ai-content-pipeline

Overview

What is ai-content-pipeline?

ai-content-pipeline is a workflow-automation skill that helps you build multi-step AI content creation pipelines using the inference.sh CLI. It focuses on orchestrating tools for image, video, audio, and text so you can turn ideas into finished media with repeatable, scriptable workflows.

Typical example flows include:

• Generate an image with FLUX → animate it into a video with Wan 2.5 → add Foley sound or music
• Turn a written script into speech with Kokoro TTS → drive an OmniHuman avatar video
• Run research with search tools → summarize with an LLM → output content formatted for social channels or marketing assets

Who is this skill for?

Use ai-content-pipeline if you:

• Produce YouTube or short-form video and want to automate parts of your content workflow
• Create social media or marketing assets and need consistent, repeatable AI pipelines
• Work with image, video, and audio models via CLI and want a single, connected process instead of manual, one-off commands
• Experiment with media workflows and need examples of how to chain different AI tools together

It’s a strong fit for technical creators, growth and content marketers, and engineers building media automation around inference.sh.

What problems does it solve?

ai-content-pipeline is designed to:

• Reduce manual steps between image generation, video creation, audio, and distribution
• Standardize workflows so you can reuse the same pipeline across multiple pieces of content
• Show concrete patterns for chaining apps like FLUX, Wan 2.5, Kokoro TTS, and OmniHuman
• Serve as a reference for building your own custom content-production pipelines on top of the inference.sh CLI

It does not fine-tune models or replace professional editing tools; instead, it coordinates AI services and media tools so they work together as one pipeline.

When is ai-content-pipeline not a good fit?

You might not need this skill if:

• You want a GUI-only editor and don’t plan to use terminal or CLI tools
• You only run single-step generations (e.g., one-off images) and don’t need automation
• You need highly customized, model-level changes (fine-tuning, custom training) rather than workflow orchestration

If you are comfortable with basic CLI usage and want multi-step AI media workflows (image → video → audio → content), ai-content-pipeline is a suitable choice.

How to Use

Prerequisites

Before using ai-content-pipeline, make sure you have:

• inference.sh CLI (infsh) installed
  • Follow the official install instructions: https://raw.githubusercontent.com/inference-sh/skills/refs/heads/main/cli-install.md
• An inference.sh account and login
  • You will authenticate from the terminal using infsh login.
• Basic terminal and JSON familiarity
  • You’ll be running infsh app run ... commands and passing JSON input.

1. Install the ai-content-pipeline skill

Install the skill into your agent or local skills setup:

npx skills add https://github.com/inferen-sh/skills --skill ai-content-pipeline

This pulls the ai-content-pipeline definition from the inferen-sh/skills repository so your agent can reference its guidance and patterns.

2. Log in with the inference.sh CLI

From your terminal:

infsh login

Follow the prompts to authenticate. Once logged in, you can run pipelines that call AI apps such as FLUX, Wan 2.5, Kokoro TTS, and others.

3. Run a simple image → video pipeline

The skill’s core patterns are demonstrated using infsh app run commands.

Basic example from the repository:

# Step 1: Generate an image with FLUX
infsh app run falai/flux-dev --input '{"prompt": "portrait of a woman smiling"}' > image.json

# Step 2: Animate that image into a video with Wan 2.5
infsh app run falai/wan-2-5 --input '{"image_url": "<url-from-previous>"}'

Workflow notes:

• The first command creates an image using FLUX and writes the result to image.json.
• Extract the image URL from image.json and insert it into the second command’s image_url field.
• The second command uses Wan 2.5 to animate the image into a video.

You can wrap this logic in your own scripts or agent instructions to fully automate the hand-off between steps.

