loom-workflow

# Loom Workflow Analyzer

Transforms Loom recordings into structured, automatable workflows.

## Quick Start

```bash
# Full pipeline - download, extract, transcribe, analyze
{baseDir}/scripts/loom-workflow analyze https://loom.com/share/abc123

# Individual steps
{baseDir}/scripts/loom-workflow download https://loom.com/share/abc123
{baseDir}/scripts/loom-workflow extract ./video.mp4
{baseDir}/scripts/loom-workflow generate ./analysis.json
```

## Pipeline

1. **Download** - Fetches Loom video via yt-dlp
2. **Smart Extract** - Captures frames at scene changes + transcript timing
3. **Transcribe** - Whisper transcription with word-level timestamps
4. **Analyze** - Multimodal AI analysis (requires vision model)
5. **Generate** - Creates Lobster workflow with approval gates

## Smart Frame Extraction

Frames are captured when:
- **Scene changes** - Significant visual change (ffmpeg scene detection)
- **Speech starts** - New narration segment begins
- **Combined** - Speech + visual change = high-value moment
- **Gap fill** - Max 10s without a frame

## Analysis Output

The analyzer produces:
- `workflow-analysis.json` - Structured workflow definition
- `workflow-summary.md` - Human-readable summary
- `*.lobster` - Executable Lobster workflow file

### Ambiguity Detection

The analyzer flags:
- Unclear mouse movements
- Implicit knowledge ("the usual process")
- Decision points ("depending on...")
- Missing credentials/context
- Tool dependencies

## Vision Analysis Step

After extraction, use the generated prompt with a vision model:

```bash
# The prompt is at: output/workflow-analysis-prompt.md
# Attach frames from: output/frames/

# Example with Claude:
cat output/workflow-analysis-prompt.md | claude --images output/frames/*.jpg
```

Save the JSON response to `workflow-analysis.json`, then:

```bash
{baseDir}/scripts/loom-workflow generate ./output/workflow-analysis.json
```

## Lobster Integration

Generated workflows use:
- `approve` gates for destructive/external...