Vision Tools Agent Skill

vision-tools

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) — no extra credentials.

Pick the tool by the question you are answering:

| Question | Tool |

|---|---|

| "What does this image show / say?" | glance |

| "Where is X?" — a thing you can name | ground |

| "Where are all the Xs?" — every instance of a kind | detect |

| "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 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 things | code 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:

• cut a box out of an image → crop, not Image.open(...).crop(...)
• sample a region's palette → scripts/dominant_colors.py
• compare two images → scripts/pixel_diff.py
• vectorize to SVG → trace
• locate / inventory elements → ground / detect
• describe / OCR an image → glance
• safely split, OCR, and merge a long screenshot → scripts/long_screenshot_ocr.py
• HTML file to a screenshot → scripts/html_shot.py

Hand-written Pillow is only for what none of them return: a relation

between two things you already located (a gap, a distance), a resize or

overlay, drawing. If you catch yourself writing .crop(), .convert(),

or histogram code where one of the tools above fits, replace it with the

tool call — same coordinates, same box format, and the output feeds the

next tool directly.

glance — ask about an image

glance <image>                                 # detailed description
glance <image> -q "<question>"                 # targeted question (qualitative only)
glance <image> --ocr                           # verbatim OCR
glance <image> --region X1,Y1,X2,Y2 -q "..."   # zoom into a crop
glance <img1> <img2> -q "..."                  # compare in ONE call

When you do compare with glance, pass all paths to one call — separate

calls cannot see both images, so two descriptions compared afterwards are

two hallucination surfaces, not a comparison. --region uploads only the

crop, so small text and icons become readable.

But "what changed between these two?" is not a glance question. A one-word

badge or a small shift is a rounding error to a vision model and exact to

scripts/pixel_diff.py. Diff first to get the box, then glance --region

that box to read what the change actually is.

For a tall scrolling screenshot, do not send the whole image through one OCR

call and accept the model's downscaling loss. Run the long-screenshot workflow,

which finds low-content cut bands, invokes glance on each chunk, uses

structured extraction for chat histories, merges only duplicated overlap, and

writes a boundary audit:

python3 scripts/long_screenshot_ocr.py work/page.png -o work/page.ocr.md
python3 scripts/long_screenshot_ocr.py work/chat.png --mode chat --resume -o work/chat.ocr.md

Read references/long-screenshot-ocr.md before using it. It defines the

verification pass for unsafe cuts and chat-message boundaries.

ground — locate a named target

ground <image> "<target description>"
ground <image> "<target>" --region X1,Y1,X2,Y2

Output: x1: .., y1: .., x2: .., y2: .. in original-image pixels — with

--region too (crop hits are mapped back).

If several boxes come back numbered, your description matched more than

one element rather than picking out a single thing. Narrow it with what

distinguishes the one you mean — its text, its position, the block it sits

in — and ask again.

The box is a handle, not just an answer — it feeds the next call:

$ ground screenshot.png "the send button"
x1: 1067, y1: 841, x2: 1108, y2: 881
$ glance screenshot.png --region 1067,841,1108,881 -q "is it enabled or greyed out?"

That two-step is how you inspect anything too small to survive a

full-image pass.

detect — find every instance of a kind

detect <image>                        # every UI element
detect <image> "buttons"              # one kind only
detect <image> --region X1,Y1,X2,Y2   # inside one box

You name a particular thing for ground; you name a kind for detect and

it enumerates the instances. Output is a numbered list with each item's

visible text and box. A full-screen

pass is a fast first draft — counts vary run to run on dense screens. For

completeness, detect the layout blocks first, then detect --region each

block.

trace — exact shape geometry (local, no vision API)

trace <image>                                  # b/w spline SVG to stdout
trace <image> --polygon                        # boxy diagrams/wireframes
trace <image> --region X1,Y1,X2,Y2 -o out.svg  # crop first

Coordinates come from the actual pixels, not a model's estimate. Flat,

high-contrast graphics only; text becomes curves (pair with --ocr when

the text matters). Small images are upscaled automatically before tracing,

so a 30px icon traces as readily as a screenshot — size is not a reason to

skip the tool. Before shipping or reusing a traced SVG, read

references/restore-graphic.md — it holds the reuse traps and the

ship-vs-hand-write call.

crop — cut a pixel box out of an image (local, no vision API)

crop <image> --region X1,Y1,X2,Y2             # writes <image-stem>.crop.png next to the input
crop <image> --region X1,Y1,X2,Y2 -o out.png
crop <image> --region X1,Y1,X2,Y2 --scale 4   # upscale the cut-out 4x (LANCZOS) first

The same X1,Y1,X2,Y2 pixel boxes ground/detect print, clamped to the

image bounds. Once a box is worth keeping — the same crop is about to feed

pixel_diff, dominant_colors, and trace in turn — cut it to a file

once and reuse it, instead of re-cropping in memory on every call.

