Vision Skill

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

1. 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.
2. An image on an external mount (mnt/...) must be imported first: data copy_to_workspace, then pass the returned workspace path.
3. 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.