ai-content-pipeline
PassBuild multi-step AI content creation pipelines combining image, video, audio, and text. Workflow examples: generate image -> animate -> add voiceover -> merge with music. Tools: FLUX, Veo, Kokoro TTS, OmniHuman, media merger, upscaling. Use for: YouTube videos, social media content, marketing materials, automated content. Triggers: content pipeline, ai workflow, content creation, multi-step ai, content automation, ai video workflow, generate and edit, ai content factory, automated content creation, ai production pipeline, media pipeline, content at scale
Install Skill
Skills are third-party code from public GitHub repositories. SkillHub scans for known malicious patterns but cannot guarantee safety. Review the source code before installing.
Install globally (user-level):
npx skillhub install openclaw/skills/ai-content-pipelineInstall in current project:
npx skillhub install openclaw/skills/ai-content-pipeline --projectSuggested path: ~/.claude/skills/ai-content-pipeline/
AI Review
Scored 56 — strong description with many triggers and real multi-step pipelines. Held back by platform lock to inference.sh ecosystem, lack of error handling, and no decision points in workflows.
SKILL.md Content
---
name: ai-content-pipeline
description: |
Build multi-step AI content creation pipelines combining image, video, audio, and text.
Workflow examples: generate image -> animate -> add voiceover -> merge with music.
Tools: FLUX, Veo, Kokoro TTS, OmniHuman, media merger, upscaling.
Use for: YouTube videos, social media content, marketing materials, automated content.
Triggers: content pipeline, ai workflow, content creation, multi-step ai,
content automation, ai video workflow, generate and edit, ai content factory,
automated content creation, ai production pipeline, media pipeline, content at scale
allowed-tools: Bash(infsh *)
---
# AI Content Pipeline

Build multi-step content creation pipelines via [inference.sh](https://inference.sh) CLI.
## Quick Start
```bash
curl -fsSL https://cli.inference.sh | sh && infsh login
# Simple pipeline: Generate image -> Animate to video
infsh app run falai/flux-dev --input '{"prompt": "portrait of a woman smiling"}' > image.json
infsh app run falai/wan-2-5 --input '{"image_url": "<url-from-previous>"}'
```
## Pipeline Patterns
### Pattern 1: Image -> Video -> Audio
```
[FLUX Image] -> [Wan 2.5 Video] -> [Foley Sound]
```
### Pattern 2: Script -> Speech -> Avatar
```
[LLM Script] -> [Kokoro TTS] -> [OmniHuman Avatar]
```
### Pattern 3: Research -> Content -> Distribution
```
[Tavily Search] -> [Claude Summary] -> [FLUX Visual] -> [Twitter Post]
```
## Complete Workflows
### YouTube Short Pipeline
Create a complete short-form video from a topic.
```bash
# 1. Generate script with Claude
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Write a 30-second script about the future of AI. Make it engaging and conversational. Just the script, no stage directions."
}' > script.json
# 2. Generate voiceover with Kokoro
infsh app run infsh/kokoro-tts --input '{
"text": "<script-text>",
"voice": "af_sarah"
}' > voice.json
# 3. Generate background image with FLUX
infsh app run falai/flux-dev --input '{
"prompt": "Futuristic city skyline at sunset, cyberpunk aesthetic, 4K wallpaper"
}' > background.json
# 4. Animate image to video with Wan
infsh app run falai/wan-2-5 --input '{
"image_url": "<background-url>",
"prompt": "slow camera pan across cityscape, subtle movement"
}' > video.json
# 5. Add captions (manually or with another tool)
# 6. Merge video with audio
infsh app run infsh/media-merger --input '{
"video_url": "<video-url>",
"audio_url": "<voice-url>"
}'
```
### Talking Head Video Pipeline
Create an AI avatar presenting content.
```bash
# 1. Write the script
infsh app run openrouter/claude-sonnet-45 --input '{
"prompt": "Write a 1-minute explainer script about quantum computing for beginners."
}' > script.json
# 2. Generate speech
infsh app run infsh/kokoro-tts --input '{
"text": "<script>",
"voice": "am_michael"
}' > speech.json
# 3. Generate or use a portrait image
infsh app run falai/flux-dev --input '{
"prompt": "Professional headshot of a friendly tech presenter, neutral background, looking at camera"
}' > portrait.json
# 4. Create talking head video
infsh app run bytedance/omnihuman-1-5 --input '{
"image_url": "<portrait-url>",
"audio_url": "<speech-url>"
}' > talking_head.json
```
### Product Demo Pipeline
Create a product showcase video.
