Skills

Vision Skills

Local vision CLIs: glance (describe/ask/OCR an image), ground (locate a target, pixel...

Install

npx skills add Anionex/agent-vision-toolkit --skill vision-skills -g -a claude-code -y
Category
Media
Updated
22d ago
License
MIT
Context cost
Mediumabout 3,000 tokens
Runs scripts
Yes, review before use

What it does

Local vision CLIs: glance (describe/ask/OCR an image), ground (locate a target, pixel box), detect (element inventory), trace (image to SVG geometry), crop (cut a pixel box to a file), and scripts/html_shot.py (HTML file to image). Use for any task involving an image, questions, text, splitting and transcribing long screenshots or chat histories, locating elements, comparing, rebuilding as HTML/SVG, digitizing a sketch or diagram, reading values off a chart, operating a GUI from screenshots, and to re-check an image yourself when a description you were given lacks a detail.

vision-skills

Five local CLIs that give a text-only agent eyes. They read one shared vision config (VISION_API_KEY / VISION_BASE_URL / VISION_MODEL / LANG), plus the optional Python-client settings VISION_API_PROTOCOL, VISION_REASONING_EFFORT, and VISION_USER_AGENT, no extra credentials.

Pick the tool by the question you are answering:

QuestionTool
"What does this image show / say?"glance
"Where is X?", a thing you can nameground
"Where are all the Xs?", every instance of a kinddetect
"What is its exact shape, size, offset?"trace
"Cut this box out as its own image file"crop
"OCR this long screenshot / scrolling page / chat history"scripts/long_screenshot_ocr.py
"Extract the icon/logo foreground as transparent PNG, manual region or auto (cropped+scaled screenshots)"scripts/extract_fg.py
"Turn this HTML file into a viewport or full-page screenshot"scripts/html_shot.py
"Which colours dominate a region, and which palette value fits it?"scripts/dominant_colors.py
A relation none of them return, a gap, a distance between two located thingscode over the pixels (Pillow)

glance answers what something is; ground and detect answer where. You give ground a description of a particular thing; you give detect a kind and it enumerates the instances.

Both give real coordinates, but they are not pixel-exact: the box arrives on a 0-1000 grid and is scaled to your image, so the last pixel or few are not reliable. That is accurate enough to crop with, to click, to compare positions against. When a number has to be exact, trace derives it from the actual pixels, offsets, sizes, shapes.

Use the provided tools before hand-rolled pixels

Everything this toolkit ships a tool for, call the tool, do not rewrite it with Pillow in the middle of a task. The CLIs exist so the same pixel work is not hand-coded differently every time:

Read the whole SKILL.md on GitHub

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