multimodal-llm

name	multimodal-llm
license	MIT
compatibility	Claude Code 2.1.76+.
author	OrchestKit
version	2.1.1
description	Vision, audio, video generation, and multimodal LLM integration patterns. Use when processing images, transcribing audio, generating speech, generating AI video (Kling v3, Sora 2, Veo 3.1 std/lite/fast, Runway Gen-4.5 via `gen4_turbo`), or building multimodal AI pipelines.
tags	["vision","audio","video","multimodal","image","speech","transcription","tts","kling","sora","veo","video-generation"]
user-invocable	false
disable-model-invocation	true
context	fork
complexity	high
persuasion-type	reference
effort	high
metadata	{"category":"mcp-enhancement"}
allowed-tools	["Read","Glob","Grep","WebFetch","WebSearch"]

Multimodal LLM Patterns

Integrate vision, audio, and video generation capabilities from leading multimodal models. Covers image analysis, document understanding, real-time voice agents, speech-to-text, text-to-speech, and AI video generation (Kling v3, Sora 2, Veo 3.1 std/lite/fast tiers, Runway Gen-4.5 via gen4_turbo).

Canonical model IDs (pinned against yonatan-hq/platform/apps/api/app/config.py):

Provider	Model IDs
Anthropic	claude-opus-4-7 (latest), claude-opus-4-6, claude-sonnet-4-6, claude-haiku-4-5-20251001
OpenAI	gpt-5.2 (current flagship)
Google	gemini-3.1-pro-preview (flagship), gemini-3.1-flash-lite-preview (cost)
Veo	veo-3.1-generate-preview / veo-3.1-lite-generate-preview / veo-3.1-fast-generate-preview
Kling	kling-v3 (model_name field in Kling API)
Runway	gen4_turbo (product label: Gen-4.5)

Quick Reference

Category	Rules	Impact	When to Use
Vision: Image Analysis	1	HIGH	Image captioning, VQA, multi-image comparison, object detection
Vision: Document Understanding	1	HIGH	OCR, chart/diagram analysis, PDF processing, table extraction
Vision: Model Selection	1	MEDIUM	Choosing provider, cost optimization, image size limits
Audio: Speech-to-Text	1	HIGH	Transcription, speaker diarization, long-form audio
Audio: Text-to-Speech	1	MEDIUM	Voice synthesis, expressive TTS, multi-speaker dialogue
Audio: Model Selection	1	MEDIUM	Real-time voice agents, provider comparison, pricing
Video: Model Selection	1	HIGH	Choosing video gen provider (Kling, Sora, Veo, Runway)
Video: API Patterns	1	HIGH	Async task polling, SDK integration, webhook callbacks
Video: Multi-Shot	1	HIGH	Storyboarding, character elements, scene consistency

Total: 9 rules across 3 categories (Vision, Audio, Video Generation)

Vision: Image Analysis

Send images to multimodal LLMs for captioning, visual QA, and object detection. Always set max_tokens and resize images before encoding.

Rule	File	Key Pattern
Image Analysis	rules/vision-image-analysis.md	Base64 encoding, multi-image, bounding boxes

Vision: Document Understanding

Extract structured data from documents, charts, and PDFs using vision models.

Rule	File	Key Pattern
Document Vision	rules/vision-document.md	PDF page ranges, detail levels, OCR strategies

Vision: Model Selection

Choose the right vision provider based on accuracy, cost, and context window needs.

Rule	File	Key Pattern
Vision Models	rules/vision-models.md	Provider comparison, token costs, image limits

Audio: Speech-to-Text

Convert audio to text with speaker diarization, timestamps, and sentiment analysis.

Rule	File	Key Pattern
Speech-to-Text	rules/audio-speech-to-text.md	Gemini long-form, GPT-4o-Transcribe, AssemblyAI features

Audio: Text-to-Speech

Generate natural speech from text with voice selection and expressive cues.

Rule	File	Key Pattern
Text-to-Speech	rules/audio-text-to-speech.md	Gemini TTS, voice config, auditory cues

Audio: Model Selection

Select the right audio/voice provider for real-time, transcription, or TTS use cases.

Rule	File	Key Pattern
Audio Models	rules/audio-models.md	Real-time voice comparison, STT benchmarks, pricing

Video: Model Selection

Choose the right video generation provider based on use case, duration, and budget.

Rule	File	Key Pattern
Video Models	rules/video-generation-models.md	Kling vs Sora vs Veo vs Runway, pricing, capabilities

Video: API Patterns

Integrate video generation APIs with proper async polling, SDKs, and webhook callbacks.

Rule	File	Key Pattern
API Integration	rules/video-generation-patterns.md	Kling REST, fal.ai SDK, Vercel AI SDK, task polling

Video: Multi-Shot

Generate multi-scene videos with consistent characters using storyboarding and character elements.

Rule	File	Key Pattern
Multi-Shot	rules/video-multi-shot.md	Kling v3 character elements, 6-shot storyboards, identity binding

Key Decisions

Decision	Recommendation
High accuracy vision	claude-opus-4-7 (2,576 px, 3× Opus 4.6) or gpt-5.2
Long documents	gemini-3.1-pro-preview (1M+ context)
Cost-efficient vision	gemini-3.1-flash-lite-preview (replaces Gemini 2.5 Flash, deprecates Oct 2026)
Video analysis	gemini-3.1-pro-preview (native video, supersedes 2.5 Pro)
Voice assistant	Grok Voice Agent on Grok 4.20 (fastest, <1s)
Emotional voice AI	Gemini Live API
Long audio transcription	gemini-3.1-pro-preview (9.5hr)
Speaker diarization	AssemblyAI or Gemini
Self-hosted STT	Whisper Large V3
Character-consistent video	kling-v3 (Character Elements 3.0)
Narrative video / storytelling	Sora 2 (best cause-and-effect coherence)
Cinematic B-roll	veo-3.1-generate-preview (camera control + polished motion)
Budget drafts	veo-3.1-lite-generate-preview (~$0.05/s, 720/1080p)
Mid-tier fast renders	veo-3.1-fast-generate-preview
Professional VFX	Runway gen4_turbo (Act-Two motion transfer)
High-volume social video	kling-v3 Standard (~$0.20/video)
Open-source video gen	Wan 2.6 or LTX-2
Lip-sync / avatar video	kling-v3 (native lip-sync API)

Example

import anthropic, base64

client = anthropic.Anthropic()
with open("image.png", "rb") as f:
    b64 = base64.standard_b64encode(f.read()).decode("utf-8")

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": [
        {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": b64}},
        {"type": "text", "text": "Describe this image"}
    ]}]
)

Common Mistakes

1. Not setting max_tokens on vision requests (responses truncated)
2. Sending oversized images without resizing (>2048px)
3. Using high detail level for simple yes/no classification
4. Using STT+LLM+TTS pipeline instead of native speech-to-speech
5. Not leveraging barge-in support for natural voice conversations
6. Using deprecated models (GPT-4V, Whisper-1)
7. Ignoring rate limits on vision and audio endpoints
8. Calling video generation APIs synchronously (they're async — poll or use callbacks)
9. Generating separate clips without character elements (characters look different each time)
10. Using Sora for high-volume social content (expensive, slow — use Kling Standard instead)

Related Skills

• ork:rag-retrieval - Multimodal RAG with image + text retrieval
• ork:llm-integration - General LLM function calling patterns
• streaming-api-patterns - WebSocket patterns for real-time audio
• ork:demo-producer - Terminal demo videos (VHS, asciinema) — not AI video gen