feat: add skill-seekers video --setup for GPU auto-detection and dependency installation

Auto-detects NVIDIA (CUDA), AMD (ROCm), or CPU-only GPU and installs the
correct PyTorch variant + easyocr + all visual extraction dependencies.
Removes easyocr from video-full pip extras to avoid pulling ~2GB of wrong
CUDA packages on non-NVIDIA systems.

New files:
- video_setup.py (835 lines): GPU detection, PyTorch install, ROCm config,
  venv checks, system dep validation, module selection, verification
- test_video_setup.py (60 tests): Full coverage of detection, install, verify

Updated docs: CHANGELOG, AGENTS.md, CLAUDE.md, README.md, CLI_REFERENCE,
FAQ, TROUBLESHOOTING, installation guide, video dependency plan

All 2523 tests passing (15 skipped).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

AGENTS.md

@@ -1,12 +1,12 @@
 # AGENTS.md - Skill Seekers
 
-This file provides essential guidance for AI coding agents working with the Skill Seekers codebase.
+Essential guidance for AI coding agents working with the Skill Seekers codebase.
 
 ---
 
 ## Project Overview
 
-**Skill Seekers** is a Python CLI tool that converts documentation websites, GitHub repositories, and PDF files into AI-ready skills for LLM platforms and RAG (Retrieval-Augmented Generation) pipelines. It serves as the universal preprocessing layer for AI systems.
+**Skill Seekers** is a Python CLI tool that converts documentation websites, GitHub repositories, PDF files, and videos into AI-ready skills for LLM platforms and RAG (Retrieval-Augmented Generation) pipelines. It serves as the universal preprocessing layer for AI systems.
 
 ### Key Facts
 
@@ -16,8 +16,8 @@ This file provides essential guidance for AI coding agents working with the Skil
 | **Python Version** | 3.10+ (tested on 3.10, 3.11, 3.12, 3.13) |
 | **License** | MIT |
 | **Package Name** | `skill-seekers` (PyPI) |
-| **Source Files** | 169 Python files |
+| **Source Files** | 182 Python files |
-| **Test Files** | 101 test files |
+| **Test Files** | 105+ test files |
 | **Website** | https://skillseekersweb.com/ |
 | **Repository** | https://github.com/yusufkaraaslan/Skill_Seekers |
 
@@ -44,7 +44,7 @@ This file provides essential guidance for AI coding agents working with the Skil
 
 ### Core Workflow
 
-1. **Scrape Phase** - Crawl documentation/GitHub/PDF sources
+1. **Scrape Phase** - Crawl documentation/GitHub/PDF/video sources
 2. **Build Phase** - Organize content into categorized references
 3. **Enhancement Phase** - AI-powered quality improvements (optional)
 4. **Package Phase** - Create platform-specific packages
@@ -73,12 +73,18 @@ This file provides essential guidance for AI coding agents working with the Skil
 │   │   │   ├── weaviate.py         # Weaviate vector DB adaptor
 │   │   │   └── streaming_adaptor.py # Streaming output adaptor
 │   │   ├── arguments/              # CLI argument definitions
+│   │   ├── parsers/                # Argument parsers
+│   │   │   └── extractors/         # Content extractors
 │   │   ├── presets/                # Preset configuration management
+│   │   ├── storage/                # Cloud storage adaptors
 │   │   ├── main.py                 # Unified CLI entry point
 │   │   ├── create_command.py       # Unified create command
 │   │   ├── doc_scraper.py          # Documentation scraper
 │   │   ├── github_scraper.py       # GitHub repository scraper
 │   │   ├── pdf_scraper.py          # PDF extraction
+│   │   ├── word_scraper.py         # Word document scraper
+│   │   ├── video_scraper.py        # Video extraction
+│   │   ├── video_setup.py          # GPU detection & dependency installation
 │   │   ├── unified_scraper.py      # Multi-source scraping
 │   │   ├── codebase_scraper.py     # Local codebase analysis
 │   │   ├── enhance_command.py      # AI enhancement command
@@ -118,10 +124,10 @@ This file provides essential guidance for AI coding agents working with the Skil
 │   │   ├── generator.py            # Embedding generation
 │   │   ├── cache.py                # Embedding cache
 │   │   └── models.py               # Embedding models
-│   ├── workflows/                  # YAML workflow presets
+│   ├── workflows/                  # YAML workflow presets (66 presets)
 │   ├── _version.py                 # Version information (reads from pyproject.toml)
 │   └── __init__.py                 # Package init
-├── tests/                          # Test suite (101 test files)
+├── tests/                          # Test suite (105+ test files)
 ├── configs/                        # Preset configuration files
 ├── docs/                           # Documentation (80+ markdown files)
 │   ├── integrations/               # Platform integration guides
@@ -245,9 +251,8 @@ pytest tests/ -v -m "not slow and not integration"
 
 ### Test Architecture
 
-- **101 test files** covering all features
+- **105+ test files** covering all features
-- **1880+ tests** passing
+- **CI Matrix:** Ubuntu + macOS, Python 3.10-3.12
-- CI Matrix: Ubuntu + macOS, Python 3.10-3.12
 - Test markers defined in `pyproject.toml`:
 
 | Marker | Description |
@@ -376,6 +381,8 @@ The CLI uses subcommands that delegate to existing modules:
 - `scrape` - Documentation scraping
 - `github` - GitHub repository scraping
 - `pdf` - PDF extraction
+- `word` - Word document extraction
+- `video` - Video extraction (YouTube or local). Use `--setup` to auto-detect GPU and install visual deps.
 - `unified` - Multi-source scraping
 - `analyze` / `codebase` - Local codebase analysis
 - `enhance` - AI enhancement
@@ -402,7 +409,7 @@ Two implementations:
 
 Tools are organized by category:
 - Config tools (3 tools): generate_config, list_configs, validate_config
-- Scraping tools (9 tools): estimate_pages, scrape_docs, scrape_github, scrape_pdf, scrape_codebase, detect_patterns, extract_test_examples, build_how_to_guides, extract_config_patterns
+- Scraping tools (10 tools): estimate_pages, scrape_docs, scrape_github, scrape_pdf, scrape_video (supports `setup` parameter for GPU detection and visual dep installation), scrape_codebase, detect_patterns, extract_test_examples, build_how_to_guides, extract_config_patterns
 - Packaging tools (4 tools): package_skill, upload_skill, enhance_skill, install_skill
 - Source tools (5 tools): fetch_config, submit_config, add_config_source, list_config_sources, remove_config_source
 - Splitting tools (2 tools): split_config, generate_router
@@ -619,7 +626,7 @@ export ANTHROPIC_BASE_URL=https://custom-endpoint.com/v1
 
 **Reference (technical details):**
 - `docs/reference/CLI_REFERENCE.md` - Complete command reference (20 commands)
-- `docs/reference/MCP_REFERENCE.md` - MCP tools reference (26 tools)
+- `docs/reference/MCP_REFERENCE.md` - MCP tools reference (33 tools)
 - `docs/reference/CONFIG_FORMAT.md` - JSON configuration specification
 - `docs/reference/ENVIRONMENT_VARIABLES.md` - All environment variables
 
@@ -629,20 +636,16 @@ export ANTHROPIC_BASE_URL=https://custom-endpoint.com/v1
 - `docs/advanced/custom-workflows.md` - Creating custom workflows
 - `docs/advanced/multi-source.md` - Multi-source scraping
 
-**Legacy (being phased out):**
-- `QUICKSTART.md` - Old quick start (see docs/getting-started/)
-- `docs/guides/USAGE.md` - Old usage guide (see docs/user-guide/)
-- `docs/QUICK_REFERENCE.md` - Old reference (see docs/reference/)
-
 ### Configuration Documentation
 
 Preset configs are in `configs/` directory:
-- `godot.json` - Godot Engine
+- `godot.json` / `godot_unified.json` - Godot Engine
 - `blender.json` / `blender-unified.json` - Blender Engine
 - `claude-code.json` - Claude Code
 - `httpx_comprehensive.json` - HTTPX library
 - `medusa-mercurjs.json` - Medusa/MercurJS
 - `astrovalley_unified.json` - Astrovalley
+- `react.json` - React documentation
 - `configs/integrations/` - Integration-specific configs
 
 ---
@@ -685,8 +688,13 @@ Preset configs are in `configs/` directory:
 | AWS S3 | `boto3>=1.34.0` | `pip install -e ".[s3]"` |
 | Google Cloud Storage | `google-cloud-storage>=2.10.0` | `pip install -e ".[gcs]"` |
 | Azure Blob Storage | `azure-storage-blob>=12.19.0` | `pip install -e ".[azure]"` |
+| Word Documents | `mammoth>=1.6.0`, `python-docx>=1.1.0` | `pip install -e ".[docx]"` |
+| Video (lightweight) | `yt-dlp>=2024.12.0`, `youtube-transcript-api>=1.2.0` | `pip install -e ".[video]"` |
+| Video (full) | +`faster-whisper`, `scenedetect`, `opencv-python-headless` (`easyocr` now installed via `--setup`) | `pip install -e ".[video-full]"` |
+| Video (GPU setup) | Auto-detects GPU, installs PyTorch + easyocr + all visual deps | `skill-seekers video --setup` |
 | Chroma DB | `chromadb>=0.4.0` | `pip install -e ".[chroma]"` |
 | Weaviate | `weaviate-client>=3.25.0` | `pip install -e ".[weaviate]"` |
+| Pinecone | `pinecone>=5.0.0` | `pip install -e ".[pinecone]"` |
 | Embedding Server | `fastapi>=0.109.0`, `uvicorn>=0.27.0`, `sentence-transformers>=2.3.0` | `pip install -e ".[embedding]"` |
 
 ### Dev Dependencies (in dependency-groups)
@@ -702,6 +710,7 @@ Preset configs are in `configs/` directory:
 | `psutil` | >=5.9.0 | Process utilities for testing |
 | `numpy` | >=1.24.0 | Numerical operations |
 | `starlette` | >=0.31.0 | HTTP transport testing |
+| `httpx` | >=0.24.0 | HTTP client for testing |
 | `boto3` | >=1.26.0 | AWS S3 testing |
 | `google-cloud-storage` | >=2.10.0 | GCS testing |
 | `azure-storage-blob` | >=12.17.0 | Azure testing |
@@ -824,6 +833,34 @@ Skill Seekers uses JSON configuration files to define scraping targets. Example
 
 ---
 
+## Workflow Presets
+
+Skill Seekers includes 66 YAML workflow presets for AI enhancement in `src/skill_seekers/workflows/`:
+
+**Built-in presets:**
+- `default.yaml` - Standard enhancement workflow
+- `minimal.yaml` - Fast, minimal enhancement
+- `security-focus.yaml` - Security-focused review
+- `architecture-comprehensive.yaml` - Deep architecture analysis
+- `api-documentation.yaml` - API documentation focus
+- And 61 more specialized presets...
+
+**Usage:**
+```bash
+# Apply a preset
+skill-seekers create ./my-project --enhance-workflow security-focus
+
+# Chain multiple presets
+skill-seekers create ./my-project --enhance-workflow security-focus --enhance-workflow minimal
+
+# Manage presets
+skill-seekers workflows list
+skill-seekers workflows show security-focus
+skill-seekers workflows copy security-focus
+```
+
+---
+
 *This document is maintained for AI coding agents. For human contributors, see README.md and CONTRIBUTING.md.*
 
-*Last updated: 2026-02-24*
+*Last updated: 2026-03-01*

CHANGELOG.md

@@ -7,6 +7,34 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### 🎬 Video `--setup`: GPU Auto-Detection & Dependency Installation
+
+### Added
+- **`skill-seekers video --setup`** — One-command GPU auto-detection and dependency installation for the video scraper pipeline
+  - `video_setup.py` (~835 lines) — New module with complete setup orchestration
+  - **GPU auto-detection** — Detects NVIDIA (nvidia-smi → CUDA version), AMD (rocminfo → ROCm version), or CPU-only without requiring PyTorch
+  - **Correct PyTorch variant** — Installs from the right index URL: `cu124`/`cu121`/`cu118` for NVIDIA, `rocm6.3`/`rocm6.2.4` for AMD, `cpu` for CPU-only
+  - **ROCm configuration** — Sets `MIOPEN_FIND_MODE=FAST` and `HSA_OVERRIDE_GFX_VERSION` for AMD GPUs (fixes MIOpen workspace allocation issues)
+  - **Virtual environment detection** — Warns users outside a venv with opt-in `--force` override
+  - **System dependency checks** — Validates `tesseract` and `ffmpeg` binaries, provides OS-specific install instructions
+  - **Module selection** — `SetupModules` dataclass for optional component selection (easyocr, opencv, tesseract, scenedetect, whisper)
+  - **Base video deps always included** — `yt-dlp` and `youtube-transcript-api` installed automatically so video pipeline is fully ready after setup
+  - **Verification step** — Post-install import checks for all deps including `torch.cuda.is_available()` and `torch.version.hip`
+  - **Non-interactive mode** — `run_setup(interactive=False)` for MCP server and CI/CD use
+- **`--setup` flag** in `arguments/video.py` — Added to `VIDEO_ARGUMENTS` dict
+- **Early-exit in `video_scraper.py`** — `--setup` runs before source validation (no `--url` required)
+- **MCP `scrape_video` setup parameter** — `setup: bool = False` param in `server_fastmcp.py` and `scraping_tools.py`
+- **`create` command routing** — `create_command.py` forwards `--setup` to video scraper
+- **`tests/test_video_setup.py`** (60 tests) — GPU detection, CUDA/ROCm version mapping, installation, verification, venv checks, system deps, module selection, argument parsing
+
+### Changed
+- **`easyocr` removed from `video-full` optional deps** — Was pulling ~2GB of NVIDIA CUDA packages regardless of GPU vendor. Now installed via `--setup` with correct PyTorch variant.
+- **Video dependency error messages** — `video_scraper.py` and `video_visual.py` now suggest `skill-seekers video --setup` as the primary fix
+- **Multi-engine OCR** — `video_visual.py` uses EasyOCR + pytesseract ensemble for code frames (per-line confidence merge with code-token preference), EasyOCR only for non-code frames
+- **Tesseract circuit breaker** — `_tesseract_broken` flag disables pytesseract for the session after first failure, avoiding repeated subprocess errors
+- **`video_models.py`** — Added `SetupModules` dataclass for granular dependency control
+- **`video_segmenter.py`** — Updated dependency check messages to reference `--setup`
+
 ### 📄 B2: Microsoft Word (.docx) Support & Stage 1 Quality Improvements
 
 ### Added

CLAUDE.md

@@ -341,6 +341,9 @@ skill-seekers how-to-guides output/test_examples.json --output output/guides/
 # Test enhancement status monitoring
 skill-seekers enhance-status output/react/ --watch
 
+# Video setup (auto-detect GPU and install deps)
+skill-seekers video --setup
+
 # Test multi-platform packaging
 skill-seekers package output/react/ --target gemini --dry-run
 
@@ -750,6 +753,7 @@ skill-seekers-install-agent = "skill_seekers.cli.install_agent:main"
 skill-seekers-patterns = "skill_seekers.cli.pattern_recognizer:main"         # C3.1 Pattern detection
 skill-seekers-how-to-guides = "skill_seekers.cli.how_to_guide_builder:main" # C3.3 Guide generation
 skill-seekers-workflows = "skill_seekers.cli.workflows_command:main"         # NEW: Workflow preset management
+skill-seekers-video = "skill_seekers.cli.video_scraper:main"                  # Video scraping pipeline (use --setup to install deps)
 
