Entry point: /faion-net — invoke this skill for automatic routing to the appropriate domain.
Multimodal AI Skill
Communication: User's language. Code: English.
Purpose
Handles multimodal AI applications. Covers vision, image generation, video generation, speech, and voice synthesis.
Context Discovery
Auto-Investigation
Check these project signals before asking questions:
| Signal |
Where to Check |
What to Look For |
| Dependencies |
package.json, requirements.txt |
openai, PIL/pillow, ffmpeg-python, elevenlabs |
| Media files |
/images, /audio, /video |
Input files to process |
| API usage |
Grep for "images.generate", "audio.transcriptions" |
Existing multimodal APIs |
| Output dirs |
/generated, /output |
Where generated content goes |
Discovery Questions
question: "Which modality are you working with?"
header: "Modality"
multiSelect: true
options:
- label: "Vision (image understanding)"
description: "GPT-4o Vision, Gemini Vision for OCR/analysis"
- label: "Image generation"
description: "DALL-E 3, Midjourney, Stable Diffusion"
- label: "Video generation/understanding"
description: "Sora, Runway, or video analysis"
- label: "Speech-to-text"
description: "Whisper, Deepgram for transcription"
- label: "Text-to-speech"
description: "OpenAI TTS, ElevenLabs for voice synthesis"
question: "What's your primary use case?"
header: "Use Case"
multiSelect: false
options:
- label: "Document/receipt OCR and analysis"
description: "Extract structured data from images"
- label: "Content generation (images/videos)"
description: "Create marketing/creative assets"
- label: "Accessibility (vision/speech conversion)"
description: "Convert between modalities for a11y"
- label: "Voice assistant/bot"
description: "Speech → Text → LLM → TTS pipeline"
question: "Volume and latency requirements?"
header: "Scale"
multiSelect: false
options:
- label: "Low volume, quality over speed"
description: "Use premium models (HD TTS, GPT-4o Vision)"
- label: "High volume, optimize for cost"
description: "Batch APIs, smaller models"
- label: "Real-time required"
description: "Streaming APIs (Deepgram, OpenAI TTS)"
- label: "Async processing OK"
description: "Queue-based approach"
Scope
| Area |
Coverage |
| Vision |
GPT-4o Vision, Gemini Vision, image understanding |
| Image Generation |
DALL-E 3, Midjourney, Stable Diffusion |
| Video Generation |
Sora, Runway, Pika |
| Speech-to-Text |
Whisper, Deepgram, AssemblyAI |
| Text-to-Speech |
OpenAI TTS, ElevenLabs, Google TTS |
| Voice |
Real-time voice, voice cloning |
Quick Start
| Task |
Files |
| Vision API |
vision-basics.md → vision-applications.md |
| Image generation |
img-gen-basics.md → img-gen-tools.md |
| Video generation |
video-gen-basics.md → video-gen-tools.md |
| Speech-to-text |
speech-to-text-basics.md → speech-to-text-advanced.md |
| Text-to-speech |
tts-basics.md → tts-implementation.md |
| Voice synthesis |
voice-basics.md → voice-implementation.md |
Methodologies (12)
Vision (2):
- vision-basics: Image understanding, OCR, scene analysis
- vision-applications: Use cases, production patterns
Image Generation (2):
- img-gen-basics: Prompt engineering, models
- img-gen-tools: DALL-E 3, Midjourney, Stable Diffusion
Video Generation (2):
- video-gen-basics: Fundamentals, prompting
- video-gen-tools: Sora, Runway, Pika, Luma
Speech-to-Text (2):
- speech-to-text-basics: Whisper API, real-time
- speech-to-text-advanced: Diarization, timestamps
Text-to-Speech (2):
- tts-basics: Voice selection, SSML
- tts-implementation: Production patterns, streaming
Voice (2):
- voice-basics: Real-time voice, cloning
- voice-implementation: Integration patterns
Code Examples
GPT-4o Vision
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "What's in this image?"},
{"type": "image_url", "image_url": {"url": "https://..."}}
]
}]
)
DALL-E 3 Image Generation
from openai import OpenAI
client = OpenAI()
response = client.images.generate(
model="dall-e-3",
prompt="A futuristic city with flying cars",
size="1024x1024",
quality="hd",
n=1
)
image_url = response.data[0].url
Whisper Speech-to-Text
from openai import OpenAI
client = OpenAI()
audio_file = open("speech.mp3", "rb")
transcription = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file,
response_format="verbose_json",
timestamp_granularities=["word"]
)
print(transcription.text)
OpenAI TTS
from openai import OpenAI
from pathlib import Path
client = OpenAI()
response = client.audio.speech.create(
model="tts-1-hd",
voice="alloy",
input="Hello, this is a test of text to speech."
