| name | textfocus |
| description | Align and center a specific word across multiple images using OCR (Tesseract) to produce documentary-style "word focus" cuts. Use when the user wants to center/lock a keyword across screenshots or video frames, create news-ticker style alignment, produce same-word cuts for After Effects / Premiere, or batch-crop frames so a chosen word stays perfectly centered and same-size. Invoked via the installed `quick-cuts` CLI. |
TextFocus (Quick Cuts) Skill
A Python-based OCR alignment tool that finds a target word in each input image and produces an output image where that word is centered and scaled to a consistent size. Designed for creating documentary / news-style video cuts where the same word stays locked in place across different footage.
When to use this skill
Activate when the user asks to:
- "Center the word X across these images/frames"
- "Align all these screenshots on the word Y"
- "Make a Warp-style / documentary cut where a word stays fixed"
- Batch-process video frames so a keyword is locked to the center
- Normalize word size and position across many images
If the user only wants generic image cropping (no OCR / no target word), this skill is not the right fit.
Prerequisites
Before running anything, verify the environment:
- Tesseract OCR must be installed on the system (Python-only install is not enough):
- Python 3.8+
- The
quick-cuts CLI itself — install from this repo with uv:
uv tool install .
quick-cuts --help
If quick-cuts is not on PATH after install, run uv tool update-shell once and reopen the shell.
If Tesseract is missing, tell the user to install it — do not try to work around it.
Primary tool: quick-cuts
quick-cuts <images> -w "<target_word>" [options]
<images> can be a glob (images/*.png), a directory (images/), or individual files.
Common options
| Flag | Purpose | Default |
|---|
-w, --word | Target word to center (required) | — |
-o, --output | Output directory | ./aligned_<word> |
-s, --size | Output canvas WxH | 1920x1080 |
--word-height | Target word height in px | 100 |
--background | white | black | transparent | dominant | dominant |
--partial | Match words starting with the target (e.g. warp → warpdotdev) | off |
--workers | Parallel worker count | CPU count |
Typical invocations
quick-cuts images/*.png -w "breaking"
quick-cuts images/ -w "warp" --partial
quick-cuts images/ -w "alert" -s 3840x2160 --word-height 200
quick-cuts frames/ -w "viral" -s 1080x1920 --background transparent
Transparent background forces .png output regardless of input format.
Working guidance for Claude
When the user asks for a word-alignment task:
- Confirm the target word and the input path before running — mistakes here waste a batch.
- Check image formats — supported:
.jpg, .jpeg, .png, .bmp, .tiff.
- Run once on a small sample first (1–3 images) to verify OCR actually finds the word, before processing a full folder.
- If OCR misses, suggest in order:
--partial, higher-resolution source images, better contrast. Do not silently change the target word.
- Output files are named
aligned_<original> in the output dir. Tell the user where they landed (output_dir.absolute() is logged).
- Progress / failures are printed to stdout. Surface the "Failed to find word in:" list back to the user so they know which frames need manual attention.
- If
quick-cuts is not found, the user hasn't installed it yet — point them at uv tool install . from this repo, not python quick_cuts.py.
Files in this skill
pyproject.toml — defines the quick-cuts entry pointquick_cuts.py — source of the CLI (invoked via the installed quick-cuts command, not directly)requirements.txt — dev-install dependency list (the CLI install uses pyproject.toml instead)README.md — full user-facing docsexamples.md — worked examplesdocs/example.gif — visual reference of the effect