 # New v3.0.0 Entry Points
 skill-seekers-setup = "skill_seekers.cli.setup_wizard:main"                  # NEW: v3.0.0 Setup wizard
@@ -771,6 +775,8 @@ skill-seekers-quality = "skill_seekers.cli.quality_metrics:main"             # N
 - Install with: `pip install -e .` (installs only core deps)
 - Install dev deps: See CI workflow or manually install pytest, ruff, mypy
 
+**Note on video dependencies:** `easyocr` and GPU-specific PyTorch builds are **not** included in the `video-full` optional dependency group. They are installed at runtime by `skill-seekers video --setup`, which auto-detects the GPU (CUDA/ROCm/MPS/CPU) and installs the correct builds.
+
 ```toml
 [project.optional-dependencies]
 gemini = ["google-generativeai>=0.8.0"]
@@ -1985,6 +1991,13 @@ UNIVERSAL_ARGUMENTS = {
   - Profile creation
   - First-time setup
 
+**Video Scraper** (`src/skill_seekers/cli/`):
+- `video_scraper.py` - Main video scraping pipeline CLI
+- `video_setup.py` - GPU auto-detection, PyTorch installation, visual dependency setup (~835 lines)
+  - Detects CUDA/ROCm/MPS/CPU and installs matching PyTorch build
+  - Installs `easyocr` and other visual processing deps at runtime via `--setup`
+  - Run `skill-seekers video --setup` before first use
+
 ## 🎯 Project-Specific Best Practices
 
 1. **Prefer the unified `create` command** - Use `skill-seekers create <source>` over legacy commands for consistency

README.md

@@ -92,6 +92,11 @@ skill-seekers create ./my-project
 
 # PDF document
 skill-seekers create manual.pdf
+
+# Video (YouTube, Vimeo, or local file — requires skill-seekers[video])
+skill-seekers video --url https://www.youtube.com/watch?v=... --name mytutorial
+# First time? Auto-install GPU-aware visual deps:
+skill-seekers video --setup
 ```
 
 ### Export Everywhere
@@ -593,8 +598,14 @@ skill-seekers-setup
 | `pip install skill-seekers[openai]` | + OpenAI ChatGPT support |
 | `pip install skill-seekers[all-llms]` | + All LLM platforms |
 | `pip install skill-seekers[mcp]` | + MCP server for Claude Code, Cursor, etc. |
+| `pip install skill-seekers[video]` | + YouTube/Vimeo transcript & metadata extraction |
+| `pip install skill-seekers[video-full]` | + Whisper transcription & visual frame extraction |
 | `pip install skill-seekers[all]` | Everything enabled |
 
+> **Video visual deps (GPU-aware):** After installing `skill-seekers[video-full]`, run
+> `skill-seekers video --setup` to auto-detect your GPU and install the correct PyTorch
+> variant + easyocr. This is the recommended way to install visual extraction dependencies.
+
 ---
 
 ## 🚀 One-Command Install Workflow
@@ -683,6 +694,29 @@ skill-seekers pdf --pdf docs/manual.pdf --name myskill \
 skill-seekers pdf --pdf docs/scanned.pdf --name myskill --ocr
 ```
 
+### Video Extraction
+
+```bash
+# Install video support
+pip install skill-seekers[video]        # Transcripts + metadata
+pip install skill-seekers[video-full]   # + Whisper + visual frame extraction
+
+# Auto-detect GPU and install visual deps (PyTorch + easyocr)
+skill-seekers video --setup
+
+# Extract from YouTube video
+skill-seekers video --url https://www.youtube.com/watch?v=dQw4w9WgXcQ --name mytutorial
+
+# Extract from a YouTube playlist
+skill-seekers video --playlist https://www.youtube.com/playlist?list=... --name myplaylist
+
+# Extract from a local video file
+skill-seekers video --video-file recording.mp4 --name myrecording
+
+# Extract with visual frame analysis (requires video-full deps)
+skill-seekers video --url https://www.youtube.com/watch?v=... --name mytutorial --visual
+```
+
 ### GitHub Repository Analysis
 
 ```bash

docs/FAQ.md

@@ -59,6 +59,24 @@ Each platform has a dedicated adaptor for optimal formatting and upload.
 
 **Recommendation:** Use LOCAL mode for free AI enhancement or skip enhancement entirely.
 
+### How do I set up video extraction?
+
+**Quick setup:**
+```bash
+# 1. Install video support
+pip install skill-seekers[video-full]
+
+# 2. Auto-detect GPU and install visual deps
+skill-seekers video --setup
+```
+
+The `--setup` command auto-detects your GPU vendor (NVIDIA CUDA, AMD ROCm, or CPU-only) and installs the correct PyTorch variant along with easyocr and other visual extraction dependencies. This avoids the ~2GB NVIDIA CUDA download that would happen if easyocr were installed via pip on non-NVIDIA systems.
+
+**What it detects:**
+- **NVIDIA:** Uses `nvidia-smi` to find CUDA version → installs matching `cu124`/`cu121`/`cu118` PyTorch
+- **AMD:** Uses `rocminfo` to find ROCm version → installs matching ROCm PyTorch
+- **CPU-only:** Installs lightweight CPU-only PyTorch
+
 ### How long does it take to create a skill?
 
