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multimodal-ai skill

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This skill helps you design and deploy multimodal AI systems by integrating text, images, audio, and video using established patterns.

npx playbooks add skill omer-metin/skills-for-antigravity --skill multimodal-ai

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SKILL.md

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---
name: multimodal-ai
description: Patterns for building multimodal AI applications that combine text, images, audio, and video. Covers vision APIs, audio transcription, and unified pipelines. Use when "multimodal AI, vision API, image understanding, GPT-4V, Claude vision, audio transcription, Whisper, document extraction, image to text, " mentioned. 
---

# Multimodal Ai

## Identity



## Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

* **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here.
* **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
* **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively.

**Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

Overview

This skill provides patterns and reusable guidance for building multimodal AI applications that combine text, images, audio, and video. It centralizes best practices for integrating vision APIs, audio transcription, document extraction, and unified pipelines so teams can move from prototype to production faster. The content emphasizes concrete patterns, failure modes, and validation rules to reduce risky assumptions in multimodal systems.

How this skill works

The skill inspects your design choices against established patterns for multimodal components (vision, audio, document OCR, and unified orchestration) and highlights where to apply each pattern. It also surfaces typical sharp edges—common failure modes and root causes—and applies strict validation rules to outputs and inputs to ensure robustness. Use it to align architecture, data flow, and evaluation steps with proven approaches.

When to use it

Designing a system that combines GPT-4V / Claude Vision with text and images
Building pipelines that include audio transcription (e.g., Whisper) and downstream NLP
Adding document extraction or image-to-text features to an existing app
Validating multimodal model behavior and failure modes before deployment
Creating unified inference orchestration for mixed-media inputs

Best practices

Follow specific integration patterns for each modality rather than ad-hoc wiring
Validate inputs and outputs with deterministic rules to catch format and scale issues
Instrument components for modality-specific failure signals (e.g., low-confidence OCR)
Design pipelines with clear fallbacks and human-in-the-loop checkpoints for edge cases
Version and test preprocessing transforms for images, audio, and documents

Example use cases

Image understanding app that combines captioning, object detection, and text extraction
Customer support assistant that uses audio transcription plus visual evidence from images
Automated document ingestion that extracts structured data from photos and PDFs
Content moderation pipeline that analyzes video, frame-level images, and audio transcripts

FAQ

Which vision and audio tools does this skill recommend?

It covers mainstream vision APIs and models (GPT-4V, Claude Vision) and transcription tools like Whisper, pairing them with patterns that control input fidelity and result validation.

How does it help prevent multimodal failures?

It enumerates common sharp edges, recommends validation gates, confidence thresholds, and fallback strategies, and advises instrumentation points to detect and mitigate failures early.