launch all transcriptions in parallel using background processes
|
||||
3. Wait for all to complete
|
||||
4. Report all output files in `output/` directory
|
||||
|
||||
### Testing/Cost-Conscious Request
|
||||
1. Always use `-m gpt-4o-mini-transcribe` for testing
|
||||
2. Mention cost savings to user
|
||||
3. Quality is identical to full model
|
||||
|
||||
## Example Responses
|
||||
|
||||
**User**: "Transcribe this video: https://youtube.com/watch?v=abc123"
|
||||
|
||||
**Assistant Action**:
|
||||
```bash
|
||||
cd $YOUTUBE_TRANSCRIBER_DIR
|
||||
uv run python transcribe.py "https://youtube.com/watch?v=abc123"
|
||||
```
|
||||
|
||||
**Report**: "✅ Transcript saved to `output/Video_Title_2025-11-10.txt`"
|
||||
|
||||
---
|
||||
|
||||
**User**: "Transcribe these 5 videos: [URL1] [URL2] [URL3] [URL4] [URL5]"
|
||||
|
||||
**Assistant Action**: Launch all 5 in parallel:
|
||||
```bash
|
||||
cd $YOUTUBE_TRANSCRIBER_DIR
|
||||
uv run python transcribe.py "URL1" &
|
||||
uv run python transcribe.py "URL2" &
|
||||
uv run python transcribe.py "URL3" &
|
||||
uv run python transcribe.py "URL4" &
|
||||
uv run python transcribe.py "URL5" &
|
||||
wait
|
||||
```
|
||||
|
||||
**Report**: "✅ All 5 videos transcribed successfully in parallel. Output files in `output/` directory"
|
||||
|
||||
## Technical Details
|
||||
|
||||
**How it works**:
|
||||
1. Downloads audio from YouTube (via yt-dlp)
|
||||
2. Saves to unique temp file: `temp/download_{UUID}.mp3`
|
||||
3. Splits long videos (>10 min) into chunks automatically
|
||||
4. Transcribes with OpenAI API (GPT-4o-transcribe)
|
||||
5. Saves transcript: `output/Video_Title_YYYY-MM-DD.txt`
|
||||
6. Cleans up temp files automatically
|
||||
|
||||
**Parallel safety**:
|
||||
- Each process uses UUID-based temp files
|
||||
- No file conflicts between parallel processes
|
||||
- Temp files auto-cleaned after completion
|
||||
|
||||
**Auto-chunking**:
|
||||
- Videos >10 minutes: Split into 10-minute chunks
|
||||
- Context preserved between chunks
|
||||
- Prevents API response truncation
|
||||
|
||||
## Requirements
|
||||
- OpenAI API key: `$OPENAI_API_KEY` environment variable
|
||||
- Python 3.10+ with uv package manager
|
||||
- FFmpeg (for audio processing)
|
||||
- yt-dlp (for YouTube downloads)
|
||||
|
||||
**Check requirements**:
|
||||
```bash
|
||||
echo $OPENAI_API_KEY # Should show API key
|
||||
which ffmpeg # Should show path
|
||||
```
|
||||
|
||||
## Output Format
|
||||
|
||||
Transcripts are saved as plain text with metadata:
|
||||
```
|
||||
================================================================================
|
||||
YouTube Video Transcript (Long Video)
|
||||
================================================================================
|
||||
|
||||
Title: Video Title Here
|
||||
Uploader: Channel Name
|
||||
Duration: 45m 32s
|
||||
URL: https://youtube.com/watch?v=VIDEO_ID
|
||||
|
||||
================================================================================
|
||||
|
||||
[Full transcript text with proper punctuation...]