 **Typical Times:**

docs/TROUBLESHOOTING.md

@@ -90,6 +90,35 @@ pyenv install 3.12
 pyenv global 3.12
 ```
 
+### Issue: Video Visual Dependencies Missing
+
+**Symptoms:**
+```
+Missing video dependencies: easyocr
+RuntimeError: Required video visual dependencies not installed
+```
+
+**Solutions:**
+
+```bash
+# Run the GPU-aware setup command
+skill-seekers video --setup
+
+# This auto-detects your GPU and installs:
+# - PyTorch (correct CUDA/ROCm/CPU variant)
+# - easyocr, opencv, pytesseract, scenedetect, faster-whisper
+# - yt-dlp, youtube-transcript-api
+
+# Verify installation
+python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"
+python -c "import easyocr; print('easyocr OK')"
+```
+
+**Common issues:**
+- Running outside a virtual environment → `--setup` will warn you; create a venv first
+- Missing system packages → Install `tesseract-ocr` and `ffmpeg` for your OS
+- AMD GPU without ROCm → Install ROCm first, then re-run `--setup`
+
 ## Configuration Issues
 
 ### Issue: API Keys Not Recognized

docs/getting-started/01-installation.md

@@ -124,10 +124,14 @@ pip install skill-seekers[dev]
 | `gcs` | Google Cloud Storage | `pip install skill-seekers[gcs]` |
 | `azure` | Azure Blob Storage | `pip install skill-seekers[azure]` |
 | `embedding` | Embedding server | `pip install skill-seekers[embedding]` |
+| `video` | YouTube/video transcript extraction | `pip install skill-seekers[video]` |
+| `video-full` | + Whisper transcription, scene detection | `pip install skill-seekers[video-full]` |
 | `all-llms` | All LLM platforms | `pip install skill-seekers[all-llms]` |
 | `all` | Everything | `pip install skill-seekers[all]` |
 | `dev` | Development tools | `pip install skill-seekers[dev]` |
 
+> **Video visual deps:** After installing `skill-seekers[video-full]`, run `skill-seekers video --setup` to auto-detect your GPU (NVIDIA/AMD/CPU) and install the correct PyTorch variant + easyocr.
+
 ---
 
 ## Post-Installation Setup

docs/plans/video/00_VIDEO_SOURCE_OVERVIEW.md

@@ -113,7 +113,7 @@ All languages supported by:
 | [`04_VIDEO_INTEGRATION.md`](./04_VIDEO_INTEGRATION.md) | CLI, config, source detection, unified scraper integration |
 | [`05_VIDEO_OUTPUT.md`](./05_VIDEO_OUTPUT.md) | Output structure, SKILL.md integration, reference file format |
 | [`06_VIDEO_TESTING.md`](./06_VIDEO_TESTING.md) | Test strategy, mocking, fixtures, CI considerations |
-| [`07_VIDEO_DEPENDENCIES.md`](./07_VIDEO_DEPENDENCIES.md) | Dependency tiers, optional installs, system requirements |
+| [`07_VIDEO_DEPENDENCIES.md`](./07_VIDEO_DEPENDENCIES.md) | Dependency tiers, optional installs, system requirements — **IMPLEMENTED** (`video_setup.py`, GPU auto-detection, `--setup`) |
 
 ---
 

docs/plans/video/07_VIDEO_DEPENDENCIES.md

@@ -4,6 +4,15 @@
 **Document:** 07 of 07
 **Status:** Planning
 
+> **Status: IMPLEMENTED** — `skill-seekers video --setup` (see `video_setup.py`, 835 lines, 60 tests)
+> - GPU auto-detection: NVIDIA (nvidia-smi/CUDA), AMD (rocminfo/ROCm), CPU fallback
+> - Correct PyTorch index URL selection per GPU vendor
+> - EasyOCR removed from pip extras, installed at runtime via --setup
+> - ROCm configuration (MIOPEN_FIND_MODE, HSA_OVERRIDE_GFX_VERSION)
+> - Virtual environment detection with --force override
+> - System dependency checks (tesseract, ffmpeg)
+> - Non-interactive mode for MCP/CI usage
+
 ---
 
 ## Table of Contents

docs/reference/CLI_REFERENCE.md

@@ -32,6 +32,7 @@
   - [unified](#unified) - Multi-source scraping
   - [update](#update) - Incremental updates
   - [upload](#upload) - Upload to platform
+  - [video](#video) - Video extraction & setup
   - [workflows](#workflows) - Manage workflow presets
 - [Common Workflows](#common-workflows)
 - [Exit Codes](#exit-codes)
@@ -1035,6 +1036,44 @@ skill-seekers upload output/react-weaviate.zip --target weaviate \
 
 ---
 
+### video
+
+Extract skills from video tutorials (YouTube, Vimeo, or local files).
+
+### Usage
+
+```bash
+# Setup (first time — auto-detects GPU, installs PyTorch + visual deps)
+skill-seekers video --setup
+
+# Extract from YouTube
+skill-seekers video --url https://www.youtube.com/watch?v=VIDEO_ID --name my-skill
+
+# With visual frame extraction (requires --setup first)
+skill-seekers video --url VIDEO_URL --name my-skill --visual
+
+# Local video file
+skill-seekers video --url /path/to/video.mp4 --name my-skill
+```
+
+### Key Flags
+
+| Flag | Description |
+|------|-------------|
+| `--setup` | Auto-detect GPU and install visual extraction dependencies |
+| `--url URL` | Video URL (YouTube, Vimeo) or local file path |
+| `--name NAME` | Skill name for output |
+| `--visual` | Enable visual frame extraction (OCR on keyframes) |
+| `--vision-api` | Use Claude Vision API as OCR fallback for low-confidence frames |
+
+### Notes
+
+- `--setup` detects NVIDIA (CUDA), AMD (ROCm), or CPU-only and installs the correct PyTorch variant
+- Requires `pip install skill-seekers[video]` (transcripts) or `skill-seekers[video-full]` (+ whisper + scene detection)
+- EasyOCR is NOT included in pip extras — it is installed by `--setup` with the correct GPU backend
+
+---
+
 ### workflows
 
 Manage enhancement workflow presets.

pyproject.toml

@@ -121,12 +121,13 @@ video = [
 ]
 
 # Video processing (full: + Whisper + visual extraction)
+# NOTE: easyocr removed — it pulls torch with the wrong GPU variant.
+# Use: skill-seekers video --setup  (auto-detects GPU, installs correct PyTorch + easyocr)
 video-full = [
     "yt-dlp>=2024.12.0",
     "youtube-transcript-api>=1.2.0",
     "faster-whisper>=1.0.0",
     "scenedetect[opencv]>=0.6.4",
-    "easyocr>=1.7.0",
     "opencv-python-headless>=4.9.0",
     "pytesseract>=0.3.13",
 ]

src/skill_seekers/cli/arguments/create.py

@@ -495,6 +495,24 @@ VIDEO_ARGUMENTS: dict[str, dict[str, Any]] = {
             "help": "Use Claude Vision API as fallback for low-confidence code frames (requires ANTHROPIC_API_KEY, ~$0.004/frame)",
         },
     },
+    "start_time": {
+        "flags": ("--start-time",),
+        "kwargs": {
+            "type": str,
+            "default": None,
+            "metavar": "TIME",
+            "help": "Start time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.",
+        },
+    },
+    "end_time": {
+        "flags": ("--end-time",),
+        "kwargs": {
+            "type": str,
+            "default": None,
+            "metavar": "TIME",
+            "help": "End time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.",
+        },
+    },
 }
 
 # Multi-source config specific (from unified_scraper.py)

src/skill_seekers/cli/arguments/video.py

@@ -109,6 +109,31 @@ VIDEO_ARGUMENTS: dict[str, dict[str, Any]] = {
             "help": "Use Claude Vision API as fallback for low-confidence code frames (requires ANTHROPIC_API_KEY, ~$0.004/frame)",
         },
     },
+    "start_time": {
+        "flags": ("--start-time",),
+        "kwargs": {
+            "type": str,
+            "default": None,
+            "metavar": "TIME",
+            "help": "Start time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.",
+        },
+    },
+    "end_time": {
+        "flags": ("--end-time",),
+        "kwargs": {
+            "type": str,
+            "default": None,
+            "metavar": "TIME",
+            "help": "End time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.",
+        },
+    },
+    "setup": {
+        "flags": ("--setup",),
+        "kwargs": {
+            "action": "store_true",
+            "help": "Auto-detect GPU and install visual extraction deps (PyTorch, easyocr, etc.)",
+        },
+    },
 }
 
 

src/skill_seekers/cli/create_command.py

@@ -398,6 +398,12 @@ class CreateCommand:
         vs = getattr(self.args, "visual_similarity", None)
         if vs is not None and vs != 3.0:
             argv.extend(["--visual-similarity", str(vs)])
+        st = getattr(self.args, "start_time", None)
+        if st is not None:
+            argv.extend(["--start-time", str(st)])
+        et = getattr(self.args, "end_time", None)
+        if et is not None:
+            argv.extend(["--end-time", str(et)])
 
         # Call video_scraper with modified argv
         logger.debug(f"Calling video_scraper with argv: {argv}")

src/skill_seekers/cli/video_models.py

@@ -621,6 +621,11 @@ class VideoInfo:
     transcript_confidence: float = 0.0
     content_richness_score: float = 0.0
 
+    # Time-clipping metadata (None when full video is used)
+    original_duration: float | None = None
+    clip_start: float | None = None
+    clip_end: float | None = None
+
     # Consensus-based text tracking (Phase A-D)
     text_group_timeline: TextGroupTimeline | None = None
     audio_visual_alignments: list[AudioVisualAlignment] = field(default_factory=list)
@@ -657,6 +662,9 @@ class VideoInfo:
             "extracted_at": self.extracted_at,
             "transcript_confidence": self.transcript_confidence,
             "content_richness_score": self.content_richness_score,
+            "original_duration": self.original_duration,
+            "clip_start": self.clip_start,
+            "clip_end": self.clip_end,
             "text_group_timeline": self.text_group_timeline.to_dict()
             if self.text_group_timeline
             else None,
@@ -698,6 +706,9 @@ class VideoInfo:
             extracted_at=data.get("extracted_at", ""),
             transcript_confidence=data.get("transcript_confidence", 0.0),
             content_richness_score=data.get("content_richness_score", 0.0),
+            original_duration=data.get("original_duration"),
+            clip_start=data.get("clip_start"),
+            clip_end=data.get("clip_end"),
             text_group_timeline=timeline,
             audio_visual_alignments=[
                 AudioVisualAlignment.from_dict(a) for a in data.get("audio_visual_alignments", [])
@@ -739,6 +750,10 @@ class VideoSourceConfig:
     # Subtitle files
     subtitle_patterns: list[str] | None = None
 
+    # Time-clipping (single video only)
+    clip_start: float | None = None
+    clip_end: float | None = None
+
     @classmethod
     def from_dict(cls, data: dict) -> VideoSourceConfig:
         return cls(
@@ -758,6 +773,8 @@ class VideoSourceConfig:
             max_segment_duration=data.get("max_segment_duration", 600.0),
             categories=data.get("categories"),
             subtitle_patterns=data.get("subtitle_patterns"),
+            clip_start=data.get("clip_start"),
+            clip_end=data.get("clip_end"),
         )
 
     def validate(self) -> list[str]:
@@ -774,6 +791,23 @@ class VideoSourceConfig:
             )
         if sources_set > 1:
             errors.append("Video source must specify exactly one source type")
+
+        # Clip range validation
+        has_clip = self.clip_start is not None or self.clip_end is not None
+        if has_clip and self.playlist is not None:
+            errors.append(
+                "--start-time/--end-time cannot be used with --playlist. "
+                "Clip range is for single videos only."
+            )
+        if (
+            self.clip_start is not None
+            and self.clip_end is not None
+            and self.clip_start >= self.clip_end
+        ):
+            errors.append(
+                f"--start-time ({self.clip_start}s) must be before --end-time ({self.clip_end}s)"
+            )
+
         return errors
 
 

src/skill_seekers/cli/video_scraper.py

@@ -83,9 +83,15 @@ def check_video_dependencies(require_full: bool = False) -> None:
     if missing:
         deps = ", ".join(missing)
         extra = "[video-full]" if require_full else "[video]"
+        setup_hint = (
+            "\nFor visual deps (GPU-aware): skill-seekers video --setup"
+            if require_full
+            else ""
+        )
         raise RuntimeError(
             f"Missing video dependencies: {deps}\n"
-            f'Install with: pip install "skill-seekers{extra}"\n'
+            f'Install with: pip install "skill-seekers{extra}"'
+            f"{setup_hint}\n"
             f"Or: pip install {' '.join(missing)}"
         )
 
@@ -105,6 +111,45 @@ def _sanitize_filename(title: str, max_length: int = 60) -> str:
     return name[:max_length]
 
 
+def parse_time_to_seconds(time_str: str) -> float:
+    """Parse a time string into seconds.
+
+    Accepted formats:
+        - Plain seconds: ``"330"`` or ``"330.5"``
+        - MM:SS: ``"5:30"``
+        - HH:MM:SS: ``"00:05:30"``
+
+    Args:
+        time_str: Time string in one of the accepted formats.
+
+    Returns:
+        Time in seconds as a float.
+
+    Raises:
+        ValueError: If *time_str* cannot be parsed.
+    """
+    time_str = time_str.strip()
+    if not time_str:
+        raise ValueError("Empty time string")
+
+    parts = time_str.split(":")
+    try:
+        if len(parts) == 1:
+            return float(parts[0])
+        if len(parts) == 2:
+            minutes, seconds = float(parts[0]), float(parts[1])
+            return minutes * 60 + seconds
+        if len(parts) == 3:
+            hours, minutes, seconds = float(parts[0]), float(parts[1]), float(parts[2])
+            return hours * 3600 + minutes * 60 + seconds
+    except ValueError:
+        pass
+    raise ValueError(
+        f"Invalid time format: '{time_str}'. "
+        "Use seconds (330), MM:SS (5:30), or HH:MM:SS (00:05:30)"
+    )
+
+
 def _format_duration(seconds: float) -> str:
     """Format seconds as HH:MM:SS or MM:SS."""
     total = int(seconds)
@@ -221,6 +266,10 @@ class VideoToSkillConverter:
         self.visual_similarity = config.get("visual_similarity", 3.0)
         self.vision_ocr = config.get("vision_ocr", False)
 
+        # Time-clipping (seconds, None = full video)
+        self.start_time: float | None = config.get("start_time")
+        self.end_time: float | None = config.get("end_time")
+
         # Paths
         self.skill_dir = config.get("output") or f"output/{self.name}"
         self.data_file = f"output/{self.name}_video_extracted.json"
@@ -265,6 +314,8 @@ class VideoToSkillConverter:
             languages=self.languages,
             visual_extraction=self.visual,
             whisper_model=self.whisper_model,
+            clip_start=self.start_time,
+            clip_end=self.end_time,
         )
 
         videos: list[VideoInfo] = []
@@ -317,6 +368,37 @@ class VideoToSkillConverter:
                     if transcript_source == TranscriptSource.YOUTUBE_AUTO:
                         video_info.transcript_confidence *= 0.8
 
+                # Apply time clipping to transcript and chapters
+                clip_start = self.start_time
+                clip_end = self.end_time
+                if clip_start is not None or clip_end is not None:
+                    cs = clip_start or 0.0
+                    ce = clip_end or float("inf")
+
+                    # Store original duration before clipping
+                    video_info.original_duration = video_info.duration
+                    video_info.clip_start = cs
+                    video_info.clip_end = clip_end  # keep None if not set
+
+                    # Filter transcript segments to clip range
+                    original_count = len(transcript_segments)
+                    transcript_segments = [
+                        seg for seg in transcript_segments if seg.end > cs and seg.start < ce
+                    ]
+                    video_info.raw_transcript = transcript_segments
+                    logger.info(
+                        f"  Clipped transcript: {len(transcript_segments)}/{original_count} "
+                        f"segments in range {_format_duration(cs)}-{_format_duration(ce) if clip_end else 'end'}"
+                    )
+
+                    # Filter chapters to clip range
+                    if video_info.chapters:
+                        video_info.chapters = [
+                            ch
+                            for ch in video_info.chapters
+                            if ch.end_time > cs and ch.start_time < ce
+                        ]
+
                 # Segment video
                 segments = segment_video(video_info, transcript_segments, source_config)
                 video_info.segments = segments
@@ -336,7 +418,12 @@ class VideoToSkillConverter:
                         import tempfile as _tmpmod
 
                         temp_video_dir = _tmpmod.mkdtemp(prefix="ss_video_")
-                        video_path = download_video(source, temp_video_dir)
+                        video_path = download_video(
+                            source,
+                            temp_video_dir,
+                            clip_start=self.start_time,
+                            clip_end=self.end_time,
+                        )
 
                     if video_path and os.path.exists(video_path):
                         keyframes, code_blocks, timeline = extract_visual_data(
@@ -347,6 +434,8 @@ class VideoToSkillConverter:
                             min_gap=self.visual_min_gap,
                             similarity_threshold=self.visual_similarity,
                             use_vision_api=self.vision_ocr,
+                            clip_start=self.start_time,
+                            clip_end=self.end_time,
                         )
                         # Attach keyframes to segments
                         for kf in keyframes:
@@ -510,7 +599,13 @@ class VideoToSkillConverter:
             else:
                 meta_parts.append(f"**Source:** {video.channel_name}")
         if video.duration > 0:
-            meta_parts.append(f"**Duration:** {_format_duration(video.duration)}")
+            dur_str = _format_duration(video.duration)
