Initial project scaffold - Phase 1 MVP structure

Core modules:
- cli.py: Command-line interface with Click
- pipeline.py: Main orchestrator
- video_processor.py: Frame extraction with ffmpeg
- audio_analyzer.py: Whisper transcription
- vision_analyzer.py: Component detection (placeholder)
- doc_generator.py: Markdown + PDF output

Also includes:
- pyproject.toml with uv/hatch config
- Prompts for AI analysis
- Basic tests
- ROADMAP.md with 4-week plan

src/cad_documenter/cli.py

@@ -0,0 +1,86 @@
+"""CAD-Documenter CLI - Main entry point."""
+
+import click
+from pathlib import Path
+from rich.console import Console
+
+from .pipeline import DocumentationPipeline
+
+console = Console()
+
+
+@click.command()
+@click.argument("video", type=click.Path(exists=True, path_type=Path))
+@click.option("-o", "--output", type=click.Path(path_type=Path), help="Output directory")
+@click.option("--frames-only", is_flag=True, help="Only extract frames, skip documentation")
+@click.option("--atomizer-hints", is_flag=True, help="Generate Atomizer FEA hints")
+@click.option("--bom", is_flag=True, help="Generate Bill of Materials")
+@click.option("--pdf", is_flag=True, help="Generate PDF via Atomaste Report Standard")
+@click.option("--frame-interval", default=2.0, help="Seconds between frame extractions")
+@click.option("--whisper-model", default="base", help="Whisper model size (tiny/base/small/medium/large)")
+@click.version_option()
+def main(
+    video: Path,
+    output: Path | None,
+    frames_only: bool,
+    atomizer_hints: bool,
+    bom: bool,
+    pdf: bool,
+    frame_interval: float,
+    whisper_model: str,
+):
+    """
+    Generate engineering documentation from a CAD walkthrough video.
+
+    VIDEO: Path to the video file (.mp4, .mov, .avi, etc.)
+    """
+    console.print(f"[bold blue]CAD-Documenter[/bold blue] v0.1.0")
+    console.print(f"Processing: [cyan]{video}[/cyan]")
+
+    # Default output directory
+    if output is None:
+        output = video.parent / f"{video.stem}_docs"
+
+    output.mkdir(parents=True, exist_ok=True)
+
+    # Run pipeline
+    pipeline = DocumentationPipeline(
+        video_path=video,
+        output_dir=output,
+        frame_interval=frame_interval,
+        whisper_model=whisper_model,
+    )
+
+    if frames_only:
+        console.print("[yellow]Extracting frames only...[/yellow]")
+        pipeline.extract_frames()
+        console.print(f"[green]✓[/green] Frames saved to {output / 'frames'}")
+        return
+
+    # Full pipeline
+    console.print("[yellow]Step 1/4:[/yellow] Extracting frames...")
+    frames = pipeline.extract_frames()
+    console.print(f"  [green]✓[/green] Extracted {len(frames)} frames")
+
+    console.print("[yellow]Step 2/4:[/yellow] Transcribing audio...")
+    transcript = pipeline.transcribe_audio()
+    console.print(f"  [green]✓[/green] Transcribed {len(transcript.segments)} segments")
+
+    console.print("[yellow]Step 3/4:[/yellow] Analyzing components...")
+    analysis = pipeline.analyze_components(frames, transcript)
+    console.print(f"  [green]✓[/green] Identified {len(analysis.components)} components")
+
+    console.print("[yellow]Step 4/4:[/yellow] Generating documentation...")
+    doc_path = pipeline.generate_documentation(analysis, atomizer_hints=atomizer_hints, bom=bom)
+    console.print(f"  [green]✓[/green] Documentation saved to {doc_path}")
+
+    if pdf:
+        console.print("[yellow]Generating PDF...[/yellow]")
+        pdf_path = pipeline.generate_pdf(doc_path)
+        console.print(f"  [green]✓[/green] PDF saved to {pdf_path}")
+
+    console.print(f"\n[bold green]Done![/bold green] Output: {output}")
+
+
+if __name__ == "__main__":
+    main()