)
response.stream_to_file("speech.mp3")
Gemini Vision
import google.generativeai as genai
genai.configure(api_key="...")
model = genai.GenerativeModel('gemini-pro-vision')
image = PIL.Image.open("image.jpg")
response = model.generate_content([
"Describe this image in detail",
image
])
print(response.text)
Model Comparison
Vision Models
| Model |
Best For |
Max Image Size |
| GPT-4o |
General vision, OCR |
20MB |
| Gemini Pro Vision |
High-res images |
20MB |
| Claude Sonnet 4 |
Document analysis |
5MB |
Image Generation
| Model |
Best For |
Cost |
| DALL-E 3 |
Photorealistic, text |
$$$ |
| Midjourney |
Artistic, creative |
$$ |
| Stable Diffusion |
Custom, open-source |
Free/$ |
Speech-to-Text
| Service |
Best For |
Languages |
| Whisper |
General, multilingual |
99 |
| Deepgram |
Real-time, low latency |
30+ |
| AssemblyAI |
Features, diarization |
10+ |
Text-to-Speech
| Service |
Best For |
Voices |
| OpenAI TTS |
Quality, variety |
6 |
| ElevenLabs |
Cloning, realism |
Custom |
| Google TTS |
Languages, SSML |
400+ |
Use Cases
| Use Case |
Modalities |
| Document analysis |
Vision → Text |
| Video narration |
Video → Speech → TTS |
| Voice assistant |
Speech → LLM → TTS |
| Content generation |
Text → Images/Video |
| Accessibility |
Vision → TTS, Speech → Text |
Related Skills
| Skill |
Relationship |
| faion-llm-integration |
Provides vision APIs |
| faion-ai-agents |
Multimodal agents |
Multimodal AI v1.0 | 12 methodologies
1---2name: faion-multimodal-ai3description: Multimodal AI: vision, image/video generation, speech-to-text, text-to-speech, voice synthesis.4---5> **Entry point:** `/faion-net` — invoke this skill for automatic routing to the appropriate domain.
6
7# Multimodal AI Skill
8
9**Communication: User's language. Code: English.**
10
11## Purpose
12
13Handles multimodal AI applications. Covers vision, image generation, video generation, speech, and voice synthesis.
14
15## Context Discovery
16
17### Auto-Investigation
18
19Check these project signals before asking questions:
20
21| Signal | Where to Check | What to Look For |
22|--------|----------------|------------------|
23| **Dependencies** | package.json, requirements.txt | openai, PIL/pillow, ffmpeg-python, elevenlabs |
24| **Media files** | /images, /audio, /video | Input files to process |
25| **API usage** | Grep for "images.generate", "audio.transcriptions" | Existing multimodal APIs |
26| **Output dirs** | /generated, /output | Where generated content goes |
27
28### Discovery Questions
29
30```yaml
31question: "Which modality are you working with?"
32header: "Modality"
33multiSelect: true
34options:
35 - label: "Vision (image understanding)"
36 description: "GPT-4o Vision, Gemini Vision for OCR/analysis"
37 - label: "Image generation"
38 description: "DALL-E 3, Midjourney, Stable Diffusion"
39 - label: "Video generation/understanding"
40 description: "Sora, Runway, or video analysis"
41 - label: "Speech-to-text"
42 description: "Whisper, Deepgram for transcription"
43 - label: "Text-to-speech"
44 description: "OpenAI TTS, ElevenLabs for voice synthesis"
45```
46
47```yaml
48question: "What's your primary use case?"
49header: "Use Case"
50multiSelect: false
51options:
52 - label: "Document/receipt OCR and analysis"
53 description: "Extract structured data from images"
54 - label: "Content generation (images/videos)"
55 description: "Create marketing/creative assets"
56 - label: "Accessibility (vision/speech conversion)"
57 description: "Convert between modalities for a11y"
58 - label: "Voice assistant/bot"
59 description: "Speech → Text → LLM → TTS pipeline"
60```
61
62```yaml
63question: "Volume and latency requirements?"