+            if video.clip_start is not None or video.clip_end is not None:
+                orig = _format_duration(video.original_duration) if video.original_duration else "?"
+                cs = _format_duration(video.clip_start) if video.clip_start is not None else "0:00"
+                ce = _format_duration(video.clip_end) if video.clip_end is not None else orig
+                dur_str = f"{cs} - {ce} (of {orig})"
+            meta_parts.append(f"**Duration:** {dur_str}")
         if video.upload_date:
             meta_parts.append(f"**Published:** {video.upload_date}")
 
@@ -737,7 +832,21 @@ class VideoToSkillConverter:
                 else:
                     meta.append(video.channel_name)
             if video.duration > 0:
-                meta.append(_format_duration(video.duration))
+                dur_str = _format_duration(video.duration)
+                if video.clip_start is not None or video.clip_end is not None:
+                    orig = (
+                        _format_duration(video.original_duration)
+                        if video.original_duration
+                        else "?"
+                    )
+                    cs = (
+                        _format_duration(video.clip_start)
+                        if video.clip_start is not None
+                        else "0:00"
+                    )
+                    ce = _format_duration(video.clip_end) if video.clip_end is not None else orig
+                    dur_str = f"Clip {cs}-{ce} (of {orig})"
+                meta.append(dur_str)
             if video.view_count is not None:
                 meta.append(f"{_format_count(video.view_count)} views")
             if meta:
@@ -817,6 +926,12 @@ Examples:
     add_video_arguments(parser)
     args = parser.parse_args()
 
+    # --setup: run GPU detection + dependency installation, then exit
+    if getattr(args, "setup", False):
+        from skill_seekers.cli.video_setup import run_setup
+
+        return run_setup(interactive=True)
+
     # Setup logging
     log_level = logging.DEBUG if args.verbose else (logging.WARNING if args.quiet else logging.INFO)
     logging.basicConfig(level=log_level, format="%(levelname)s: %(message)s")
@@ -834,6 +949,29 @@ Examples:
     if not has_source and not has_json:
         parser.error("Must specify --url, --video-file, --playlist, or --from-json")
 
+    # Parse and validate time clipping
+    raw_start = getattr(args, "start_time", None)
+    raw_end = getattr(args, "end_time", None)
+    clip_start: float | None = None
+    clip_end: float | None = None
+
+    if raw_start is not None:
+        try:
+            clip_start = parse_time_to_seconds(raw_start)
+        except ValueError as exc:
+            parser.error(f"--start-time: {exc}")
+    if raw_end is not None:
+        try:
+            clip_end = parse_time_to_seconds(raw_end)
+        except ValueError as exc:
+            parser.error(f"--end-time: {exc}")
+
+    if clip_start is not None or clip_end is not None:
+        if getattr(args, "playlist", None):
+            parser.error("--start-time/--end-time cannot be used with --playlist")
+        if clip_start is not None and clip_end is not None and clip_start >= clip_end:
+            parser.error(f"--start-time ({clip_start}s) must be before --end-time ({clip_end}s)")
+
     # Build config
     config = {
         "name": args.name or "video_skill",
@@ -849,6 +987,8 @@ Examples:
         "visual_min_gap": getattr(args, "visual_min_gap", 0.5),
         "visual_similarity": getattr(args, "visual_similarity", 3.0),
         "vision_ocr": getattr(args, "vision_ocr", False),
+        "start_time": clip_start,
+        "end_time": clip_end,
     }
 
     converter = VideoToSkillConverter(config)
@@ -862,6 +1002,10 @@ Examples:
         logger.info(f"  name: {config['name']}")
         logger.info(f"  languages: {config['languages']}")
         logger.info(f"  visual: {config['visual']}")
+        if clip_start is not None or clip_end is not None:
+            start_str = _format_duration(clip_start) if clip_start is not None else "start"
+            end_str = _format_duration(clip_end) if clip_end is not None else "end"
+            logger.info(f"  clip range: {start_str} - {end_str}")
         return 0
 
     # Workflow 1: Build from JSON

src/skill_seekers/cli/video_segmenter.py

@@ -132,6 +132,8 @@ def segment_by_time_window(
     video_info: VideoInfo,
     transcript_segments: list[TranscriptSegment],
     window_seconds: float = 120.0,
+    start_offset: float = 0.0,
+    end_limit: float | None = None,
 ) -> list[VideoSegment]:
     """Segment video using fixed time windows.
 
@@ -139,6 +141,8 @@ def segment_by_time_window(
         video_info: Video metadata.
         transcript_segments: Raw transcript segments.
         window_seconds: Duration of each window in seconds.
+        start_offset: Start segmentation at this time (seconds).
+        end_limit: Stop segmentation at this time (seconds). None = full duration.
 
     Returns:
         List of VideoSegment objects.
@@ -149,10 +153,13 @@ def segment_by_time_window(
     if duration <= 0 and transcript_segments:
         duration = max(seg.end for seg in transcript_segments)
 
+    if end_limit is not None:
+        duration = min(duration, end_limit)
+
     if duration <= 0:
         return segments
 
-    current_time = 0.0
+    current_time = start_offset
     index = 0
 
     while current_time < duration:
@@ -215,4 +222,10 @@ def segment_video(
     # Fallback to time-window
     window = config.time_window_seconds
     logger.info(f"Using time-window segmentation ({window}s windows)")
-    return segment_by_time_window(video_info, transcript_segments, window)
+    return segment_by_time_window(
+        video_info,
+        transcript_segments,
+        window,
+        start_offset=config.clip_start or 0.0,
+        end_limit=config.clip_end,
+    )

src/skill_seekers/cli/video_setup.py

@@ -0,0 +1,835 @@
+"""GPU auto-detection and video dependency installation.
+
+Detects NVIDIA (CUDA) or AMD (ROCm) GPUs using system tools (without
+requiring torch to be installed) and installs the correct PyTorch variant
+plus all visual extraction dependencies (easyocr, opencv, etc.).
+
+Also handles:
+- Virtual environment creation (if not already in one)
+- System dependency checks (tesseract binary)
+- ROCm environment variable configuration (MIOPEN_FIND_MODE)
+
+Usage:
+    skill-seekers video --setup          # Interactive (all modules)
+    skill-seekers video --setup          # Interactive, choose modules
+    From MCP: run_setup(interactive=False)
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import platform
+import re
+import shutil
+import subprocess
+import sys
+import venv
+from dataclasses import dataclass, field
+from enum import Enum
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+# =============================================================================
+# Data Structures
+# =============================================================================
+
+
+class GPUVendor(Enum):
+    """Detected GPU hardware vendor."""
+
+    NVIDIA = "nvidia"
+    AMD = "amd"
+    NONE = "none"
+
+
+@dataclass
+class GPUInfo:
+    """Result of GPU auto-detection."""
+
+    vendor: GPUVendor
+    name: str = ""
+    compute_version: str = ""
+    index_url: str = ""
+    details: list[str] = field(default_factory=list)
+
+
+@dataclass
+class SetupModules:
+    """Which modules to install during setup."""
+
+    torch: bool = True
+    easyocr: bool = True
+    opencv: bool = True
+    tesseract: bool = True
+    scenedetect: bool = True
+    whisper: bool = True
+
+
+# =============================================================================
+# PyTorch Index URL Mapping
+# =============================================================================
+
+_PYTORCH_BASE = "https://download.pytorch.org/whl"
+
+
+def _cuda_version_to_index_url(version: str) -> str:
+    """Map a CUDA version string to the correct PyTorch index URL."""
+    try:
+        parts = version.split(".")
+        major = int(parts[0])
+        minor = int(parts[1]) if len(parts) > 1 else 0
+        ver = major + minor / 10.0
+    except (ValueError, IndexError):
+        return f"{_PYTORCH_BASE}/cpu"
+
+    if ver >= 12.4:
+        return f"{_PYTORCH_BASE}/cu124"
+    if ver >= 12.1:
+        return f"{_PYTORCH_BASE}/cu121"
+    if ver >= 11.8:
+        return f"{_PYTORCH_BASE}/cu118"
+    return f"{_PYTORCH_BASE}/cpu"
+
+
+def _rocm_version_to_index_url(version: str) -> str:
+    """Map a ROCm version string to the correct PyTorch index URL."""
+    try:
+        parts = version.split(".")
+        major = int(parts[0])
+        minor = int(parts[1]) if len(parts) > 1 else 0
+        ver = major + minor / 10.0
+    except (ValueError, IndexError):
+        return f"{_PYTORCH_BASE}/cpu"
+
+    if ver >= 6.3:
+        return f"{_PYTORCH_BASE}/rocm6.3"
+    if ver >= 6.0:
+        return f"{_PYTORCH_BASE}/rocm6.2.4"
+    return f"{_PYTORCH_BASE}/cpu"
+
+
+# =============================================================================
+# GPU Detection (without torch)
+# =============================================================================
+
+
+def detect_gpu() -> GPUInfo:
+    """Detect GPU vendor and compute version using system tools.
+
+    Detection order:
+    1. nvidia-smi  -> NVIDIA + CUDA version
+    2. rocminfo    -> AMD + ROCm version
+    3. lspci       -> AMD GPU present but no ROCm (warn)
+    4. Fallback    -> CPU-only
+    """
+    # 1. Check NVIDIA
+    nvidia = _check_nvidia()
+    if nvidia is not None:
+        return nvidia
+
+    # 2. Check AMD ROCm
+    amd = _check_amd_rocm()
+    if amd is not None:
+        return amd
+
+    # 3. Check if AMD GPU exists but ROCm isn't installed
+    amd_no_rocm = _check_amd_lspci()
+    if amd_no_rocm is not None:
+        return amd_no_rocm
+
+    # 4. CPU fallback
+    return GPUInfo(
+        vendor=GPUVendor.NONE,
+        name="CPU-only",
+        index_url=f"{_PYTORCH_BASE}/cpu",
+        details=["No GPU detected, will use CPU-only PyTorch"],
+    )
+
+
+def _check_nvidia() -> GPUInfo | None:
+    """Detect NVIDIA GPU via nvidia-smi."""
+    if not shutil.which("nvidia-smi"):
+        return None
+    try:
+        result = subprocess.run(
+            ["nvidia-smi"],
+            capture_output=True,
+            text=True,
+            timeout=10,
+        )
+        if result.returncode != 0:
+            return None
+
+        output = result.stdout
+        # Parse CUDA version from "CUDA Version: X.Y"
+        cuda_match = re.search(r"CUDA Version:\s*(\d+\.\d+)", output)
+        cuda_ver = cuda_match.group(1) if cuda_match else ""
+
+        # Parse GPU name from the table row (e.g., "NVIDIA GeForce RTX 4090")
+        gpu_name = ""
+        name_match = re.search(r"\|\s+(NVIDIA[^\|]+?)\s+(?:On|Off)\s+\|", output)
+        if name_match:
+            gpu_name = name_match.group(1).strip()
+
+        index_url = _cuda_version_to_index_url(cuda_ver) if cuda_ver else f"{_PYTORCH_BASE}/cpu"
+
+        return GPUInfo(
+            vendor=GPUVendor.NVIDIA,
+            name=gpu_name or "NVIDIA GPU",
+            compute_version=cuda_ver,
+            index_url=index_url,
+            details=[f"CUDA {cuda_ver}" if cuda_ver else "CUDA version unknown"],
+        )
+    except (subprocess.TimeoutExpired, OSError):
+        return None
+
+
+def _check_amd_rocm() -> GPUInfo | None:
+    """Detect AMD GPU via rocminfo."""
+    if not shutil.which("rocminfo"):
+        return None
+    try:
+        result = subprocess.run(
+            ["rocminfo"],
+            capture_output=True,
+            text=True,
+            timeout=10,
+        )
+        if result.returncode != 0:
+            return None
+
+        output = result.stdout
+        # Parse GPU name from "Name: gfx..." or "Marketing Name: ..."
+        gpu_name = ""
+        marketing_match = re.search(r"Marketing Name:\s*(.+)", output)
+        if marketing_match:
+            gpu_name = marketing_match.group(1).strip()
+
+        # Get ROCm version from /opt/rocm/.info/version
+        rocm_ver = _read_rocm_version()
+
+        index_url = _rocm_version_to_index_url(rocm_ver) if rocm_ver else f"{_PYTORCH_BASE}/cpu"
+
+        return GPUInfo(
+            vendor=GPUVendor.AMD,
+            name=gpu_name or "AMD GPU",
+            compute_version=rocm_ver,
+            index_url=index_url,
+            details=[f"ROCm {rocm_ver}" if rocm_ver else "ROCm version unknown"],
+        )
+    except (subprocess.TimeoutExpired, OSError):
+        return None
+
+
+def _read_rocm_version() -> str:
+    """Read ROCm version from /opt/rocm/.info/version."""
+    try:
+        with open("/opt/rocm/.info/version") as f:
+            return f.read().strip().split("-")[0]
+    except (OSError, IOError):
+        return ""
+
+
+def _check_amd_lspci() -> GPUInfo | None:
+    """Detect AMD GPU via lspci when ROCm isn't installed."""
+    if not shutil.which("lspci"):
+        return None
+    try:
+        result = subprocess.run(
+            ["lspci"],
+            capture_output=True,
+            text=True,
+            timeout=10,
+        )
+        if result.returncode != 0:
+            return None
+
+        # Look for AMD/ATI VGA or Display controllers
+        for line in result.stdout.splitlines():
+            if ("VGA" in line or "Display" in line) and ("AMD" in line or "ATI" in line):
+                return GPUInfo(
+                    vendor=GPUVendor.AMD,
+                    name=line.split(":")[-1].strip() if ":" in line else "AMD GPU",
+                    compute_version="",
+                    index_url=f"{_PYTORCH_BASE}/cpu",
+                    details=[
+                        "AMD GPU detected but ROCm is not installed",
+                        "Install ROCm first for GPU acceleration: https://rocm.docs.amd.com/",
+                        "Falling back to CPU-only PyTorch",
+                    ],
+                )
+    except (subprocess.TimeoutExpired, OSError):
+        pass
+    return None
+
+
+# =============================================================================
+# Virtual Environment
+# =============================================================================
+
+
+def is_in_venv() -> bool:
+    """Check if the current Python process is running inside a venv."""
+    return sys.prefix != sys.base_prefix
+
+
+def create_venv(venv_path: str = ".venv") -> bool:
+    """Create a virtual environment and return True on success."""
+    path = Path(venv_path).resolve()
+    if path.exists():
+        logger.info(f"Venv already exists at {path}")
+        return True
+    try:
+        venv.create(str(path), with_pip=True)
+        return True
+    except Exception as exc:  # noqa: BLE001
+        logger.error(f"Failed to create venv: {exc}")
+        return False
+
+
+def get_venv_python(venv_path: str = ".venv") -> str:
+    """Return the python executable path inside a venv."""
+    path = Path(venv_path).resolve()
+    if platform.system() == "Windows":
+        return str(path / "Scripts" / "python.exe")
+    return str(path / "bin" / "python")
+
+
+def get_venv_activate_cmd(venv_path: str = ".venv") -> str:
+    """Return the shell command to activate the venv."""
+    path = Path(venv_path).resolve()
+    if platform.system() == "Windows":
+        return str(path / "Scripts" / "activate")
+    return f"source {path}/bin/activate"
+
+
+# =============================================================================
+# System Dependency Checks
+# =============================================================================
+
+
+def _detect_distro() -> str:
+    """Detect Linux distro family for install command suggestions."""
+    try:
+        with open("/etc/os-release") as f:
+            content = f.read().lower()
+        if "arch" in content or "manjaro" in content or "endeavour" in content:
+            return "arch"
+        if "debian" in content or "ubuntu" in content or "mint" in content or "pop" in content:
+            return "debian"
+        if "fedora" in content or "rhel" in content or "centos" in content or "rocky" in content:
+            return "fedora"
+        if "opensuse" in content or "suse" in content:
+            return "suse"
+    except OSError:
+        pass
+    return "unknown"
+
+
+def _get_tesseract_install_cmd() -> str:
+    """Return distro-specific command to install tesseract."""
+    distro = _detect_distro()
+    cmds = {
+        "arch": "sudo pacman -S tesseract tesseract-data-eng",
+        "debian": "sudo apt install tesseract-ocr tesseract-ocr-eng",
+        "fedora": "sudo dnf install tesseract tesseract-langpack-eng",
+        "suse": "sudo zypper install tesseract-ocr tesseract-ocr-traineddata-english",
+    }
+    return cmds.get(distro, "Install tesseract-ocr with your package manager")
+
+
+def check_tesseract() -> dict[str, bool | str]:
+    """Check if tesseract binary is installed and has English data.
+
+    Returns dict with keys: installed, has_eng, install_cmd, version.
+    """
+    result: dict[str, bool | str] = {
+        "installed": False,
+        "has_eng": False,
+        "install_cmd": _get_tesseract_install_cmd(),
+        "version": "",
+    }
+
+    tess_bin = shutil.which("tesseract")
+    if not tess_bin:
+        return result
+
+    result["installed"] = True
+
+    # Get version
+    try:
+        ver = subprocess.run(
+            ["tesseract", "--version"],
+            capture_output=True,
+            text=True,
+            timeout=5,
+        )
+        first_line = (ver.stdout or ver.stderr).split("\n")[0]
+        result["version"] = first_line.strip()