--scale N upscales the cut-out before writing (default output name becomes

<image-stem>[email protected]): for icons too small for ground/trace to see

clearly, crop with --scale 4, then run ground/trace on the upscaled

file — coordinates it returns are in the upscaled grid, divide by N to map

back to the original image. Requires the optional pillow.

extract_fg — icon foreground as transparent PNG: manual region or auto (local, no vision API)

# manual: you know the region (and optionally the background colour)
python3 scripts/extract_fg.py shot.png --region X1,Y1,X2,Y2 -o icon.png
python3 scripts/extract_fg.py shot.png --region X1,Y1,X2,Y2 --mode dark          # grey/black line logos
python3 scripts/extract_fg.py shot.png --region X1,Y1,X2,Y2 --exclude-color '#E6E6E6'
# auto: `crop --scale` cut-outs with the icon centred — no region needed
crop shot.png --region X1,Y1,X2,Y2 --scale 4 -o d/icon1.png
python3 scripts/extract_fg.py d/icon1.png d/icon2.png       # writes <stem>.clean.png next to each input
python3 scripts/extract_fg.py d/icon1.png --disc-radius 60
python3 scripts/extract_fg.py d/icon1.png --boxes "101,84,184,171"

Manual mode keeps every sufficiently large connected component of the

region (separate logo sub-shapes stay together; specks drop out). Auto mode

takes a crop --scale cut-out with the icon centred (disc + glyph): the

disc centre is the image centre, the disc radius defaults to

min(w,h)/2 * 0.6, and the disc colour is sampled from a ring around the

centre; that colour is excluded and the glyph is picked as the most

saturated among the three largest coloured components (white rings,

ripples, and text fall away), output as a 1:1 transparent PNG. When auto

inference fails, override the radius with --disc-radius, or pass a

ground box (in the upscaled grid) as --boxes to recentre and re-filter

by overlap. Multiple images may be passed at once (auto mode).

Requires the optional pillow (and numpy for auto mode).

html_shot — render an HTML file to an image (local, needs a Chrome-family browser)

python3 scripts/html_shot.py page.html                      # writes page.png, 1280x800
python3 scripts/html_shot.py page.html --width 1440 --height 900 -o page.png
python3 scripts/html_shot.py page.html --scale 2            # 2x pixels: small text stays readable

The visual-alignment loop: write HTML, screenshot it at the reference

viewport, then compare it with the design. Use pixel_diff to locate

material differences, not to chase a zero-difference score. Rendering

happens in headless Chrome/Chromium/Edge — no Python dependencies. Only the

viewport is captured, so pass --height for pages taller than the window;

--wait-ms N pauses for fonts, images, or animation before capturing.

Paths are relative to this skill's own directory.

pixel_diff — where two images differ (local, no vision API)

python3 scripts/pixel_diff.py <a> <b>      # path is relative to this skill dir

Prints an overall difference percentage plus the worst regions as x1: ..

boxes you can feed straight into glance --region. Exact where a vision

model rounds off.

dominant_colors — a region's palette, and the exact value among candidates (local, no vision API)

python3 scripts/dominant_colors.py <image> --region X1,Y1,X2,Y2          # top colour clusters + shares
python3 scripts/dominant_colors.py <image> --region X1,Y1,X2,Y2 \
  --candidates '#F9FAFA,#F5F5F5,#F3F3F3,#EDEDED'                        # pick the best candidate

A vision model names a colour ("light gray") but not its value. The first

mode downsamples, quantizes, and merges near-duplicates to list the region's

significant colours with the share each owns — the histogram shows which

colour is the background and which is the accent. Given the candidate palette

your label implies, the second mode scores each candidate by how close the

region's pixels are to it and prints the winner. Take the value from here,

never from glance's prose. Paths are relative to this skill's own

directory.

Work from a copy, not a temp path

If the image lives in a temp directory, before your first tool call on one, copy it somewhere durable and run everything against the copy — that is what keeps the image reachable later:

cp "<the temp path>" work/shot.png
glance work/shot.png -q "..."

Exception: the user asked for the image to stay in a temp folder.

When you have a description instead of the image

If an image reached you only as text — a description written by a person,

a tool, or another model — and the image's file path is visible in the

conversation, do not reason past a missing detail. Look again yourself:

• glance <path> -q "<the specific detail>" — one qualitative follow-up.
• ground <path> "<target>" then glance <path> --region <that box> -q "..." —

locate, then zoom. The reliable way to inspect one element closely.

If the file no longer exists, say so instead of guessing.

Coarse to fine — the method behind every task above

For a single question about an image, glance is the whole answer. For

anything multi-step, work outside-in:

• One full-image pass (glance, or a description you already have) for

the layout and an inventory of what is where.

• For any element that matters, ground it, then zoom with

glance --region <box> -q "...". Full-image passes routinely miss small

text and icons; a crop puts all the pixels on one detail, so the model

sees it at effectively higher resolution. When the same box will be

checked more than once, cut it to a file first with crop.

• Never take a *prose* answer for a pixel-level fact — exact colors, small

offsets, sizes. Vision models confidently report styling that is not

there: coloured syntax highlighting in a monochrome code block, a border

that does not exist. Get the number from trace, from a ground box, or

from pixel_diff; sample the pixels yourself only for what those cannot

return.

Use cases

Each file below is one job, start to finish: when it applies, the call

sequence, and how to tell you got it right.

| The job | Read |

|---|---|

| OCR a long screenshot, scrolling page, or chat history without losing text at chunk boundaries | references/long-screenshot-ocr.md |

| Rebuild a page or component as HTML/CSS, including a roughly three-minute fast approximation mode, or align an existing UI with its reference image | references/restore-ui.md |

| Extract or rebuild an icon, logo, illustration, or other isolated graphic as transparent PNG/SVG | references/restore-graphic.md |

| Turn a sketch, diagram, or whiteboard into Mermaid, Graphviz, or another structured representation | references/restore-structure.md |

| Operate a GUI from screenshots — locate, act, verify each step | references/gui.md |

Notes

• Only PNG / JPEG / GIF / WebP images are supported.
• If a command is not found, the optional tools were not installed — report

this to the user instead of improvising a replacement.

• If the vision API fails, relay the error faithfully; never fabricate

image content.

Source repository: https://github.com/Anionex/agent-vision-toolkit

Installation guide: https://github.com/Anionex/agent-vision-toolkit/blob/main/AGENT_INSTALL.md