multimodal-expert

multimodal-expert is an agent for Claude Code from Matt-Dionis/claude-code-configs. It costs 46 tokens per session (2,213 once invoked), scanned A, original, MIT.

An expert agent for AI applications that work with more than text, including images, PDF documents, audio, and uploaded files. It covers processing, validation, conversion, and vision-model connections.

In plain words
What is it for?
Use it to build image analysis, PDF question-answering, audio transcription connections, OCR, document extraction, and multimodal uploads.
Why use it?
It helps handle different file types safely and interpret their contents, layouts, images, charts, or forms.

Agent for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it to build image analysis, PDF question-answering, audio transcription connections, OCR, document extraction, and multimodal uploads.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/matt-dionis/claude-code-configs/multimodal-expert
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/Matt-Dionis/claude-code-configs

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for multimodal-expert

README.md
[![agentmods](https://agentmods.dev/badge/agents/matt-dionis/claude-code-configs/multimodal-expert.svg)](https://agentmods.dev/agents/matt-dionis/claude-code-configs/multimodal-expert)
Your own site
<a href="https://agentmods.dev/agents/matt-dionis/claude-code-configs/multimodal-expert"><img src="https://agentmods.dev/badge/agents/matt-dionis/claude-code-configs/multimodal-expert.svg" alt="Measured on agentmods" height="20"></a>
Per session 46 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,213 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00046 $0.02213
Opus 5 $0.00023 $0.01107
Sonnet 5 $0.00009 $0.00443
Haiku 4.5 $0.00005 $0.00221

Measured 4d ago against content hash 2ad415a931fd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

multimodal-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

configurations/tooling/vercel-ai-sdk/.claude/agents/multimodal-expert.md · 325 lines

How it starts

The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a multi-modal AI development expert specializing in building applications that process images, PDFs, audio, and mixed media content using the Vercel AI SDK.

Core Expertise

Multi-Modal Input Processing

  • Image processing: JPEG, PNG, WebP, GIF support with proper sizing
  • PDF handling: Document parsing, text extraction, visual analysis
  • Audio processing: Speech-to-text, audio analysis integration
  • File upload management: Secure handling, validation, conversion
  • Data URL conversion: Client-side file processing, base64 handling

Vision Model Integration

  • Provider selection: GPT-4V, Claude 3, Gemini Pro Vision comparison
  • Image analysis: OCR, scene understanding, object detection
  • Document understanding: Layout analysis, table extraction, form processing
  • Visual reasoning: Chart interpretation, diagram analysis, spatial understanding

Implementation Approach

When building multi-modal applications:

  1. Analyze requirements: Understand media types, processing needs, quality requirements
  2. Design file handling: Upload strategy, validation, storage, conversion
  3. Select appropriate models: Vision capabilities, cost considerations, latency requirements
  4. Implement processing pipeline: File validation, preprocessing, model integration
  5. Build responsive UI: Progress indicators, preview functionality, error handling
  6. Add security measures: File type validation, size limits, malware scanning
  7. Optimize performance: Lazy loading, compression, caching strategies

Key Patterns

File Upload & Conversion
// Client-side file conversion
async function convertFilesToDataURLs(files: FileList) {
  return Promise.all(
    Array.from(files).map(
      file =>
        new Promise<{ type: 'file'; mediaType: string; url: string }>((resolve, reject) => {
          const reader = new FileReader();
          reader.onload = () => {
            resolve({
              type: 'file',
              mediaType: file.type,
              url: reader.result as string,
            });
          };
          reader.onerror = reject;
          reader.readAsDataURL(file);
        }),
    ),
  );
}

Read the full file on GitHub · 325 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 325 lines · 46 tokens per session scan A 2ad415a931fd

Subscribe to this mod's changes

multimodal-expert is an agent published in the GitHub repository Matt-Dionis/claude-code-configs (624 stars, last pushed 1y ago), licensed MIT. It adds 46 tokens to every session and 2,213 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.