64header: "Scale"
65multiSelect: false
66options:
67 - label: "Low volume, quality over speed"
68 description: "Use premium models (HD TTS, GPT-4o Vision)"
69 - label: "High volume, optimize for cost"
70 description: "Batch APIs, smaller models"
71 - label: "Real-time required"
72 description: "Streaming APIs (Deepgram, OpenAI TTS)"
73 - label: "Async processing OK"
74 description: "Queue-based approach"
75```
76
77## Scope
78
79| Area | Coverage |
80|------|----------|
81| **Vision** | GPT-4o Vision, Gemini Vision, image understanding |
82| **Image Generation** | DALL-E 3, Midjourney, Stable Diffusion |
83| **Video Generation** | Sora, Runway, Pika |
84| **Speech-to-Text** | Whisper, Deepgram, AssemblyAI |
85| **Text-to-Speech** | OpenAI TTS, ElevenLabs, Google TTS |
86| **Voice** | Real-time voice, voice cloning |
87
88## Quick Start
89
90| Task | Files |
91|------|-------|
92| Vision API | vision-basics.md → vision-applications.md |
93| Image generation | img-gen-basics.md → img-gen-tools.md |
94| Video generation | video-gen-basics.md → video-gen-tools.md |
95| Speech-to-text | speech-to-text-basics.md → speech-to-text-advanced.md |
96| Text-to-speech | tts-basics.md → tts-implementation.md |
97| Voice synthesis | voice-basics.md → voice-implementation.md |
98
99## Methodologies (12)
100
101**Vision (2):**
102- vision-basics: Image understanding, OCR, scene analysis
103- vision-applications: Use cases, production patterns
104
105**Image Generation (2):**
106- img-gen-basics: Prompt engineering, models
107- img-gen-tools: DALL-E 3, Midjourney, Stable Diffusion
108
109**Video Generation (2):**
110- video-gen-basics: Fundamentals, prompting
111- video-gen-tools: Sora, Runway, Pika, Luma
112
113**Speech-to-Text (2):**
114- speech-to-text-basics: Whisper API, real-time
115- speech-to-text-advanced: Diarization, timestamps
116
117**Text-to-Speech (2):**
118- tts-basics: Voice selection, SSML
119- tts-implementation: Production patterns, streaming
120
121**Voice (2):**
122- voice-basics: Real-time voice, cloning
123- voice-implementation: Integration patterns
124
125## Code Examples
126
127### GPT-4o Vision
128
129```python
130from openai import OpenAI
131
132client = OpenAI()
133
134response = client.chat.completions.create(
135 model="gpt-4o",
136 messages=[{
137 "role": "user",
138 "content": [
139 {"type": "text", "text": "What's in this image?"},
140 {"type": "image_url", "image_url": {"url": "https://..."}}
141 ]
142 }]
143)
144```
145
146### DALL-E 3 Image Generation
147
148```python
149from openai import OpenAI
150
151client = OpenAI()
152
153response = client.images.generate(
154 model="dall-e-3",
155 prompt="A futuristic city with flying cars",
156 size="1024x1024",
157 quality="hd",
158 n=1
159)
160
161image_url = response.data[0].url
162```
163
164### Whisper Speech-to-Text
165
166```python
167from openai import OpenAI
168
169client = OpenAI()
170
171audio_file = open("speech.mp3", "rb")
172transcription = client.audio.transcriptions.create(
173 model="whisper-1",
174 file=audio_file,
175 response_format="verbose_json",
176 timestamp_granularities=["word"]
177)
178
179print(transcription.text)
180```
181
182### OpenAI TTS
183
184```python
185from openai import OpenAI
186from pathlib import Path
187
188client = OpenAI()
189
190response = client.audio.speech.create(
191 model="tts-1-hd",
192 voice="alloy",
193 input="Hello, this is a test of text to speech."
194)
195
196response.stream_to_file("speech.mp3")
197```
198
199### Gemini Vision
200
201```python
202import google.generativeai as genai
203
204genai.configure(api_key="...")
205model = genai.GenerativeModel('gemini-pro-vision')
206
207image = PIL.Image.open("image.jpg")
208response = model.generate_content([
209 "Describe this image in detail",
210 image
211])
212
213print(response.text)
214```
215
216## Model Comparison
217
218### Vision Models
219
220| Model | Best For | Max Image Size |
221|-------|----------|----------------|
222| GPT-4o | General vision, OCR | 20MB |
223| Gemini Pro Vision | High-res images | 20MB |
224| Claude Sonnet 4 | Document analysis | 5MB |
225
226### Image Generation
227
228| Model | Best For | Cost |
229|-------|----------|------|
230| DALL-E 3 | Photorealistic, text | $$$ |
231| Midjourney | Artistic, creative | $$ |
232| Stable Diffusion | Custom, open-source | Free/$ |
233
234### Speech-to-Text
235
236| Service | Best For | Languages |
237|---------|----------|-----------|
238| Whisper | General, multilingual | 99 |
239| Deepgram | Real-time, low latency | 30+ |
240| AssemblyAI | Features, diarization | 10+ |
241
242### Text-to-Speech
243
244| Service | Best For | Voices |
245|---------|----------|--------|
246| OpenAI TTS | Quality, variety | 6 |
247| ElevenLabs | Cloning, realism | Custom |
248| Google TTS | Languages, SSML | 400+ |
249
250## Use Cases
251
252| Use Case | Modalities |
253|----------|------------|
254| Document analysis | Vision → Text |
255| Video narration | Video → Speech → TTS |
256| Voice assistant | Speech → LLM → TTS |
257| Content generation | Text → Images/Video |
258| Accessibility | Vision → TTS, Speech → Text |
259
260## Related Skills
261
262| Skill | Relationship |
263|-------|-------------|
264| faion-llm-integration | Provides vision APIs |
265| faion-ai-agents | Multimodal agents |
266
267---
268
269*Multimodal AI v1.0 | 12 methodologies*