clipcase 1.0.0

Convert screen recording videos into structured QA test cases using AI vision

Clipcase

Convert screen recording videos of application usage into structured, comprehensive test cases — automatically.

Drop a .mov/.mp4 video, run one command, and get test cases in Markdown, CSV, and Excel formats.

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How It Works

Video (.mov/.mp4) ──▶ ffmpeg (frame extraction) ──▶ AI Vision (frame analysis) ──▶ Test Cases (MD/CSV/XLSX)

1. Frame extraction — Uses ffmpeg to extract screenshots from the video at a configurable frame rate.
2. AI analysis — Sends sampled frames to a vision-capable LLM (Claude or GPT-4o) to understand the complete user flow.
3. Test case generation — The AI generates structured test cases following QA best practices, covering positive, negative, and edge case scenarios.
4. Export — Outputs test cases in Markdown, CSV, and styled Excel formats.

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Prerequisites

Dependency	Why	Install
Python 3.9+	Runtime	python.org
ffmpeg	Frame extraction from video	brew install ffmpeg (macOS) / sudo apt install ffmpeg (Linux) / choco install ffmpeg (Windows)
API Key	AI analysis (Claude or GPT-4o)	See API Setup below

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Quick Start

1. Clone and install

git clone <repo-url> clipcase
cd clipcase
pip install -r requirements.txt

2. Set your API key

cp .env.example .env
# Edit .env and paste your API key

Or export directly:

export ANTHROPIC_API_KEY=sk-ant-api03-...
# or
export OPENAI_API_KEY=sk-...

3. Run

python clipcase.py your_recording.mov

That's it. Your test cases will appear as:

• your_recording_test_cases.md
• your_recording_test_cases.csv
• your_recording_test_cases.xlsx

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Usage

python clipcase.py <video_file> [options]

Options

Flag	Default	Description
--fps	2	Frames per second to extract
--provider	anthropic	LLM provider: anthropic or openai
--output	auto	Base name for output files
--frames-dir	frames	Directory for extracted frames
--frames-only	off	Only extract frames, skip AI
--sample-interval	10	Use every Nth frame for analysis
--principles	auto	Path to custom principles file

Examples

# Basic — uses Claude, 2 fps
python clipcase.py recording.mov

# Use OpenAI GPT-4o instead
python clipcase.py recording.mov --provider openai

# Use Google Gemini (free tier)
python clipcase.py recording.mov --provider gemini

# Higher frame rate for fast-paced UI
python clipcase.py recording.mov --fps 4

# Just extract frames (no AI cost), review manually
python clipcase.py recording.mov --frames-only

# Custom output name
python clipcase.py recording.mov --output login_flow_tests

# Analyze more frames (lower interval = more frames sent to AI)
python clipcase.py recording.mov --sample-interval 5

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API Setup

Option A: Anthropic (Claude) — Recommended

1. Go to console.anthropic.com
2. Create an API key
3. Add to .env:

   ANTHROPIC_API_KEY=sk-ant-api03-...
   

Option B: OpenAI (GPT-4o)

1. Go to platform.openai.com
2. Create an API key
3. Add to .env:

   OPENAI_API_KEY=sk-...
   

4. Use --provider openai when running

Option C: Google Gemini — Free tier available

1. Go to aistudio.google.com/app/apikey
2. Sign in with your Google account
3. Click Create API Key (free, no credit card needed)
4. Add to .env:

   GEMINI_API_KEY=AIza...
   

5. Use --provider gemini when running

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Custom Test Case Principles

You can customize how test cases are written by providing your own principles file:

python clipcase.py recording.mov --principles my_standards.md

If you place a file named test_case_creation_principles.md in the same directory as the video, it will be picked up automatically.

The included test_case_creation_principles.md covers:

• Clarity, independence, repeatability, traceability, maintainability
• Naming conventions (Verify that..., Verify if the user...)
• Pre-conditions, expected outcomes, test data guidelines
• Test type classification (Smoke, Sanity, Regression, E2E)

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Output Formats

Markdown (.md)

Full document with flow analysis, test case table, and summary statistics.

CSV (.csv)

Tab-importable into Google Sheets, Jira, TestRail, or any test management tool. Checkmarks rendered as ✓.

Excel (.xlsx)

Professionally styled with:

• Color-coded header row
• Alternating row shading
• Auto-filters on every column
• Frozen header row
• Auto-sized columns

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Project Structure

clipcase/
├── clipcase.py                         # Main CLI tool
├── requirements.txt                    # Python dependencies
├── .env.example                        # API key template
├── .gitignore                          # Git ignore rules
├── test_case_creation_principles.md    # QA principles (auto-loaded)
├── test_case_template.md               # Reference template
└── README.md                           # This file

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Tips

• Short videos work best: 1–5 minute recordings of a single flow produce the most focused test cases.
• Record one flow per video: Login flow, checkout flow, onboarding flow — keep them separate.
• Review and refine: AI-generated test cases are a strong starting point. Have your QA team review and adjust.
• Lower --sample-interval for complex UIs where many small changes happen quickly.
• Use --frames-only to preview what the AI will see before spending API credits.

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Cost Estimate

Each run makes 2–4 API calls depending on video length:

Provider	Approximate Cost per Run
Claude (Anthropic)	~$0.30 – $1.00
GPT-4o (OpenAI)	~$0.50 – $1.50
Gemini 1.5 Pro (Google)	Free tier available (generous quota)

Costs scale with frame count and video length.

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Troubleshooting

Issue	Fix
ffmpeg: command not found	Install ffmpeg (see Prerequisites)
ANTHROPIC_API_KEY not set	Add key to .env or export in shell
Unicode filename error	Tool handles this automatically via symlink
openpyxl not installed	Run pip install openpyxl for Excel support
Too few test cases generated	Lower --sample-interval to send more frames

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License

MIT