```bash
# 1. Generate product image
infsh app run falai/flux-dev --input '{
"prompt": "Sleek wireless earbuds on white surface, studio lighting, product photography"
}' > product.json
# 2. Animate product reveal
infsh app run falai/wan-2-5 --input '{
"image_url": "<product-url>",
"prompt": "slow 360 rotation, smooth motion"
}' > product_video.json
# 3. Upscale video quality
infsh app run falai/topaz-video-upscaler --input '{
"video_url": "<product-video-url>"
}' > upscaled.json
# 4. Add background music
infsh app run infsh/media-merger --input '{
"video_url": "<upscaled-url>",
"audio_url": "https://your-music.mp3",
"audio_volume": 0.3
}'
```
### Blog to Video Pipeline
Convert written content to video format.
```bash
# 1. Summarize blog post
infsh app run openrouter/claude-haiku-45 --input '{
"prompt": "Summarize this blog post into 5 key points for a video script: <blog-content>"
}' > summary.json
# 2. Generate images for each point
for i in 1 2 3 4 5; do
infsh app run falai/flux-dev --input "{
\"prompt\": \"Visual representing point $i: <point-text>\"
}" > "image_$i.json"
done
# 3. Animate each image
for i in 1 2 3 4 5; do
infsh app run falai/wan-2-5 --input "{
\"image_url\": \"<image-$i-url>\"
}" > "video_$i.json"
done
# 4. Generate voiceover
infsh app run infsh/kokoro-tts --input '{
"text": "<full-script>",
"voice": "bf_emma"
}' > narration.json
# 5. Merge all clips
infsh app run infsh/media-merger --input '{
"videos": ["<video1>", "<video2>", "<video3>", "<video4>", "<video5>"],
"audio_url": "<narration-url>",
"transition": "crossfade"
}'
```
## Pipeline Building Blocks
### Content Generation
| Step | App | Purpose |
|------|-----|---------|
| Script | `openrouter/claude-sonnet-45` | Write content |
| Research | `tavily/search-assistant` | Gather information |
| Summary | `openrouter/claude-haiku-45` | Condense content |
### Visual Assets
| Step | App | Purpose |
|------|-----|---------|
| Image | `falai/flux-dev` | Generate images |
| Image | `google/imagen-3` | Alternative image gen |
| Upscale | `falai/topaz-image-upscaler` | Enhance quality |
### Animation
| Step | App | Purpose |
|------|-----|---------|
| I2V | `falai/wan-2-5` | Animate images |
| T2V | `google/veo-3-1-fast` | Generate from text |
| Avatar | `bytedance/omnihuman-1-5` | Talking heads |
### Audio
| Step | App | Purpose |
|------|-----|---------|
| TTS | `infsh/kokoro-tts` | Voice narration |
| Music | `infsh/ai-music` | Background music |
| Foley | `infsh/hunyuanvideo-foley` | Sound effects |
### Post-Production
| Step | App | Purpose |
|------|-----|---------|
| Upscale | `falai/topaz-video-upscaler` | Enhance video |
| Merge | `infsh/media-merger` | Combine media |
| Caption | `infsh/caption-video` | Add subtitles |
## Best Practices
1. **Plan the pipeline first** - Map out each step before running
2. **Save intermediate results** - Store outputs for iteration
3. **Use appropriate quality** - Fast models for drafts, quality for finals
4. **Match resolutions** - Keep consistent aspect ratios throughout
5. **Test each step** - Verify outputs before proceeding
## Related Skills
```bash
# Video generation models
npx skills add inferencesh/skills@ai-video-generation
# Image generation
npx skills add inferencesh/skills@ai-image-generation
# Text-to-speech
npx skills add inferencesh/skills@text-to-speech
# LLM models for scripts
npx skills add inferencesh/skills@llm-models
# Full platform skill
npx skills add inferencesh/skills@inference-sh
```
Browse all apps: `infsh app list`
## Documentation
- [Content Pipeline Example](https://inference.sh/docs/examples/content-pipeline) - Official pipeline guide
- [Building Workflows](https://inference.sh/blog/guides/ai-workflows) - Workflow best practices