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Ai Content Pipeline

Organization
101-skills
ai-content-pipeline

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

Overview

Publisher101-skills
Repositorysuperpowers
Skill nameai-content-pipeline
Stars
5
Forks
3
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by 101-skills on GitHub. Read the source before you install it.

Installation

Install the Ai Content Pipeline AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/101-skills/superpowers/tree/main/guides/content/ai-content-pipeline
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/101-skills/superpowers.git /tmp/superpowers
mkdir -p .claude/skills
cp -r /tmp/superpowers/guides/content/ai-content-pipeline .claude/skills/ai-content-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Content Pipeline in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Content Pipeline on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Content Pipeline is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Install the belt CLI skill: npx skills add belt-sh/cli

AI Content Pipeline

Build multi-step content creation pipelines via inference.sh CLI.

Quick Start

Requires inference.sh CLI (belt). Install instructions

bash
belt login

# Simple pipeline: Generate image -> Animate to video
belt app run falai/flux-dev --input '{"prompt": "portrait of a woman smiling"}' > image.json
belt 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
belt 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
belt app run infsh/kokoro-tts --input '{
  "prompt": "<script-text>",
  "voice": "af_sarah"
}' > voice.json

# 3. Generate background image with FLUX
belt 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
belt 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
belt 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
belt app run openrouter/claude-sonnet-45 --input '{
  "prompt": "Write a 1-minute explainer script about quantum computing for beginners."
}' > script.json

# 2. Generate speech
belt app run infsh/kokoro-tts --input '{
  "prompt": "<script>",
  "voice": "am_michael"
}' > speech.json

# 3. Generate or use a portrait image
belt 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
belt 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
belt app run falai/flux-dev --input '{
  "prompt": "Sleek wireless earbuds on white surface, studio lighting, product photography"
}' > product.json

# 2. Animate product reveal
belt app run falai/wan-2-5 --input '{
  "image_url": "<product-url>",
  "prompt": "slow 360 rotation, smooth motion"
}' > product_video.json

# 3. Upscale video quality
belt app run falai/topaz-video-upscaler --input '{
  "video_url": "<product-video-url>"
}' > upscaled.json

# 4. Add background music
belt 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
belt 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
  belt 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
  belt app run falai/wan-2-5 --input "{
    \"image_url\": \"<image-$i-url>\"
  }" > "video_$i.json"
done

# 4. Generate voiceover
belt app run infsh/kokoro-tts --input '{
  "prompt": "<full-script>",
  "voice": "bf_emma"
}' > narration.json

# 5. Merge all clips
belt app run infsh/media-merger --input '{
  "videos": ["<video1>", "<video2>", "<video3>", "<video4>", "<video5>"],
  "audio_url": "<narration-url>",
  "transition": "crossfade"
}'

Pipeline Building Blocks

Content Generation

StepAppPurpose
Scriptopenrouter/claude-sonnet-45Write content
Researchtavily/search-assistantGather information
Summaryopenrouter/claude-haiku-45Condense content

Visual Assets

StepAppPurpose
Imagefalai/flux-devGenerate images
Imagegoogle/imagen-3Alternative image gen
Upscalefalai/topaz-image-upscalerEnhance quality

Animation

StepAppPurpose
I2Vfalai/wan-2-5Animate images
T2Vgoogle/veo-3-1-fastGenerate from text
Avatarbytedance/omnihuman-1-5Talking heads

Audio

StepAppPurpose
TTSinfsh/kokoro-ttsVoice narration
Musicinfsh/ai-musicBackground music
Foleyinfsh/hunyuanvideo-foleySound effects

Post-Production

StepAppPurpose
Upscalefalai/topaz-video-upscalerEnhance video
Mergeinfsh/media-mergerCombine media
Captioninfsh/caption-videoAdd 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 inference-sh/skills@ai-video-generation

# Image generation
npx skills add inference-sh/skills@ai-image-generation

# Text-to-speech
npx skills add inference-sh/skills@text-to-speech

# LLM models for scripts
npx skills add inference-sh/skills@llm-models

# Full platform skill
npx skills add inference-sh/skills@infsh-cli

Browse all apps: belt app list

Documentation

Frequently asked questions

What does the Ai Content Pipeline AI skill do?

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

Why use Ai Content Pipeline on TypingMind?

Because you install it once and use it with any model. Ai Content Pipeline is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Content Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/101-skills/superpowers/tree/main/guides/content/ai-content-pipeline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ai Content Pipeline?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Content Pipeline?

As many as you like. As long as a model supports skills, you can use Ai Content Pipeline with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Content Pipeline AI skill free?

It is published on GitHub by 101-skills. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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