AI-native workflow analyzer for Loom recordings. Breaks down recorded business processes into structured, automatable workflows. Use when: - Analyzing Loom videos to understand workflows - Extracting steps, tools, and decision points from screen recordings - Generating Lobster workflow files from video walkthroughs - Identifying ambiguities and human intervention points in processes
Scanned 9/7/2026
Install to Claude Code
npx -y skills add modbender/skill-library-mcp --skill loom-workflow --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: loom-workflow
description: |
AI-native workflow analyzer for Loom recordings. Breaks down recorded business processes
into structured, automatable workflows. Use when:
- Analyzing Loom videos to understand workflows
- Extracting steps, tools, and decision points from screen recordings
- Generating Lobster workflow files from video walkthroughs
- Identifying ambiguities and human intervention points in processes
---
# 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 actions
- `llm-task` for classification/decision steps
- Resume tokens for interrupted workflows
- JSON piping between steps
## Requirements
- `yt-dlp` - Video download
- `ffmpeg` - Frame extraction + scene detection
- `whisper` - Audio transcription
- Vision-capable LLM for analysis step
## Multilingual Support
Works with any language - Whisper auto-detects and transcribes.
Analysis should be prompted in the video's language for best results.
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