refactor: convert 5 more skills to commands, update transcriber defaults

Convert backlog, project-plan, save-doc, youtube-transcriber, and
z-image from skills/ to commands/ so they appear as user-invocable
slash commands with plugin name prefixes.

Update youtube-transcriber: switch default model from gpt-4o-transcribe
to gpt-4o-mini-transcribe (OpenAI's current recommendation, half cost)
and fix cost estimates that were 4-7x too high.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

plugins/backlog/commands/next.md

@@ -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

plugins/project-plan/commands/generate.md

@@ -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
-```

plugins/save-doc/commands/save.md

@@ -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

plugins/youtube-transcriber/commands/transcribe.md

@@ -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

plugins/youtube-transcriber/skills/transcribe/SKILL.md

@@ -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

plugins/z-image/commands/generate.md

@@ -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`)