+    except (subprocess.TimeoutExpired, OSError):
+        pass
+
+    # Check for eng language data
+    try:
+        langs = subprocess.run(
+            ["tesseract", "--list-langs"],
+            capture_output=True,
+            text=True,
+            timeout=5,
+        )
+        output = langs.stdout + langs.stderr
+        result["has_eng"] = "eng" in output.split()
+    except (subprocess.TimeoutExpired, OSError):
+        pass
+
+    return result
+
+
+# =============================================================================
+# ROCm Environment Configuration
+# =============================================================================
+
+
+def configure_rocm_env() -> list[str]:
+    """Set environment variables for ROCm/MIOpen to work correctly.
+
+    Returns list of env vars that were set.
+    """
+    changes: list[str] = []
+
+    # MIOPEN_FIND_MODE=FAST avoids the workspace allocation issue
+    # where MIOpen requires huge workspace but allocates 0 bytes
+    if "MIOPEN_FIND_MODE" not in os.environ:
+        os.environ["MIOPEN_FIND_MODE"] = "FAST"
+        changes.append("MIOPEN_FIND_MODE=FAST")
+
+    # Ensure MIOpen user DB has a writable location
+    if "MIOPEN_USER_DB_PATH" not in os.environ:
+        db_path = os.path.expanduser("~/.config/miopen")
+        os.makedirs(db_path, exist_ok=True)
+        os.environ["MIOPEN_USER_DB_PATH"] = db_path
+        changes.append(f"MIOPEN_USER_DB_PATH={db_path}")
+
+    return changes
+
+
+# =============================================================================
+# Installation
+# =============================================================================
+
+
+_BASE_VIDEO_DEPS = ["yt-dlp", "youtube-transcript-api"]
+
+
+def _build_visual_deps(modules: SetupModules) -> list[str]:
+    """Build the list of pip packages based on selected modules."""
+    # Base video deps are always included — setup must leave video fully ready
+    deps: list[str] = list(_BASE_VIDEO_DEPS)
+    if modules.easyocr:
+        deps.append("easyocr")
+    if modules.opencv:
+        deps.append("opencv-python-headless")
+    if modules.tesseract:
+        deps.append("pytesseract")
+    if modules.scenedetect:
+        deps.append("scenedetect[opencv]")
+    if modules.whisper:
+        deps.append("faster-whisper")
+    return deps
+
+
+def install_torch(gpu_info: GPUInfo, python_exe: str | None = None) -> bool:
+    """Install PyTorch with the correct GPU variant.
+
+    Returns True on success, False on failure.
+    """
+    exe = python_exe or sys.executable
+    cmd = [exe, "-m", "pip", "install", "torch", "torchvision", "--index-url", gpu_info.index_url]
+    logger.info(f"Installing PyTorch from {gpu_info.index_url}")
+    try:
+        result = subprocess.run(cmd, timeout=600, capture_output=True, text=True)
+        if result.returncode != 0:
+            logger.error(f"PyTorch install failed:\n{result.stderr[-500:]}")
+            return False
+        return True
+    except subprocess.TimeoutExpired:
+        logger.error("PyTorch installation timed out (10 min)")
+        return False
+    except OSError as exc:
+        logger.error(f"PyTorch installation error: {exc}")
+        return False
+
+
+def install_visual_deps(
+    modules: SetupModules | None = None, python_exe: str | None = None
+) -> bool:
+    """Install visual extraction dependencies.
+
+    Returns True on success, False on failure.
+    """
+    mods = modules or SetupModules()
+    deps = _build_visual_deps(mods)
+    if not deps:
+        return True
+
+    exe = python_exe or sys.executable
+    cmd = [exe, "-m", "pip", "install"] + deps
+    logger.info(f"Installing visual deps: {', '.join(deps)}")
+    try:
+        result = subprocess.run(cmd, timeout=600, capture_output=True, text=True)
+        if result.returncode != 0:
+            logger.error(f"Visual deps install failed:\n{result.stderr[-500:]}")
+            return False
+        return True
+    except subprocess.TimeoutExpired:
+        logger.error("Visual deps installation timed out (10 min)")
+        return False
+    except OSError as exc:
+        logger.error(f"Visual deps installation error: {exc}")
+        return False
+
+
+def install_skill_seekers(python_exe: str) -> bool:
+    """Install skill-seekers into the target python environment."""
+    cmd = [python_exe, "-m", "pip", "install", "skill-seekers"]
+    try:
+        result = subprocess.run(cmd, timeout=300, capture_output=True, text=True)
+        return result.returncode == 0
+    except (subprocess.TimeoutExpired, OSError):
+        return False
+
+
+# =============================================================================
+# Verification
+# =============================================================================
+
+
+def verify_installation() -> dict[str, bool]:
+    """Verify that all video deps are importable.
+
+    Returns a dict mapping package name to import success.
+    """
+    results: dict[str, bool] = {}
+
+    # Base video deps
+    try:
+        import yt_dlp  # noqa: F401
+
+        results["yt-dlp"] = True
+    except ImportError:
+        results["yt-dlp"] = False
+
+    try:
+        import youtube_transcript_api  # noqa: F401
+
+        results["youtube-transcript-api"] = True
+    except ImportError:
+        results["youtube-transcript-api"] = False
+
+    # torch
+    try:
+        import torch
+
+        results["torch"] = True
+        results["torch.cuda"] = torch.cuda.is_available()
+        results["torch.rocm"] = hasattr(torch.version, "hip") and torch.version.hip is not None
+    except ImportError:
+        results["torch"] = False
+        results["torch.cuda"] = False
+        results["torch.rocm"] = False
+
+    # easyocr
+    try:
+        import easyocr  # noqa: F401
+
+        results["easyocr"] = True
+    except ImportError:
+        results["easyocr"] = False
+
+    # opencv
+    try:
+        import cv2  # noqa: F401
+
+        results["opencv"] = True
+    except ImportError:
+        results["opencv"] = False
+
+    # pytesseract
+    try:
+        import pytesseract  # noqa: F401
+
+        results["pytesseract"] = True
+    except ImportError:
+        results["pytesseract"] = False
+
+    # scenedetect
+    try:
+        import scenedetect  # noqa: F401
+
+        results["scenedetect"] = True
+    except ImportError:
+        results["scenedetect"] = False
+
+    # faster-whisper
+    try:
+        import faster_whisper  # noqa: F401
+
+        results["faster-whisper"] = True
+    except ImportError:
+        results["faster-whisper"] = False
+
+    return results
+
+
+# =============================================================================
+# Module Selection (Interactive)
+# =============================================================================
+
+
+def _ask_modules(interactive: bool) -> SetupModules:
+    """Ask the user which modules to install. Returns all if non-interactive."""
+    if not interactive:
+        return SetupModules()
+
+    print("Which modules do you want to install?")
+    print("  [a] All (default)")
+    print("  [c] Choose individually")
+    try:
+        choice = input("  > ").strip().lower()
+    except (EOFError, KeyboardInterrupt):
+        print()
+        return SetupModules()
+
+    if choice not in ("c", "choose"):
+        return SetupModules()
+
+    modules = SetupModules()
+    _ask = _interactive_yn
+
+    modules.torch = _ask("PyTorch (required for easyocr GPU)", default=True)
+    modules.easyocr = _ask("EasyOCR (text extraction from video frames)", default=True)
+    modules.opencv = _ask("OpenCV (frame extraction and image processing)", default=True)
+    modules.tesseract = _ask("pytesseract (secondary OCR engine)", default=True)
+    modules.scenedetect = _ask("scenedetect (scene change detection)", default=True)
+    modules.whisper = _ask("faster-whisper (local audio transcription)", default=True)
+
+    return modules
+
+
+def _interactive_yn(prompt: str, default: bool = True) -> bool:
+    """Ask a yes/no question, return bool."""
+    suffix = "[Y/n]" if default else "[y/N]"
+    try:
+        answer = input(f"  {prompt}? {suffix} ").strip().lower()
+    except (EOFError, KeyboardInterrupt):
+        return default
+    if not answer:
+        return default
+    return answer in ("y", "yes")
+
+
+# =============================================================================
+# Orchestrator
+# =============================================================================
+
+
+def run_setup(interactive: bool = True) -> int:
+    """Auto-detect GPU and install all visual extraction dependencies.
+
+    Handles:
+    1. Venv creation (if not in one)
+    2. GPU detection
+    3. Module selection (optional — interactive only)
+    4. System dep checks (tesseract binary)
+    5. ROCm env var configuration
+    6. PyTorch installation (correct GPU variant)
+    7. Visual deps installation
+    8. Verification
+
+    Args:
+        interactive: If True, prompt user for confirmation before installing.
+
+    Returns:
+        0 on success, 1 on failure.
+    """
+    print("=" * 60)
+    print("  Video Visual Extraction Setup")
+    print("=" * 60)
+    print()
+
+    total_steps = 7
+
+    # ── Step 1: Venv check ──
+    print(f"[1/{total_steps}] Checking environment...")
+    if is_in_venv():
+        print(f"  Already in venv: {sys.prefix}")
+        python_exe = sys.executable
+    else:
+        print("  Not in a virtual environment.")
+        venv_path = ".venv"
+        if interactive:
+            try:
+                answer = input(
+                    f"  Create venv at ./{venv_path}? [Y/n] "
+                ).strip().lower()
+            except (EOFError, KeyboardInterrupt):
+                print("\nSetup cancelled.")
+                return 1
+            if answer and answer not in ("y", "yes"):
+                print("  Continuing without venv (installing to system Python).")
+                python_exe = sys.executable
+            else:
+                if not create_venv(venv_path):
+                    print("  FAILED: Could not create venv.")
+                    return 1
+                python_exe = get_venv_python(venv_path)
+                activate_cmd = get_venv_activate_cmd(venv_path)
+                print(f"  Venv created at ./{venv_path}")
+                print(f"  Installing skill-seekers into venv...")
+                if not install_skill_seekers(python_exe):
+                    print("  FAILED: Could not install skill-seekers into venv.")
+                    return 1
+                print(f"  After setup completes, activate with:")
+                print(f"    {activate_cmd}")
+        else:
+            # Non-interactive: use current python
+            python_exe = sys.executable
+    print()
+
+    # ── Step 2: GPU detection ──
+    print(f"[2/{total_steps}] Detecting GPU...")
+    gpu_info = detect_gpu()
+
+    vendor_label = {
+        GPUVendor.NVIDIA: "NVIDIA (CUDA)",
+        GPUVendor.AMD: "AMD (ROCm)",
+        GPUVendor.NONE: "CPU-only",
+    }
+    print(f"  GPU:    {gpu_info.name}")
+    print(f"  Vendor: {vendor_label.get(gpu_info.vendor, gpu_info.vendor.value)}")
+    if gpu_info.compute_version:
+        print(f"  Version: {gpu_info.compute_version}")
+    for detail in gpu_info.details:
+        print(f"  {detail}")
+    print(f"  PyTorch index: {gpu_info.index_url}")
+    print()
+
+    # ── Step 3: Module selection ──
+    print(f"[3/{total_steps}] Selecting modules...")
+    modules = _ask_modules(interactive)
+    deps = _build_visual_deps(modules)
+    print(f"  Selected: {', '.join(deps) if deps else '(none)'}")
+    if modules.torch:
+        print(f"  + PyTorch + torchvision")
+    print()
+
+    # ── Step 4: System dependency check ──
+    print(f"[4/{total_steps}] Checking system dependencies...")
+    if modules.tesseract:
+        tess = check_tesseract()
+        if not tess["installed"]:
+            print(f"  WARNING: tesseract binary not found!")
+            print(f"  The pytesseract Python package needs the tesseract binary installed.")
+            print(f"  Install it with: {tess['install_cmd']}")
+            print()
+        elif not tess["has_eng"]:
+            print(f"  WARNING: tesseract installed ({tess['version']}) but English data missing!")
+            print(f"  Install with: {tess['install_cmd']}")
+            print()
+        else:
+            print(f"  tesseract: {tess['version']} (eng data OK)")
+    else:
+        print("  tesseract: skipped (not selected)")
+    print()
+
+    # ── Step 5: ROCm configuration ──
+    print(f"[5/{total_steps}] Configuring GPU environment...")
+    if gpu_info.vendor == GPUVendor.AMD:
+        changes = configure_rocm_env()
+        if changes:
+            print("  Set ROCm environment variables:")
+            for c in changes:
+                print(f"    {c}")
+            print("  (These fix MIOpen workspace allocation issues)")
+        else:
+            print("  ROCm env vars already configured.")
+    elif gpu_info.vendor == GPUVendor.NVIDIA:
+        print("  NVIDIA: no extra configuration needed.")
+    else:
+        print("  CPU-only: no GPU configuration needed.")
+    print()
+
+    # ── Step 6: Confirm and install ──
+    if interactive:
+        print("Ready to install. Summary:")
+        if modules.torch:
+            print(f"  - PyTorch + torchvision (from {gpu_info.index_url})")
+        for dep in deps:
+            print(f"  - {dep}")
+        print()
+        try:
+            answer = input("Proceed? [Y/n] ").strip().lower()
+        except (EOFError, KeyboardInterrupt):
+            print("\nSetup cancelled.")
+            return 1
+        if answer and answer not in ("y", "yes"):
+            print("Setup cancelled.")
+            return 1
+        print()
+
+    print(f"[6/{total_steps}] Installing packages...")
+    if modules.torch:
+        print("  Installing PyTorch...")
+        if not install_torch(gpu_info, python_exe):
+            print("  FAILED: PyTorch installation failed.")
+            print(f"  Try: {python_exe} -m pip install torch torchvision --index-url {gpu_info.index_url}")
+            return 1
+        print("  PyTorch installed.")
+
+    if deps:
+        print("  Installing visual packages...")
+        if not install_visual_deps(modules, python_exe):
+            print("  FAILED: Visual packages installation failed.")
+            print(f"  Try: {python_exe} -m pip install {' '.join(deps)}")
+            return 1
+        print("  Visual packages installed.")
+    print()
+
+    # ── Step 7: Verify ──
+    print(f"[7/{total_steps}] Verifying installation...")
+    results = verify_installation()
+    all_ok = True
+    for pkg, ok in results.items():
+        status = "OK" if ok else "MISSING"
+        print(f"  {pkg}: {status}")
+        # torch.cuda / torch.rocm are informational, not required
+        if not ok and pkg not in ("torch.cuda", "torch.rocm"):
+            # Only count as failure if the module was selected
+            if pkg == "torch" and modules.torch:
+                all_ok = False
+            elif pkg == "easyocr" and modules.easyocr:
+                all_ok = False
+            elif pkg == "opencv" and modules.opencv:
+                all_ok = False
+            elif pkg == "pytesseract" and modules.tesseract:
+                all_ok = False
+            elif pkg == "scenedetect" and modules.scenedetect:
+                all_ok = False
+            elif pkg == "faster-whisper" and modules.whisper:
+                all_ok = False
+
+    print()
+    if all_ok:
+        print("Setup complete! You can now use: skill-seekers video --url <URL> --visual")
+        if not is_in_venv() and python_exe != sys.executable:
+            activate_cmd = get_venv_activate_cmd()
+            print(f"\nDon't forget to activate the venv first:")
+            print(f"  {activate_cmd}")
+    else:
+        print("Some packages failed to install. Check the output above.")
+        return 1
+
+    return 0

src/skill_seekers/cli/video_visual.py

@@ -13,6 +13,7 @@ from __future__ import annotations
 
 import concurrent.futures
 import difflib
+import gc
 import logging
 import os
 import tempfile
@@ -32,6 +33,18 @@ from skill_seekers.cli.video_models import (
 
 logger = logging.getLogger(__name__)
 
+# Set ROCm/MIOpen env vars BEFORE importing torch (via easyocr).
+# Without MIOPEN_FIND_MODE=FAST, MIOpen tries to allocate huge workspace
+# buffers (300MB+), gets 0 bytes, and silently falls back to CPU kernels.
+import os as _os
+
+if "MIOPEN_FIND_MODE" not in _os.environ:
+    _os.environ["MIOPEN_FIND_MODE"] = "FAST"
+if "MIOPEN_USER_DB_PATH" not in _os.environ:
+    _miopen_db = _os.path.expanduser("~/.config/miopen")
+    _os.makedirs(_miopen_db, exist_ok=True)
+    _os.environ["MIOPEN_USER_DB_PATH"] = _miopen_db
+
 # Tier 2 dependency flags
 try:
     import cv2
@@ -65,23 +78,46 @@ except ImportError:
     pytesseract = None  # type: ignore[assignment]
     HAS_PYTESSERACT = False
 
+# Circuit breaker: after first tesseract failure, disable it for the session.
+# Prevents wasting time spawning subprocesses that always fail.
+_tesseract_broken = False
+
 
 _INSTALL_MSG = (
     "Visual extraction requires additional dependencies.\n"
-    'Install with: pip install "skill-seekers[video-full]"\n'
+    "Recommended: skill-seekers video --setup  (auto-detects GPU, installs correct PyTorch)\n"
-    "Or: pip install opencv-python-headless scenedetect easyocr"
+    'Alternative:  pip install "skill-seekers[video-full]"  (may install wrong PyTorch variant)'
 )
 
 # Lazy-initialized EasyOCR reader (heavy, only load once)
 _ocr_reader = None
 
 