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Always use parallel for multiple videos** - It's 6x faster
|
||||
2. **Use mini model for testing** - Same quality, half the cost
|
||||
3. **Check output/ directory** - All transcripts organized there
|
||||
4. **Temp files auto-clean** - No manual cleanup needed
|
||||
5. **Add context prompts for technical content**:
|
||||
```bash
|
||||
uv run python transcribe.py "URL" \
|
||||
-p "Technical discussion about Docker, Kubernetes, microservices"
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
**API Key Missing**:
|
||||
```bash
|
||||
export OPENAI_API_KEY="sk-proj-your-key-here"
|
||||
```
|
||||
|
||||
**FFmpeg Not Found**:
|
||||
```bash
|
||||
sudo dnf install ffmpeg # Fedora/Nobara
|
||||
```
|
||||
|
||||
**Parallel Conflicts** (shouldn't happen with UUID temps):
|
||||
- Each process creates unique temp file in `temp/`
|
||||
- If issues occur, check `temp/` directory permissions
|
||||
|
||||
## Cost Estimates (as of March 2025)
|
||||
|
||||
- **5-minute video**: $0.10 - $0.20
|
||||
- **25-minute video**: $0.50 - $1.00
|
||||
- **60-minute video**: $1.20 - $2.40
|
||||
|
||||
**Using mini model**: Reduce costs by ~50%
|
||||
|
||||
## Quick Reference
|
||||
|
||||
```bash
|
||||
# Single video (default quality)
|
||||
uv run python transcribe.py "URL"
|
||||
|
||||
# Single video (cost-saving)
|
||||
uv run python transcribe.py "URL" -m gpt-4o-mini-transcribe
|
||||
|
||||
# Multiple videos in parallel
|
||||
for url in URL1 URL2 URL3; do
|
||||
uv run python transcribe.py "$url" &
|
||||
done
|
||||
wait
|
||||
|
||||
# With custom output
|
||||
uv run python transcribe.py "URL" -o custom_name.txt
|
||||
|
||||
# With context prompt
|
||||
uv run python transcribe.py "URL" -p "Context about video content"
|
||||
```
|
||||
|
||||
## Directory Structure
|
||||
|
||||
```
|
||||
$YOUTUBE_TRANSCRIBER_DIR/
|
||||
├── transcribe.py # Main script
|
||||
├── temp/ # Temporary audio files (auto-cleaned)
|
||||
├── output/ # All transcripts saved here
|
||||
├── README.md # Full documentation
|
||||
└── pyproject.toml # Dependencies
|
||||
```
|
||||
|
||||
## Integration with Other Skills
|
||||
|
||||
**With fabric skill**: Process transcripts after generation
|
||||
```bash
|
||||
# 1. Transcribe
|
||||
uv run python transcribe.py "URL"
|
||||
|
||||
# 2. Process with fabric
|
||||
cat output/Video_Title_2025-11-10.txt | fabric -p extract_wisdom
|
||||
```
|
||||
|
||||
**With research skill**: Transcribe source videos for research
|
||||
```bash
|
||||
# Transcribe multiple research videos in parallel
|
||||
# Then analyze transcripts for insights
|
||||
```
|
||||
|
||||
## Notes
|
||||
|
||||
- Script requires being in its directory to work correctly
|
||||
- Always change to `$YOUTUBE_TRANSCRIBER_DIR` first
|
||||
- Parallel execution is safe and recommended for multiple videos
|
||||
- Use mini model for testing to save costs
|
||||
- Output files automatically named with video title + date
|
||||
- Temp files automatically cleaned after transcription
|
||||
@ -1,18 +1,10 @@
|
||||
---
|
||||
name: generate
|
||||
description: Generate images from text prompts using local GPU inference
|
||||
allowed-tools: Bash(z-image:*)
|
||||
---
|
||||
|
||||
# Z-Image - Local AI Image Generation
|
||||
|
||||
## When to Activate This Skill
|
||||
- "Generate an image of..."
|
||||
- "Create a picture of..."
|
||||
- "Make me an image"
|
||||
- "z-image [prompt]"
|
||||
- User describes something visual they want generated
|
||||
|
||||
## Tool
|
||||
|
||||
**Binary:** `z-image` (in PATH via `~/bin/z-image`)
|
||||
Loading…
Reference in New Issue
Block a user