/ai-provider-claude-vision

name: ai-provider-claude-vision
description: Image understanding and document analysis with Claude's multimodal capabilities -- image input formats, PDF processing, multi-image patterns, structured extraction, and token cost estimation

Claude Vision Patterns

> **Quick Guide:** Use `type: "image"` content blocks for images (base64, URL, or file_id) and `type: "document"` content blocks for PDFs. Supported image formats: JPEG, PNG, GIF, WebP. Images before text in the content array improves results. Token cost formula: `tokens = (width * height) / 750`. Images are auto-resized if the long edge exceeds 1568px or exceeds ~1600 tokens. PDFs use `type: "document"` with `media_type: "application/pdf"`. No OCR library needed -- Claude reads text directly from images and PDFs.

---

<critical_requirements>

CRITICAL: Before Using This Skill

**(You MUST use `type: "image"` for images and `type: "document"` for PDFs -- they are different content block types)**

**(You MUST place images and documents BEFORE text in the content array -- Claude performs better with visual content first)**

**(You MUST always provide `max_tokens` in every request -- it is required and has no default)**

**(You MUST iterate over `response.content` blocks -- never assume a single text block in the response)**

**(You MUST use named constants for max_tokens, token budgets, and pixel limits -- no magic numbers)**

</critical_requirements>

---

**Auto-detection:** Claude vision, image analysis, image input, base64 image, URL image, type image, type document, media_type image/jpeg, media_type image/png, image/webp, image/gif, application/pdf, PDF processing, document extraction, multimodal, multi-image, image comparison, chart analysis, screenshot analysis, image understanding, visual content, vision API

**When to use:**

• Sending images to Claude for analysis, description, or data extraction
• Processing PDF documents for text extraction, chart analysis, or summarization
• Comparing multiple images in a single request
• Extracting structured data from screenshots, receipts, charts, or forms
• Building document processing pipelines with Claude
• Estimating token costs for image-heavy workloads

**Key patterns covered:**

• Image input via base64, URL, and Files API
• PDF document input and processing
• Multi-image requests and comparison patterns
• Image + text prompting best practices
• Token cost estimation and image sizing
• Structured data extraction from visual content
• Multi-turn vision conversations
• Prompt caching with images and PDFs

**When NOT to use:**

• General Claude API usage without images or documents -- use the general Anthropic SDK patterns instead
• Image generation or editing -- Claude is understanding-only, it cannot create or modify images
• Identifying specific people in images -- Claude refuses to name people (Anthropic policy)
• Medical diagnostic imaging (CTs, MRIs) -- not designed for clinical diagnosis

---

Examples Index

• [Core: Image & PDF Input](examples/core.md) -- Base64, URL, file_id, PDF input, multi-image, token estimation
• [Extraction & Prompting](examples/extraction.md) -- Structured extraction, comparison, prompting best practices, caching
• [Quick API Reference](reference.md) -- Content block types, supported formats, size limits, token formula

---

<philosophy>

Philosophy

Claude's vision capabilities treat images and documents as **first-class content blocks** alongside text. There is no separate "vision API" -- you add image or document blocks to the same Messages API you already use for text.

**Core principles:**

1. **Images are content blocks, not attachments** -- Images and PDFs are content blocks in the `messages` array, interleaved with text. They are not uploaded separately or referenced by URL-only. 2. **Image-first ordering** -- Place images before text in the content array. This mirrors how `documents first, query last` improves text prompts. Claude processes visual content better when it sees the image before the question. 3. **No OCR needed** -- Claude reads text directly from images and PDFs. You do not need to pre-extract text with an OCR library. For PDFs, Claude processes both the extracted text and a rendered image of each page. 4. **Token costs scale with pixels** -- Image tokens are proportional to resolution: `tokens = (width * height) / 750`. Downsizing images before sending saves tokens without losing meaningful detail for most use cases. 5. **PDFs are dual-processed** -- Each PDF page is converted to an image AND has its text extracted. Claude sees both, giving it access to visual layout and textual content.

**When to use vision:**

• Analyzing screenshots, photos, charts, diagrams, or infographics
• Extracting data from forms, receipts, invoices, or tables
• Processing PDF documents for summarization, extraction, or analysis
• Comparing multiple images (before/after, A/B testing, design review)
• Understanding visual context that text alone cannot capture

**When NOT to use:**

• Pure text tasks with no visual component -- vision adds unnecessary token cost
• Tasks requiring pixel-perfect spatial precision -- Claude's spatial reasoning is approximate
• Identifying specific people -- Claude refuses to name individuals (Anthropic policy)
• Replacing professional medical imaging analysis (CTs, MRIs, X-rays)

</philosophy>

---

<patterns>

Core Patterns

Pattern 1: Base64 Image Input

Read a local file, encode to base64, send as `type: "image"` content block. Image block before text block.

// Image block first, text prompt second, iterate response content blocks
content: [
  {
    type: "image",
    source: { type: "base64", media_type: "image/png", data: imageData },
  },
  { type: "text", text: "Describe what you see in this image." },
];

**Why good:** Image before text improves results, explicit media_type, structured content blocks

// BAD: base64 as text string -- Claude cannot in