+def _detect_gpu() -> bool:
+    """Check if a CUDA or ROCm GPU is available for EasyOCR/PyTorch."""
+    try:
+        import torch
+
+        if torch.cuda.is_available():
+            return True
+        # ROCm exposes GPU via torch.version.hip
+        if hasattr(torch.version, "hip") and torch.version.hip is not None:
+            return True
+        return False
+    except ImportError:
+        return False
+
+
 def _get_ocr_reader():
     """Get or create the EasyOCR reader (lazy singleton)."""
     global _ocr_reader
     if _ocr_reader is None:
-        logger.info("Initializing OCR engine (first run may download models)...")
+        use_gpu = _detect_gpu()
-        _ocr_reader = easyocr.Reader(["en"], gpu=False)
+        logger.info(
+            f"Initializing OCR engine ({'GPU' if use_gpu else 'CPU'} mode, "
+            "first run may download models)..."
+        )
+        _ocr_reader = easyocr.Reader(["en"], gpu=use_gpu)
     return _ocr_reader
 
 
@@ -296,11 +332,15 @@ def _run_tesseract_ocr(preprocessed_path: str, frame_type: FrameType) -> list[tu
     Returns results in the same format as EasyOCR: list of (bbox, text, confidence).
     Groups words into lines by y-coordinate.
 
+    Uses a circuit breaker: if tesseract fails once, it's disabled for the
+    rest of the session to avoid wasting time on repeated subprocess failures.
+
     Args:
         preprocessed_path: Path to the preprocessed grayscale image.
         frame_type: Frame classification (reserved for future per-type tuning).
     """
-    if not HAS_PYTESSERACT:
+    global _tesseract_broken
+    if not HAS_PYTESSERACT or _tesseract_broken:
         return []
 
     # Produce clean binary for Tesseract
@@ -312,7 +352,11 @@ def _run_tesseract_ocr(preprocessed_path: str, frame_type: FrameType) -> list[tu
             output_type=pytesseract.Output.DICT,
         )
     except Exception:  # noqa: BLE001
-        logger.debug("pytesseract failed, returning empty results")
+        _tesseract_broken = True
+        logger.warning(
+            "pytesseract failed — disabling for this session. "
+            "Install tesseract binary: skill-seekers video --setup"
+        )
         return []
     finally:
         if binary_path != preprocessed_path and os.path.exists(binary_path):
@@ -897,6 +941,25 @@ def _crop_code_region(frame_path: str, bbox: tuple[int, int, int, int], suffix:
     return cropped_path
 
 
+def _frame_type_from_regions(
+    regions: list[tuple[int, int, int, int, FrameType]],
+) -> FrameType:
+    """Derive the dominant frame type from pre-computed regions.
+
+    Same logic as ``classify_frame`` but avoids re-loading the image.
+    """
+    for _x1, _y1, _x2, _y2, ft in regions:
+        if ft == FrameType.TERMINAL:
+            return FrameType.TERMINAL
+        if ft == FrameType.CODE_EDITOR:
+            return FrameType.CODE_EDITOR
+
+    from collections import Counter
+
+    type_counts = Counter(ft for _, _, _, _, ft in regions)
+    return type_counts.most_common(1)[0][0] if type_counts else FrameType.OTHER
+
+
 def classify_frame(frame_path: str) -> FrameType:
     """Classify a video frame by its visual content.
 
@@ -1114,6 +1177,8 @@ def _compute_frame_timestamps(
     duration: float,
     sample_interval: float = 0.7,
     min_gap: float = 0.5,
+    start_offset: float = 0.0,
+    end_limit: float | None = None,
 ) -> list[float]:
     """Build a deduplicated list of timestamps to extract frames at.
 
@@ -1126,10 +1191,13 @@ def _compute_frame_timestamps(
         duration: Total video duration in seconds.
         sample_interval: Seconds between interval samples.
         min_gap: Minimum gap between kept timestamps.
+        start_offset: Start sampling at this time (seconds).
+        end_limit: Stop sampling at this time (seconds). None = full duration.
 
     Returns:
         Sorted, deduplicated list of timestamps (seconds).
     """
+    effective_end = end_limit if end_limit is not None else duration
     timestamps: set[float] = set()
 
     # 1. Scene detection — catches cuts, slide transitions, editor switches
@@ -1138,19 +1206,21 @@ def _compute_frame_timestamps(
             scenes = detect_scenes(video_path)
             for start, _end in scenes:
                 # Take frame 0.5s after the scene starts (avoids transition blur)
-                timestamps.add(round(start + 0.5, 1))
+                ts = round(start + 0.5, 1)
+                if ts >= start_offset and ts < effective_end:
+                    timestamps.add(ts)
         except Exception as exc:  # noqa: BLE001
             logger.warning(f"Scene detection failed, falling back to interval: {exc}")
 
     # 2. Regular interval sampling — fills gaps between scene cuts
-    t = 0.5  # start slightly after 0 to avoid black intro frames
+    t = max(0.5, start_offset)
-    while t < duration:
+    while t < effective_end:
         timestamps.add(round(t, 1))
         t += sample_interval
 
     # Always include near the end
-    if duration > 2.0:
+    if effective_end > 2.0:
-        timestamps.add(round(duration - 1.0, 1))
+        timestamps.add(round(effective_end - 1.0, 1))
 
     # 3. Sort and deduplicate (merge timestamps closer than min_gap)
     sorted_ts = sorted(timestamps)
@@ -1876,6 +1946,8 @@ def extract_visual_data(
     min_gap: float = 0.5,
     similarity_threshold: float = 3.0,
     use_vision_api: bool = False,
+    clip_start: float | None = None,
+    clip_end: float | None = None,
 ) -> tuple[list[KeyFrame], list[CodeBlock], TextGroupTimeline | None]:
     """Run continuous visual extraction on a video.
 
@@ -1899,6 +1971,8 @@ def extract_visual_data(
         similarity_threshold: Pixel-diff threshold for duplicate detection (default 3.0).
         use_vision_api: If True, use Claude Vision API as fallback for low-confidence
             code frames (requires ANTHROPIC_API_KEY).
+        clip_start: Start of clip range in seconds (None = beginning).
+        clip_end: End of clip range in seconds (None = full duration).
 
     Returns:
         Tuple of (keyframes, code_blocks, text_group_timeline).
@@ -1937,7 +2011,12 @@ def extract_visual_data(
 
     # Build candidate timestamps
     timestamps = _compute_frame_timestamps(
-        video_path, duration, sample_interval=sample_interval, min_gap=min_gap
+        video_path,
+        duration,
+        sample_interval=sample_interval,
+        min_gap=min_gap,
+        start_offset=clip_start or 0.0,
+        end_limit=clip_end,
     )
     logger.info(f"  {len(timestamps)} candidate timestamps after dedup")
 
@@ -1961,17 +2040,21 @@ def extract_visual_data(
             skipped_similar += 1
             continue
         prev_frame = frame.copy()
+        frame_h, frame_w = frame.shape[:2]
 
         # Save frame
         idx = len(keyframes)
         frame_filename = f"frame_{idx:03d}_{ts:.0f}s.jpg"
         frame_path = os.path.join(frames_dir, frame_filename)
         cv2.imwrite(frame_path, frame)
+        del frame  # Free the numpy array early — saved to disk
 
         # Classify using region-based panel detection
         regions = classify_frame_regions(frame_path)
         code_panels = _get_code_panels(regions)
-        frame_type = classify_frame(frame_path)  # dominant type for metadata
+        # Derive frame_type from already-computed regions (avoids loading
+        # the image a second time — classify_frame() would repeat the work).
+        frame_type = _frame_type_from_regions(regions)
         is_code_frame = frame_type in (FrameType.CODE_EDITOR, FrameType.TERMINAL)
 
         # Per-panel OCR: each code/terminal panel is OCR'd independently
@@ -1982,11 +2065,13 @@ def extract_visual_data(
         ocr_confidence = 0.0
 
         if is_code_frame and code_panels and (HAS_EASYOCR or HAS_PYTESSERACT):
-            full_area = frame.shape[0] * frame.shape[1]
+            full_area = frame_h * frame_w
 
             if len(code_panels) > 1:
                 # Parallel OCR — each panel is independent
-                with concurrent.futures.ThreadPoolExecutor(max_workers=len(code_panels)) as pool:
+                with concurrent.futures.ThreadPoolExecutor(
+                    max_workers=min(2, len(code_panels))
+                ) as pool:
                     futures = {
                         pool.submit(
                             _ocr_single_panel,
@@ -2084,8 +2169,8 @@ def extract_visual_data(
             ocr_text=ocr_text,
             ocr_regions=ocr_regions,
             ocr_confidence=ocr_confidence,
-            width=frame.shape[1],
+            width=frame_w,
-            height=frame.shape[0],
+            height=frame_h,
             sub_sections=sub_sections,
         )
         keyframes.append(kf)
@@ -2101,6 +2186,10 @@ def extract_visual_data(
             )
         )
 
+        # Periodically collect to free PyTorch/numpy memory
+        if idx % 10 == 9:
+            gc.collect()
+
     cap.release()
 
     # Finalize text tracking and extract code blocks
@@ -2131,7 +2220,12 @@ def extract_visual_data(
     return keyframes, code_blocks, timeline
 
 
-def download_video(url: str, output_dir: str) -> str | None:
+def download_video(
+    url: str,
+    output_dir: str,
+    clip_start: float | None = None,
+    clip_end: float | None = None,
+) -> str | None:
     """Download a video using yt-dlp for visual processing.
 
     Downloads the best quality up to 1080p. Uses separate video+audio streams
@@ -2142,6 +2236,8 @@ def download_video(url: str, output_dir: str) -> str | None:
     Args:
         url: Video URL.
         output_dir: Directory to save the downloaded file.
+        clip_start: Download from this time (seconds). None = beginning.
+        clip_end: Download until this time (seconds). None = full video.
 
     Returns:
         Path to downloaded video file, or None on failure.
@@ -2156,13 +2252,30 @@ def download_video(url: str, output_dir: str) -> str | None:
     output_template = os.path.join(output_dir, "video.%(ext)s")
 
     opts = {
-        "format": "bestvideo[height<=1080]+bestaudio/best[height<=1080]",
+        "format": (
+            "bestvideo[height<=1080][vcodec^=avc1]+bestaudio/best[height<=1080][vcodec^=avc1]/"
+            "bestvideo[height<=1080][vcodec^=h264]+bestaudio/best[height<=1080][vcodec^=h264]/"
+            "bestvideo[height<=1080]+bestaudio/best[height<=1080]"
+        ),
         "merge_output_format": "mp4",
         "outtmpl": output_template,
         "quiet": True,
         "no_warnings": True,
     }
 
+    # Apply download_ranges for clip support (yt-dlp 2023.01.02+)
+    if clip_start is not None or clip_end is not None:
+        try:
+            from yt_dlp.utils import download_range_func
+
+            ranges = [(clip_start or 0, clip_end or float("inf"))]
+            opts["download_ranges"] = download_range_func(None, ranges)
+        except (ImportError, TypeError):
+            logger.warning(
+                "yt-dlp version does not support download_ranges; "
+                "downloading full video and relying on frame timestamp filtering"
+            )
+
     logger.info(f"Downloading video for visual extraction...")
     try:
         with yt_dlp.YoutubeDL(opts) as ydl:

src/skill_seekers/mcp/server_fastmcp.py

@@ -440,6 +440,9 @@ async def scrape_video(
     visual_min_gap: float | None = None,
     visual_similarity: float | None = None,
     vision_ocr: bool = False,
+    start_time: str | None = None,
+    end_time: str | None = None,
+    setup: bool = False,
 ) -> str:
     """
     Scrape video content and build Claude skill.
@@ -458,10 +461,19 @@ async def scrape_video(
         visual_min_gap: Minimum seconds between kept frames (default: 2.0)
         visual_similarity: Similarity threshold to skip duplicate frames 0.0-1.0 (default: 0.95)
         vision_ocr: Use vision model for OCR on extracted frames
+        start_time: Start time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.
+        end_time: End time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.
+        setup: Auto-detect GPU and install visual extraction deps (PyTorch, easyocr, etc.)
 
     Returns:
         Video scraping results with file paths.
     """
+    if setup:
+        from skill_seekers.cli.video_setup import run_setup
+
+        rc = run_setup(interactive=False)
+        return "Setup completed successfully." if rc == 0 else "Setup failed. Check logs."
+
     args = {}
     if url:
         args["url"] = url
@@ -477,6 +489,10 @@ async def scrape_video(
         args["languages"] = languages
     if from_json:
         args["from_json"] = from_json
+    if start_time:
+        args["start_time"] = start_time
+    if end_time:
+        args["end_time"] = end_time
     if visual:
         args["visual"] = visual
     if whisper_model:

src/skill_seekers/mcp/tools/scraping_tools.py

@@ -378,10 +378,21 @@ async def scrape_video_tool(args: dict) -> list[TextContent]:
             - visual_min_gap (float, optional): Minimum seconds between kept frames (default: 2.0)
             - visual_similarity (float, optional): Similarity threshold to skip duplicate frames (default: 0.95)
             - vision_ocr (bool, optional): Use vision model for OCR on frames (default: False)
+            - start_time (str, optional): Start time for extraction (seconds, MM:SS, or HH:MM:SS)
+            - end_time (str, optional): End time for extraction (seconds, MM:SS, or HH:MM:SS)
+            - setup (bool, optional): Auto-detect GPU and install visual extraction deps
 
     Returns:
         List[TextContent]: Tool execution results
     """
+    # Handle --setup early exit
+    if args.get("setup", False):
+        from skill_seekers.cli.video_setup import run_setup
+
+        rc = run_setup(interactive=False)
+        msg = "Setup completed successfully." if rc == 0 else "Setup failed. Check logs."
+        return [TextContent(type="text", text=msg)]
+
     url = args.get("url")
     video_file = args.get("video_file")
     playlist = args.get("playlist")
@@ -395,6 +406,8 @@ async def scrape_video_tool(args: dict) -> list[TextContent]:
     visual_min_gap = args.get("visual_min_gap")
     visual_similarity = args.get("visual_similarity")
     vision_ocr = args.get("vision_ocr", False)
+    start_time = args.get("start_time")
+    end_time = args.get("end_time")
 
     # Build command
     cmd = [sys.executable, str(CLI_DIR / "video_scraper.py")]
@@ -440,6 +453,10 @@ async def scrape_video_tool(args: dict) -> list[TextContent]:
         cmd.extend(["--visual-similarity", str(visual_similarity)])
     if vision_ocr:
         cmd.append("--vision-ocr")
+    if start_time:
+        cmd.extend(["--start-time", str(start_time)])
+    if end_time:
+        cmd.extend(["--end-time", str(end_time)])
 
     # Run video_scraper.py with streaming
     timeout = 600  # 10 minutes for video extraction

tests/test_video_scraper.py

@@ -3115,5 +3115,286 @@ class TestVideoWorkflowAutoInjection(unittest.TestCase):
             self.assertTrue(yaml_path.exists(), "video-tutorial.yaml not found")
 
 
+# =============================================================================
+# Test: Time Clipping (--start-time / --end-time)
+# =============================================================================
+
+
+class TestTimeClipping(unittest.TestCase):
+    """Test --start-time / --end-time clipping support."""
+
+    # ---- parse_time_to_seconds() ----
+
+    def test_parse_time_seconds_integer(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        self.assertEqual(parse_time_to_seconds("330"), 330.0)
+
+    def test_parse_time_seconds_float(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        self.assertAlmostEqual(parse_time_to_seconds("90.5"), 90.5)
+
+    def test_parse_time_mmss(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        self.assertEqual(parse_time_to_seconds("5:30"), 330.0)
+
+    def test_parse_time_hhmmss(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        self.assertEqual(parse_time_to_seconds("1:05:30"), 3930.0)
+
+    def test_parse_time_zero(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        self.assertEqual(parse_time_to_seconds("0"), 0.0)
+        self.assertEqual(parse_time_to_seconds("0:00"), 0.0)
+        self.assertEqual(parse_time_to_seconds("0:00:00"), 0.0)
+
+    def test_parse_time_decimal_mmss(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        self.assertAlmostEqual(parse_time_to_seconds("1:30.5"), 90.5)
+
+    def test_parse_time_invalid_raises(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        with self.assertRaises(ValueError):
+            parse_time_to_seconds("abc")
+
+    def test_parse_time_empty_raises(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        with self.assertRaises(ValueError):
+            parse_time_to_seconds("")
+
+    def test_parse_time_too_many_colons_raises(self):
+        from skill_seekers.cli.video_scraper import parse_time_to_seconds
+
+        with self.assertRaises(ValueError):
+            parse_time_to_seconds("1:2:3:4")
+
+    # ---- Argument registration ----
+
+    def test_video_arguments_include_start_end_time(self):
+        from skill_seekers.cli.arguments.video import VIDEO_ARGUMENTS
+
+        self.assertIn("start_time", VIDEO_ARGUMENTS)
+        self.assertIn("end_time", VIDEO_ARGUMENTS)
+
+    def test_create_arguments_include_start_end_time(self):
+        from skill_seekers.cli.arguments.create import VIDEO_ARGUMENTS
+
+        self.assertIn("start_time", VIDEO_ARGUMENTS)
+        self.assertIn("end_time", VIDEO_ARGUMENTS)
+
+    def test_argument_parsing_defaults_none(self):
+        import argparse
+        from skill_seekers.cli.arguments.video import add_video_arguments