4. Use the pipeline patterns as templates

The skill documents several high-level patterns you can adapt:

Pattern 1: Image → Video → Audio

[FLUX Image] -> [Wan 2.5 Video] -> [Foley Sound]

Use this to:

• Create short product clips from static images and add ambient sound
• Animate character art for social posts or promotional teasers

Pattern 2: Script → Speech → Avatar

[LLM Script] -> [Kokoro TTS] -> [OmniHuman Avatar]

Use this to:

• Turn blog posts or marketing copy into voiceover videos
• Generate talking-head avatar content for social media or internal training

Pattern 3: Research → Content → Distribution

The repository pattern (truncated in preview) follows this structure:

[Tavily Search] -> [Claude Summary/Content] -> [Channel-specific Output]

Use this to:

• Research a topic using a search tool like Tavily
• Summarize and compose content with an LLM (e.g., Claude)
• Format outputs for specific channels (Twitter/X, LinkedIn, newsletters, YouTube descriptions)

5. Integrate with your own workflows

Once you understand the examples, you can:

• Replace tools: Swap in different apps in infsh app run for image, video, or audio generation if you prefer alternative models.
• Add steps: Insert upscaling, media merging, or additional editing tools between stages (for example, upscaling the FLUX image before animation).
• Script the pipeline: Put your series of infsh commands into a shell script or agent rule so you can trigger the whole pipeline with a single command or prompt.
• Parameterize inputs: Pass dynamic prompts, scripts, or URLs from your agent or another system to generate content on demand.

6. Explore repository files for deeper guidance

In your skills installation or directly in GitHub, open:

• SKILL.md – Primary definition and description of ai-content-pipeline; outlines tools, patterns, and quick start example.
• guides/content/ai-content-pipeline – Additional context and guides for content-focused workflows (location may include supporting docs or examples in the main repo).

Use these as reference material when extending the skill or designing your own workflows.

FAQ

What does ai-content-pipeline actually install?

The ai-content-pipeline skill installs metadata, documentation, and workflow guidance from the inferen-sh/skills repository so your agent understands how to orchestrate AI content workflows using the inference.sh CLI. The heavy lifting (image, video, audio generation) runs through infsh and the underlying AI apps, not inside the skill itself.

Do I need the inference.sh CLI to use ai-content-pipeline?

Yes. The core examples and patterns in ai-content-pipeline depend on the infsh CLI. Install it using the official instructions at cli-install.md, then run infsh login before executing any pipelines.

Can I use ai-content-pipeline without writing code?

You do not need to write full applications, but you should be comfortable running terminal commands and editing basic JSON. The skill is CLI-first and best suited to users who can work with infsh app run commands or automate them via simple scripts.

Which AI tools and models can I connect in these pipelines?

The examples in ai-content-pipeline reference tools such as:

• FLUX for image generation
• Wan 2.5 for turning images into video
• Kokoro TTS for text-to-speech
• OmniHuman for avatar video
• Foley sound and media-merging tools for audio and compositing

You can swap or extend these with other apps available in the inference.sh ecosystem, as long as they can be called via infsh app run.

Is ai-content-pipeline suitable for YouTube and social-media content?

Yes. The repository description explicitly calls out use cases such as YouTube videos, social media content, marketing materials, and automated content production. The pipeline patterns are well-suited for creating explainers, shorts, promos, and templated content at scale.

How does this skill help with marketing and content operations?

ai-content-pipeline provides patterns that connect research, writing, media generation, and distribution formatting. For example, you can:

• Research a topic with search tools
• Generate a script or post with an LLM
• Turn it into video with voice and avatar
• Format variants for different social channels

This reduces manual copy-pasting between tools and helps teams scale content production more reliably.

Can I customize the steps in each pipeline?

Yes. The patterns in ai-content-pipeline are templates, not rigid rules. You can:

• Insert extra steps (e.g., upscaling, caption generation, thumbnail creation)
• Change the order of operations if your use case requires it
• Swap out individual apps as long as they are supported by inference.sh

Does ai-content-pipeline store or manage my media files?

No. ai-content-pipeline describes how to call AI apps and chain their outputs via the inference.sh CLI. Storage, asset management, and long-term organization of files are up to your own environment, scripts, or content management systems.

Where can I see or modify the underlying configuration?

Open the skill and guides in the inferen-sh/skills GitHub repository, particularly:

• SKILL.md for the ai-content-pipeline definition and quick start
• Any related files under guides/content/ai-content-pipeline for extended guidance

You can adapt the examples and commands into your own repositories, scripts, or agent rules to match your production setup.