| name | vision-skill |
| description | Understand images (describe content, answer questions about them, extract text/OCR) via a vision model. Use when the user asks what an image, screenshot, chart, scan, or photo contains. |
| allowed-tools | vision data |
| metadata | {"author":"opencompany","version":"1.0","category":"vision","icon":"👁️","color":"#bd93f9"} |
Vision Skill
See images through the vision tool (a vision-capable model looks at the
image and answers in text). Check the tools in your tool list for full
schemas; this skill covers strategy.
Workflow
- Locate the image with the
data tool first (list / search) — the
vision tool takes a workspace-relative path, and guessing paths wastes
a paid model call.
- An image on an external mount (
mnt/...) must be imported first:
data copy_to_workspace, then pass the returned workspace path.
- Choose the operation:
describe — what is in the image; pass question for something
specific ("what is the y-axis peak?") instead of asking for a general
description and hoping.extract_text — OCR-style text extraction (documents, screenshots,
signs). Returns only the text.
The budget ladder
budget controls resolution and therefore cost: small (~256 visual
tokens), normal (~1024, default), large (~2048, fine detail).
- Skim at
normal; escalate to large only when the answer needs fine
detail (dense charts, small print, UI screenshots).
- Never open with
large on an image you haven't seen at normal — each
call bills the vision provider.
When NOT to use this tool
- The host model may already see images natively: if an image you read via
the
data tool is visible in the conversation, answer from it directly —
a delegate call would be a second opinion at extra cost and latency.
- Extracting text from a PDF: use
data read (text extraction) first;
vision OCR is for rasterized/scanned content the parser cannot read.
Anti-patterns
- Calling
vision on a path that was never confirmed via data list/read.
- Repeating
describe with rephrased questions — ask one specific
question with the detail you actually need.
- Using
extract_text on charts to infer numbers — ask describe with a
targeted question instead; OCR on plot labels loses structure.