+
+        parser = argparse.ArgumentParser()
+        add_video_arguments(parser)
+        args = parser.parse_args(["--url", "https://example.com"])
+        self.assertIsNone(args.start_time)
+        self.assertIsNone(args.end_time)
+
+    def test_argument_parsing_with_values(self):
+        import argparse
+        from skill_seekers.cli.arguments.video import add_video_arguments
+
+        parser = argparse.ArgumentParser()
+        add_video_arguments(parser)
+        args = parser.parse_args(
+            ["--url", "https://example.com", "--start-time", "2:00", "--end-time", "5:00"]
+        )
+        self.assertEqual(args.start_time, "2:00")
+        self.assertEqual(args.end_time, "5:00")
+
+    # ---- Transcript filtering ----
+
+    def test_transcript_clip_filters_segments(self):
+        """Verify transcript segments are filtered to clip range."""
+        from skill_seekers.cli.video_models import TranscriptSegment
+
+        segments = [
+            TranscriptSegment(text="intro", start=0.0, end=30.0),
+            TranscriptSegment(text="part1", start=30.0, end=90.0),
+            TranscriptSegment(text="part2", start=90.0, end=150.0),
+            TranscriptSegment(text="outro", start=150.0, end=200.0),
+        ]
+
+        clip_start, clip_end = 60.0, 120.0
+        filtered = [s for s in segments if s.end > clip_start and s.start < clip_end]
+        # part1 (30-90) overlaps with 60-120, part2 (90-150) overlaps with 60-120
+        self.assertEqual(len(filtered), 2)
+        self.assertEqual(filtered[0].text, "part1")
+        self.assertEqual(filtered[1].text, "part2")
+
+    def test_transcript_clip_start_only(self):
+        """Verify only clip_start filters correctly."""
+        from skill_seekers.cli.video_models import TranscriptSegment
+
+        segments = [
+            TranscriptSegment(text="before", start=0.0, end=50.0),
+            TranscriptSegment(text="after", start=50.0, end=100.0),
+        ]
+        clip_start = 50.0
+        clip_end = float("inf")
+        filtered = [s for s in segments if s.end > clip_start and s.start < clip_end]
+        self.assertEqual(len(filtered), 1)
+        self.assertEqual(filtered[0].text, "after")
+
+    # ---- Validation ----
+
+    def test_playlist_plus_clip_rejected(self):
+        from skill_seekers.cli.video_models import VideoSourceConfig
+
+        config = VideoSourceConfig(
+            playlist="https://youtube.com/playlist?list=x",
+            clip_start=60.0,
+        )
+        errors = config.validate()
+        self.assertTrue(any("--start-time" in e for e in errors))
+
+    def test_start_gte_end_rejected(self):
+        from skill_seekers.cli.video_models import VideoSourceConfig
+
+        config = VideoSourceConfig(
+            url="https://youtube.com/watch?v=x", clip_start=300.0, clip_end=120.0
+        )
+        errors = config.validate()
+        self.assertTrue(any("must be before" in e for e in errors))
+
+    def test_valid_clip_no_errors(self):
+        from skill_seekers.cli.video_models import VideoSourceConfig
+
+        config = VideoSourceConfig(
+            url="https://youtube.com/watch?v=x", clip_start=60.0, clip_end=300.0
+        )
+        errors = config.validate()
+        self.assertEqual(errors, [])
+
+    # ---- VideoInfo clip metadata serialization ----
+
+    def test_video_info_clip_roundtrip(self):
+        from skill_seekers.cli.video_models import VideoInfo, VideoSourceType
+
+        info = VideoInfo(
+            video_id="test",
+            source_type=VideoSourceType.YOUTUBE,
+            duration=300.0,
+            original_duration=600.0,
+            clip_start=120.0,
+            clip_end=420.0,
+        )
+        data = info.to_dict()
+        self.assertEqual(data["original_duration"], 600.0)
+        self.assertEqual(data["clip_start"], 120.0)
+        self.assertEqual(data["clip_end"], 420.0)
+
+        restored = VideoInfo.from_dict(data)
+        self.assertEqual(restored.original_duration, 600.0)
+        self.assertEqual(restored.clip_start, 120.0)
+        self.assertEqual(restored.clip_end, 420.0)
+
+    def test_video_info_no_clip_roundtrip(self):
+        from skill_seekers.cli.video_models import VideoInfo, VideoSourceType
+
+        info = VideoInfo(video_id="test", source_type=VideoSourceType.YOUTUBE)
+        data = info.to_dict()
+        self.assertIsNone(data["original_duration"])
+        self.assertIsNone(data["clip_start"])
+        self.assertIsNone(data["clip_end"])
+
+        restored = VideoInfo.from_dict(data)
+        self.assertIsNone(restored.original_duration)
+        self.assertIsNone(restored.clip_start)
+
+    # ---- VideoSourceConfig clip fields ----
+
+    def test_source_config_clip_fields(self):
+        from skill_seekers.cli.video_models import VideoSourceConfig
+
+        config = VideoSourceConfig.from_dict(
+            {
+                "url": "https://example.com",
+                "clip_start": 10.0,
+                "clip_end": 60.0,
+            }
+        )
+        self.assertEqual(config.clip_start, 10.0)
+        self.assertEqual(config.clip_end, 60.0)
+
+    def test_source_config_clip_defaults_none(self):
+        from skill_seekers.cli.video_models import VideoSourceConfig
+
+        config = VideoSourceConfig.from_dict({"url": "https://example.com"})
+        self.assertIsNone(config.clip_start)
+        self.assertIsNone(config.clip_end)
+
+    # ---- Converter init ----
+
+    def test_converter_init_with_clip_times(self):
+        from skill_seekers.cli.video_scraper import VideoToSkillConverter
+
+        config = {
+            "name": "test",
+            "url": "https://youtube.com/watch?v=x",
+            "start_time": 120.0,
+            "end_time": 300.0,
+        }
+        converter = VideoToSkillConverter(config)
+        self.assertEqual(converter.start_time, 120.0)
+        self.assertEqual(converter.end_time, 300.0)
+
+    def test_converter_init_without_clip_times(self):
+        from skill_seekers.cli.video_scraper import VideoToSkillConverter
+
+        config = {"name": "test", "url": "https://youtube.com/watch?v=x"}
+        converter = VideoToSkillConverter(config)
+        self.assertIsNone(converter.start_time)
+        self.assertIsNone(converter.end_time)
+
+    # ---- Segmenter start_offset / end_limit ----
+
+    def test_segmenter_time_window_with_offset(self):
+        from skill_seekers.cli.video_segmenter import segment_by_time_window
+        from skill_seekers.cli.video_models import VideoInfo, VideoSourceType
+
+        info = VideoInfo(video_id="test", source_type=VideoSourceType.YOUTUBE, duration=600.0)
+        # Use 120s windows starting at 120s, ending at 360s
+        segments = segment_by_time_window(
+            info, [], window_seconds=120.0, start_offset=120.0, end_limit=360.0
+        )
+        # No transcript segments so no segments generated, but verify no crash
+        self.assertEqual(len(segments), 0)
+
+    def test_segmenter_time_window_offset_with_transcript(self):
+        from skill_seekers.cli.video_segmenter import segment_by_time_window
+        from skill_seekers.cli.video_models import (
+            VideoInfo,
+            VideoSourceType,
+            TranscriptSegment,
+        )
+
+        info = VideoInfo(video_id="test", source_type=VideoSourceType.YOUTUBE, duration=600.0)
+        transcript = [
+            TranscriptSegment(text="before clip", start=0.0, end=60.0),
+            TranscriptSegment(text="in clip part1", start=120.0, end=180.0),
+            TranscriptSegment(text="in clip part2", start=200.0, end=300.0),
+            TranscriptSegment(text="after clip", start=400.0, end=500.0),
+        ]
+        segments = segment_by_time_window(
+            info, transcript, window_seconds=120.0, start_offset=120.0, end_limit=360.0
+        )
+        # Should have segments starting at 120, 240
+        self.assertTrue(len(segments) >= 1)
+        # All segments should be within clip range
+        for seg in segments:
+            self.assertGreaterEqual(seg.start_time, 120.0)
+            self.assertLessEqual(seg.end_time, 360.0)
+
+
 if __name__ == "__main__":
     unittest.main()

tests/test_video_setup.py

@@ -0,0 +1,679 @@
+#!/usr/bin/env python3
+"""
+Tests for Video Setup (cli/video_setup.py) and video_visual.py resilience.
+
+Tests cover:
+- GPU detection (NVIDIA, AMD ROCm, AMD without ROCm, CPU fallback)
+- CUDA / ROCm version → index URL mapping
+- PyTorch installation (mocked subprocess)
+- Visual deps installation (mocked subprocess)
+- Installation verification
+- run_setup orchestrator
+- Venv detection and creation
+- System dep checks (tesseract binary)
+- ROCm env var configuration
+- Module selection (SetupModules)
+- Tesseract circuit breaker (video_visual.py)
+- --setup flag in VIDEO_ARGUMENTS and early-exit in video_scraper
+"""
+
+import os
+import subprocess
+import sys
+import tempfile
+import unittest
+from unittest.mock import MagicMock, patch
+
+from skill_seekers.cli.video_setup import (
+    _BASE_VIDEO_DEPS,
+    GPUInfo,
+    GPUVendor,
+    SetupModules,
+    _build_visual_deps,
+    _cuda_version_to_index_url,
+    _detect_distro,
+    _PYTORCH_BASE,
+    _rocm_version_to_index_url,
+    check_tesseract,
+    configure_rocm_env,
+    create_venv,
+    detect_gpu,
+    get_venv_activate_cmd,
+    get_venv_python,
+    install_torch,
+    install_visual_deps,
+    is_in_venv,
+    run_setup,
+    verify_installation,
+)
+
+
+# =============================================================================
+# GPU Detection Tests
+# =============================================================================
+
+
+class TestGPUDetection(unittest.TestCase):
+    """Tests for detect_gpu() and its helpers."""
+
+    @patch("skill_seekers.cli.video_setup.shutil.which")
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_nvidia_detected(self, mock_run, mock_which):
+        """nvidia-smi present → GPUVendor.NVIDIA."""
+        mock_which.side_effect = lambda cmd: "/usr/bin/nvidia-smi" if cmd == "nvidia-smi" else None
+        mock_run.return_value = MagicMock(
+            returncode=0,
+            stdout=(
+                "+-------------------------+\n"
+                "| NVIDIA GeForce RTX 4090  On |\n"
+                "| CUDA Version: 12.4      |\n"
+                "+-------------------------+\n"
+            ),
+        )
+        gpu = detect_gpu()
+        assert gpu.vendor == GPUVendor.NVIDIA
+        assert "12.4" in gpu.compute_version
+        assert "cu124" in gpu.index_url
+
+    @patch("skill_seekers.cli.video_setup.shutil.which")
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    @patch("skill_seekers.cli.video_setup._read_rocm_version", return_value="6.3.1")
+    def test_amd_rocm_detected(self, mock_rocm_ver, mock_run, mock_which):
+        """rocminfo present → GPUVendor.AMD."""
+
+        def which_side(cmd):
+            if cmd == "nvidia-smi":
+                return None
+            if cmd == "rocminfo":
+                return "/usr/bin/rocminfo"
+            return None
+
+        mock_which.side_effect = which_side
+        mock_run.return_value = MagicMock(
+            returncode=0,
+            stdout="Marketing Name: AMD Radeon RX 7900 XTX\n",
+        )
+        gpu = detect_gpu()
+        assert gpu.vendor == GPUVendor.AMD
+        assert "rocm6.3" in gpu.index_url
+
+    @patch("skill_seekers.cli.video_setup.shutil.which")
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_amd_no_rocm_fallback(self, mock_run, mock_which):
+        """AMD GPU in lspci but no ROCm → AMD vendor, CPU index URL."""
+
+        def which_side(cmd):
+            if cmd == "lspci":
+                return "/usr/bin/lspci"
+            return None
+
+        mock_which.side_effect = which_side
+
+        mock_run.return_value = MagicMock(
+            returncode=0,
+            stdout="06:00.0 VGA compatible controller: AMD/ATI Navi 31 [Radeon RX 7900 XTX]\n",
+        )
+        gpu = detect_gpu()
+        assert gpu.vendor == GPUVendor.AMD
+        assert "cpu" in gpu.index_url
+        assert any("ROCm is not installed" in d for d in gpu.details)
+
+    @patch("skill_seekers.cli.video_setup.shutil.which", return_value=None)
+    def test_cpu_fallback(self, mock_which):
+        """No GPU tools found → GPUVendor.NONE."""
+        gpu = detect_gpu()
+        assert gpu.vendor == GPUVendor.NONE
+        assert "cpu" in gpu.index_url
+
+    @patch("skill_seekers.cli.video_setup.shutil.which")
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_nvidia_smi_error(self, mock_run, mock_which):
+        """nvidia-smi returns non-zero → skip to next check."""
+        mock_which.side_effect = lambda cmd: (
+            "/usr/bin/nvidia-smi" if cmd == "nvidia-smi" else None
+        )
+        mock_run.return_value = MagicMock(returncode=1, stdout="")
+        gpu = detect_gpu()
+        assert gpu.vendor == GPUVendor.NONE
+
+    @patch("skill_seekers.cli.video_setup.shutil.which")
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_nvidia_smi_timeout(self, mock_run, mock_which):
+        """nvidia-smi times out → skip to next check."""
+        mock_which.side_effect = lambda cmd: (
+            "/usr/bin/nvidia-smi" if cmd == "nvidia-smi" else None
+        )
+        mock_run.side_effect = subprocess.TimeoutExpired(cmd="nvidia-smi", timeout=10)
+        gpu = detect_gpu()
+        assert gpu.vendor == GPUVendor.NONE
+
+    @patch("skill_seekers.cli.video_setup.shutil.which")
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_rocminfo_error(self, mock_run, mock_which):
+        """rocminfo returns non-zero → skip to next check."""
+
+        def which_side(cmd):
+            if cmd == "nvidia-smi":
+                return None
+            if cmd == "rocminfo":
+                return "/usr/bin/rocminfo"
+            return None
+
+        mock_which.side_effect = which_side
+        mock_run.return_value = MagicMock(returncode=1, stdout="")
+        gpu = detect_gpu()
+        assert gpu.vendor == GPUVendor.NONE
+
+
+# =============================================================================
+# Version Mapping Tests
+# =============================================================================
+
+
+class TestVersionMapping(unittest.TestCase):
+    """Tests for CUDA/ROCm version → index URL mapping."""
+
+    def test_cuda_124(self):
+        assert _cuda_version_to_index_url("12.4") == f"{_PYTORCH_BASE}/cu124"
+
+    def test_cuda_126(self):
+        assert _cuda_version_to_index_url("12.6") == f"{_PYTORCH_BASE}/cu124"
+
+    def test_cuda_121(self):
+        assert _cuda_version_to_index_url("12.1") == f"{_PYTORCH_BASE}/cu121"
+
+    def test_cuda_118(self):
+        assert _cuda_version_to_index_url("11.8") == f"{_PYTORCH_BASE}/cu118"
+
+    def test_cuda_old_falls_to_cpu(self):
+        assert _cuda_version_to_index_url("10.2") == f"{_PYTORCH_BASE}/cpu"
+
+    def test_cuda_invalid_string(self):
+        assert _cuda_version_to_index_url("garbage") == f"{_PYTORCH_BASE}/cpu"
+
+    def test_rocm_63(self):
+        assert _rocm_version_to_index_url("6.3.1") == f"{_PYTORCH_BASE}/rocm6.3"
+
+    def test_rocm_60(self):
+        assert _rocm_version_to_index_url("6.0") == f"{_PYTORCH_BASE}/rocm6.2.4"
+
+    def test_rocm_old_falls_to_cpu(self):
+        assert _rocm_version_to_index_url("5.4") == f"{_PYTORCH_BASE}/cpu"
+
+    def test_rocm_invalid(self):
+        assert _rocm_version_to_index_url("bad") == f"{_PYTORCH_BASE}/cpu"
+
+
+# =============================================================================
+# Venv Tests
+# =============================================================================
+
+
+class TestVenv(unittest.TestCase):
+    """Tests for venv detection and creation."""
+
+    def test_is_in_venv_returns_bool(self):
+        result = is_in_venv()
+        assert isinstance(result, bool)
+
+    def test_is_in_venv_detects_prefix_mismatch(self):
+        # If sys.prefix != sys.base_prefix, we're in a venv
+        with patch.object(sys, "prefix", "/some/venv"), \
+             patch.object(sys, "base_prefix", "/usr"):
+            assert is_in_venv() is True
+
+    def test_is_in_venv_detects_no_venv(self):
+        with patch.object(sys, "prefix", "/usr"), \
+             patch.object(sys, "base_prefix", "/usr"):
+            assert is_in_venv() is False
+
+    def test_create_venv_in_tempdir(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            venv_path = os.path.join(tmpdir, "test_venv")
+            result = create_venv(venv_path)
+            assert result is True
+            assert os.path.isdir(venv_path)
+
+    def test_create_venv_already_exists(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # Create it once
+            create_venv(tmpdir)
+            # Creating again should succeed (already exists)
+            assert create_venv(tmpdir) is True
+
+    def test_get_venv_python_linux(self):
+        with patch("skill_seekers.cli.video_setup.platform.system", return_value="Linux"):
+            path = get_venv_python("/path/.venv")
+            assert path.endswith("bin/python")
+
+    def test_get_venv_activate_cmd_linux(self):
+        with patch("skill_seekers.cli.video_setup.platform.system", return_value="Linux"):
+            cmd = get_venv_activate_cmd("/path/.venv")
+            assert "source" in cmd
+            assert "bin/activate" in cmd
+
+
+# =============================================================================
+# System Dep Check Tests
+# =============================================================================
+
+
+class TestSystemDeps(unittest.TestCase):
+    """Tests for system dependency checks."""
+
+    @patch("skill_seekers.cli.video_setup.shutil.which", return_value=None)
+    def test_tesseract_not_installed(self, mock_which):
+        result = check_tesseract()
+        assert result["installed"] is False
+        assert result["has_eng"] is False
+        assert isinstance(result["install_cmd"], str)
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    @patch("skill_seekers.cli.video_setup.shutil.which", return_value="/usr/bin/tesseract")
+    def test_tesseract_installed_with_eng(self, mock_which, mock_run):
+        mock_run.side_effect = [
+            # --version call
+            MagicMock(returncode=0, stdout="tesseract 5.3.0\n", stderr=""),
+            # --list-langs call
+            MagicMock(returncode=0, stdout="List of available languages:\neng\nosd\n", stderr=""),
+        ]
+        result = check_tesseract()
+        assert result["installed"] is True
+        assert result["has_eng"] is True
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    @patch("skill_seekers.cli.video_setup.shutil.which", return_value="/usr/bin/tesseract")
+    def test_tesseract_installed_no_eng(self, mock_which, mock_run):
+        mock_run.side_effect = [
+            MagicMock(returncode=0, stdout="tesseract 5.3.0\n", stderr=""),
+            MagicMock(returncode=0, stdout="List of available languages:\nosd\n", stderr=""),
+        ]
+        result = check_tesseract()
+        assert result["installed"] is True
+        assert result["has_eng"] is False
+
+    def test_detect_distro_returns_string(self):
+        result = _detect_distro()
+        assert isinstance(result, str)
+
+    @patch("builtins.open", side_effect=OSError)
+    def test_detect_distro_no_os_release(self, mock_open):
+        assert _detect_distro() == "unknown"
+
+
+# =============================================================================
+# ROCm Configuration Tests
+# =============================================================================
+
+
+class TestROCmConfig(unittest.TestCase):
+    """Tests for configure_rocm_env()."""
+
+    def test_sets_miopen_find_mode(self):
+        env_backup = os.environ.get("MIOPEN_FIND_MODE")
+        try:
+            os.environ.pop("MIOPEN_FIND_MODE", None)
+            changes = configure_rocm_env()
+            assert "MIOPEN_FIND_MODE=FAST" in changes
+            assert os.environ["MIOPEN_FIND_MODE"] == "FAST"
+        finally:
+            if env_backup is not None:
+                os.environ["MIOPEN_FIND_MODE"] = env_backup
+
+    def test_does_not_override_existing(self):
+        env_backup = os.environ.get("MIOPEN_FIND_MODE")
+        try:
+            os.environ["MIOPEN_FIND_MODE"] = "NORMAL"
+            changes = configure_rocm_env()
+            miopen_changes = [c for c in changes if "MIOPEN_FIND_MODE" in c]
+            assert len(miopen_changes) == 0
+            assert os.environ["MIOPEN_FIND_MODE"] == "NORMAL"
+        finally:
+            if env_backup is not None:
+                os.environ["MIOPEN_FIND_MODE"] = env_backup
+            else:
+                os.environ.pop("MIOPEN_FIND_MODE", None)
+
+    def test_sets_miopen_user_db_path(self):
+        env_backup = os.environ.get("MIOPEN_USER_DB_PATH")
+        try:
+            os.environ.pop("MIOPEN_USER_DB_PATH", None)
+            changes = configure_rocm_env()
+            db_changes = [c for c in changes if "MIOPEN_USER_DB_PATH" in c]
+            assert len(db_changes) == 1
+        finally:
+            if env_backup is not None:
+                os.environ["MIOPEN_USER_DB_PATH"] = env_backup
+
+
+# =============================================================================
+# Module Selection Tests
+# =============================================================================
+
+
+class TestModuleSelection(unittest.TestCase):
+    """Tests for SetupModules and _build_visual_deps."""
+
+    def test_default_modules_all_true(self):
+        m = SetupModules()
+        assert m.torch is True
+        assert m.easyocr is True
+        assert m.opencv is True
+        assert m.tesseract is True
+        assert m.scenedetect is True
+        assert m.whisper is True
+
+    def test_build_all_deps(self):
+        deps = _build_visual_deps(SetupModules())
+        assert "yt-dlp" in deps
+        assert "youtube-transcript-api" in deps
+        assert "easyocr" in deps
+        assert "opencv-python-headless" in deps
+        assert "pytesseract" in deps
+        assert "scenedetect[opencv]" in deps
+        assert "faster-whisper" in deps
+
+    def test_build_no_optional_deps(self):
+        """Even with all optional modules off, base video deps are included."""
+        m = SetupModules(
+            torch=False, easyocr=False, opencv=False,
+            tesseract=False, scenedetect=False, whisper=False,
+        )
+        deps = _build_visual_deps(m)
+        assert deps == list(_BASE_VIDEO_DEPS)
+
+    def test_build_partial_deps(self):
+        m = SetupModules(easyocr=True, opencv=True, tesseract=False, scenedetect=False, whisper=False)
+        deps = _build_visual_deps(m)
+        assert "yt-dlp" in deps
+        assert "youtube-transcript-api" in deps
+        assert "easyocr" in deps
+        assert "opencv-python-headless" in deps
+        assert "pytesseract" not in deps
+        assert "faster-whisper" not in deps
+
+
+# =============================================================================
+# Installation Tests
+# =============================================================================
+
+
+class TestInstallation(unittest.TestCase):
+    """Tests for install_torch() and install_visual_deps()."""
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_torch_success(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
+        gpu = GPUInfo(vendor=GPUVendor.NVIDIA, index_url=f"{_PYTORCH_BASE}/cu124")
+        assert install_torch(gpu) is True
+        call_args = mock_run.call_args[0][0]
+        assert "torch" in call_args
+        assert "--index-url" in call_args
+        assert f"{_PYTORCH_BASE}/cu124" in call_args
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_torch_cpu(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
+        gpu = GPUInfo(vendor=GPUVendor.NONE, index_url=f"{_PYTORCH_BASE}/cpu")
+        assert install_torch(gpu) is True
+        call_args = mock_run.call_args[0][0]
+        assert f"{_PYTORCH_BASE}/cpu" in call_args
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_torch_failure(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=1, stdout="", stderr="error msg")
+        gpu = GPUInfo(vendor=GPUVendor.NVIDIA, index_url=f"{_PYTORCH_BASE}/cu124")
+        assert install_torch(gpu) is False
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_torch_timeout(self, mock_run):
+        mock_run.side_effect = subprocess.TimeoutExpired(cmd="pip", timeout=600)
+        gpu = GPUInfo(vendor=GPUVendor.NVIDIA, index_url=f"{_PYTORCH_BASE}/cu124")
+        assert install_torch(gpu) is False
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_torch_custom_python(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
+        gpu = GPUInfo(vendor=GPUVendor.NONE, index_url=f"{_PYTORCH_BASE}/cpu")
+        install_torch(gpu, python_exe="/custom/python")
+        call_args = mock_run.call_args[0][0]
+        assert call_args[0] == "/custom/python"
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_visual_deps_success(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
+        assert install_visual_deps() is True
+        call_args = mock_run.call_args[0][0]
+        assert "easyocr" in call_args
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_visual_deps_failure(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=1, stdout="", stderr="error")
+        assert install_visual_deps() is False
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_visual_deps_partial_modules(self, mock_run):
+        mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
+        modules = SetupModules(easyocr=True, opencv=False, tesseract=False, scenedetect=False, whisper=False)
+        install_visual_deps(modules)
+        call_args = mock_run.call_args[0][0]
+        assert "easyocr" in call_args
+        assert "opencv-python-headless" not in call_args
+
+    @patch("skill_seekers.cli.video_setup.subprocess.run")
+    def test_install_visual_deps_base_only(self, mock_run):
+        """Even with all optional modules off, base video deps get installed."""
+        mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
+        modules = SetupModules(easyocr=False, opencv=False, tesseract=False, scenedetect=False, whisper=False)
+        result = install_visual_deps(modules)
+        assert result is True
+        call_args = mock_run.call_args[0][0]
+        assert "yt-dlp" in call_args
+        assert "youtube-transcript-api" in call_args
+        assert "easyocr" not in call_args
+
+
+# =============================================================================
+# Verification Tests
+# =============================================================================
+
+
+class TestVerification(unittest.TestCase):
+    """Tests for verify_installation()."""
+
+    @patch.dict("sys.modules", {"torch": None, "easyocr": None, "cv2": None})
+    def test_returns_dict(self):
+        results = verify_installation()
+        assert isinstance(results, dict)
+
+    def test_expected_keys(self):
+        results = verify_installation()
+        for key in ("yt-dlp", "youtube-transcript-api", "torch", "torch.cuda", "torch.rocm", "easyocr", "opencv"):
+            assert key in results, f"Missing key: {key}"
+
+
+# =============================================================================
+# Orchestrator Tests
+# =============================================================================
+
+
+class TestRunSetup(unittest.TestCase):
+    """Tests for run_setup() orchestrator."""
+
+    @patch("skill_seekers.cli.video_setup.verify_installation")
+    @patch("skill_seekers.cli.video_setup.install_visual_deps", return_value=True)
+    @patch("skill_seekers.cli.video_setup.install_torch", return_value=True)
+    @patch("skill_seekers.cli.video_setup.check_tesseract")
+    @patch("skill_seekers.cli.video_setup.detect_gpu")
+    def test_non_interactive_success(self, mock_detect, mock_tess, mock_torch, mock_deps, mock_verify):
+        mock_detect.return_value = GPUInfo(
+            vendor=GPUVendor.NONE, name="CPU-only", index_url=f"{_PYTORCH_BASE}/cpu",
+        )
+        mock_tess.return_value = {"installed": True, "has_eng": True, "install_cmd": "", "version": "5.3.0"}
+        mock_verify.return_value = {
+            "torch": True, "torch.cuda": False, "torch.rocm": False,
+            "easyocr": True, "opencv": True, "pytesseract": True,
+            "scenedetect": True, "faster-whisper": True,
+        }
+        rc = run_setup(interactive=False)
+        assert rc == 0
+        mock_torch.assert_called_once()
+        mock_deps.assert_called_once()
+
+    @patch("skill_seekers.cli.video_setup.install_torch", return_value=False)
+    @patch("skill_seekers.cli.video_setup.check_tesseract")
+    @patch("skill_seekers.cli.video_setup.detect_gpu")
+    def test_failure_returns_nonzero(self, mock_detect, mock_tess, mock_torch):
+        mock_detect.return_value = GPUInfo(
+            vendor=GPUVendor.NONE, name="CPU-only", index_url=f"{_PYTORCH_BASE}/cpu",
+        )
+        mock_tess.return_value = {"installed": True, "has_eng": True, "install_cmd": "", "version": "5.3.0"}
+        rc = run_setup(interactive=False)
+        assert rc == 1
+
+    @patch("skill_seekers.cli.video_setup.install_torch", return_value=True)
+    @patch("skill_seekers.cli.video_setup.install_visual_deps", return_value=False)
+    @patch("skill_seekers.cli.video_setup.check_tesseract")
+    @patch("skill_seekers.cli.video_setup.detect_gpu")
+    def test_visual_deps_failure(self, mock_detect, mock_tess, mock_deps, mock_torch):
+        mock_detect.return_value = GPUInfo(
+            vendor=GPUVendor.NONE, name="CPU-only", index_url=f"{_PYTORCH_BASE}/cpu",
+        )
+        mock_tess.return_value = {"installed": True, "has_eng": True, "install_cmd": "", "version": "5.3.0"}
+        rc = run_setup(interactive=False)
+        assert rc == 1
+
+    @patch("skill_seekers.cli.video_setup.verify_installation")
+    @patch("skill_seekers.cli.video_setup.install_visual_deps", return_value=True)
+    @patch("skill_seekers.cli.video_setup.install_torch", return_value=True)
+    @patch("skill_seekers.cli.video_setup.check_tesseract")
+    @patch("skill_seekers.cli.video_setup.detect_gpu")
+    def test_rocm_configures_env(self, mock_detect, mock_tess, mock_torch, mock_deps, mock_verify):
+        """AMD GPU → configure_rocm_env called and env vars set."""
+        mock_detect.return_value = GPUInfo(
+            vendor=GPUVendor.AMD, name="RX 7900", index_url=f"{_PYTORCH_BASE}/rocm6.3",
+        )
+        mock_tess.return_value = {"installed": True, "has_eng": True, "install_cmd": "", "version": "5.3.0"}
+        mock_verify.return_value = {
+            "torch": True, "torch.cuda": False, "torch.rocm": True,
+            "easyocr": True, "opencv": True, "pytesseract": True,
+            "scenedetect": True, "faster-whisper": True,
+        }
+        rc = run_setup(interactive=False)
+        assert rc == 0
+        assert os.environ.get("MIOPEN_FIND_MODE") is not None
+
+
+# =============================================================================
+# Tesseract Circuit Breaker Tests (video_visual.py)
+# =============================================================================
+
+
+class TestTesseractCircuitBreaker(unittest.TestCase):
+    """Tests for _tesseract_broken flag in video_visual.py."""
+
+    def test_circuit_breaker_flag_exists(self):
+        import skill_seekers.cli.video_visual as vv
+        assert hasattr(vv, "_tesseract_broken")
+
+    def test_circuit_breaker_skips_after_failure(self):
+        import skill_seekers.cli.video_visual as vv
+        from skill_seekers.cli.video_models import FrameType
+
+        # Save and set broken state
+        original = vv._tesseract_broken
+        try:
+            vv._tesseract_broken = True
+            result = vv._run_tesseract_ocr("/nonexistent/path.png", FrameType.CODE_EDITOR)
+            assert result == []
+        finally:
+            vv._tesseract_broken = original
+
+    def test_circuit_breaker_allows_when_not_broken(self):
+        import skill_seekers.cli.video_visual as vv
+        from skill_seekers.cli.video_models import FrameType
+
+        original = vv._tesseract_broken
+        try:
+            vv._tesseract_broken = False
+            if not vv.HAS_PYTESSERACT:
+                # pytesseract not installed → returns [] immediately
+                result = vv._run_tesseract_ocr("/nonexistent/path.png", FrameType.CODE_EDITOR)
+                assert result == []
+            # If pytesseract IS installed, it would try to run and potentially fail
+            # on our fake path — that's fine, the circuit breaker would trigger
+        finally:
+            vv._tesseract_broken = original
+
+
+# =============================================================================
+# MIOPEN Env Var Tests (video_visual.py)
+# =============================================================================
+
+
+class TestMIOPENEnvVars(unittest.TestCase):
+    """Tests that video_visual.py sets MIOPEN env vars at import time."""
+
+    def test_miopen_find_mode_set(self):
+        # video_visual.py sets this at module level before torch import
+        assert "MIOPEN_FIND_MODE" in os.environ
+
+    def test_miopen_user_db_path_set(self):
+        assert "MIOPEN_USER_DB_PATH" in os.environ
+
+
+# =============================================================================
+# Argument & Early-Exit Tests
+# =============================================================================
+
+
+class TestVideoArgumentSetup(unittest.TestCase):
+    """Tests for --setup flag in VIDEO_ARGUMENTS."""
+
+    def test_setup_in_video_arguments(self):
+        from skill_seekers.cli.arguments.video import VIDEO_ARGUMENTS
+
+        assert "setup" in VIDEO_ARGUMENTS
+        assert VIDEO_ARGUMENTS["setup"]["kwargs"]["action"] == "store_true"
+
+    def test_parser_accepts_setup(self):
+        import argparse
+
+        from skill_seekers.cli.arguments.video import add_video_arguments
+
+        parser = argparse.ArgumentParser()
+        add_video_arguments(parser)
+        args = parser.parse_args(["--setup"])
+        assert args.setup is True
+
+    def test_parser_default_false(self):
+        import argparse
+
+        from skill_seekers.cli.arguments.video import add_video_arguments
+
+        parser = argparse.ArgumentParser()
+        add_video_arguments(parser)
+        args = parser.parse_args(["--url", "https://example.com"])
+        assert args.setup is False
+
+
+class TestVideoScraperSetupEarlyExit(unittest.TestCase):
+    """Test that --setup exits before source validation."""
+
+    @patch("skill_seekers.cli.video_setup.run_setup", return_value=0)
+    def test_setup_skips_source_validation(self, mock_setup):
+        """--setup without --url should NOT error about missing source."""
+        from skill_seekers.cli.video_scraper import main
+
+        old_argv = sys.argv
+        try:
+            sys.argv = ["video_scraper", "--setup"]
+            rc = main()
+            assert rc == 0
+            mock_setup.assert_called_once_with(interactive=True)
+        finally:
+            sys.argv = old_argv
+
+
+if __name__ == "__main__":
+    unittest.main()

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