ai-automation-workflows

Overview

What is ai-automation-workflows?

ai-automation-workflows is a practical guide skill that shows you how to build automated AI workflows using the inference.sh CLI (infsh). It focuses on real-world automation patterns such as batch image generation, scheduled jobs, and reusable pipelines that call AI models from the command line.

Instead of just calling a single model once, this skill teaches you how to:

• Run batch jobs over many inputs
• Create repeatable scripts for content and data processing
• Wire AI generation into cron-like schedules and simple event-driven flows
• Build agent-style loops that can run unattended

All examples are centered around the infsh CLI and standard Bash scripting, so you can adapt them easily to your own infrastructure, CI, or servers.

Who is this skill for?

ai-automation-workflows is designed for:

• Developers and DevOps engineers who want to script AI tasks from the shell
• Data and content teams who need repeatable, scalable AI image or content generation
• Automation and MLOps engineers building AI-powered batch pipelines or cron jobs
• Power users comfortable with Bash, CLI tools, and basic scripting

If you already use the command line and want to automate AI workloads instead of running them manually in a UI, this skill is a good fit.

What problems does it solve?

Use ai-automation-workflows when you need to:

• Generate multiple AI images or assets at once with consistent settings
• Run daily or hourly jobs (e.g., a new image or report every morning)
• Integrate AI calls into existing scripts, build steps, or data pipelines
• Standardize how your team calls AI models via a single CLI interface

It is especially helpful when manual UI-driven workflows become slow, error-prone, or hard to reproduce.

When is it not a good fit?

This skill may not be ideal if:

• You are not comfortable using the terminal or editing Bash scripts
• You need a no-code visual workflow builder rather than CLI-based automation
• Your use case requires complex distributed orchestration beyond simple scripts

In those cases, you might still use ai-automation-workflows as a reference, but you will likely want additional tooling or a higher-level orchestrator.

How to Use

1. Installation and prerequisites

Before using ai-automation-workflows, make sure you have:

• inference.sh CLI (infsh) installed
• Access to the inferen-sh/skills repository
• A terminal environment that can run Bash scripts

To install the skill into a compatible host environment that supports skills, use:

npx skills add https://github.com/inferen-sh/skills --skill ai-automation-workflows

Then, install and configure the inference.sh CLI by following the upstream instructions:

# Install infsh (see upstream docs for your platform specifics)
# After installation, authenticate:
infsh login

The skill content itself lives under the guides/content/ai-automation-workflows path in the inferen-sh/skills repository, with the primary overview in SKILL.md.

2. Core concepts in ai-automation-workflows

The skill is organized around a few key automation concepts:

• Quick Start – a minimal example that logs in with infsh and runs a single AI model call
• Automation Patterns – structured examples for batch jobs and pipelines
• Batch Processing – running the same workflow over a list of prompts or inputs
• Sequential Pipelines – chaining multiple steps together (e.g., prompt generation then image creation)

You can explore these concepts in SKILL.md inside the repository. They are designed to be copied, modified, and integrated into your own scripts.

3. Quick start: run a simple automated job

The simplest way to see ai-automation-workflows in action is to run the daily image generation example using the inference.sh CLI. After infsh login, you can use a command like:

infsh app run falai/flux-dev --input '{
  "prompt": "Inspirational quote background, minimalist design, date: '"$(date +%Y-%m-%d)"'"
}'

This pattern shows how to:

• Call a model (falai/flux-dev) from the CLI
• Pass structured JSON input
• Dynamically include the current date with $(date +%Y-%m-%d)

From here, you can turn this into a daily cron job or integrate it into your existing deployment scripts.

4. Pattern: batch processing with Bash

One of the main examples in ai-automation-workflows is batch image generation. The pattern uses a Bash array of prompts and loops over them, calling infsh for each item.

A simplified structure looks like this:

#!/bin/bash
# batch_images.sh - Generate images for multiple prompts

PROMPTS=(
  "Mountain landscape at sunrise"
  "Ocean waves at sunset"
  "Forest path in autumn"
  "Desert dunes at night"
)

for prompt in "${PROMPTS[@]}"; do
  echo "Generating: $prompt"
  infsh app run falai/flux-dev --input "{ \"prompt\": \"$prompt\" }"
  # Add logging, output handling, or error checks as needed
done

What this pattern gives you:

• Consistent parameters across all runs
• A simple way to scale from one item to many items
• A template for batch jobs in any domain (not just images)

You can adapt the prompts and model to your own use case, or swap in a different infsh app run target.

5. Pattern: building sequential AI pipelines

ai-automation-workflows also shows how to move from single calls to pipelines, where one step’s output feeds the next. For example:

1. Generate or transform structured text or prompts in one step.
2. Use that text as input to an image, summarization, or classification model.
3. Optionally post-process or store the results.

In practice, this means:

• Calling one infsh app run command
• Parsing its output (JSON or text)
• Passing it as input to another infsh app run in the same Bash script

This sequential pattern is the basis for more advanced agent-style loops and multi-step workflows.

6. Integrating with cron and scheduled jobs

While the repository focuses on CLI patterns, they are easy to turn into scheduled jobs using standard tools like cron. A typical approach is:

1. Put your workflow into a script, e.g. daily_image.sh.
2. Ensure it runs successfully when called manually.
3. Register it with your scheduler:

crontab -e
# Example: run every day at 08:00
0 8 * * * /usr/bin/bash /path/to/daily_image.sh >> /var/log/ai-daily.log 2>&1

This turns a one-off example from ai-automation-workflows into a reliable scheduled job that generates new AI content on a fixed cadence.

7. Customizing the skill for your stack

After you have the examples running, adapt them to your environment by:

• Changing the model IDs in infsh app run ... to match your preferred models
• Adjusting input JSON fields for your content or data schemas
• Integrating logging, metrics, or notifications into your Bash scripts
• Embedding scripts into CI/CD, data processing, or reporting pipelines

Because ai-automation-workflows relies on standard CLI and Bash patterns, it works well on local machines, servers, and containerized environments.

FAQ

Is ai-automation-workflows a library or a guide?

ai-automation-workflows is a guide-style skill inside the inferen-sh/skills repository. It does not ship a compiled library or package; instead, it provides examples, patterns, and scripts that show you how to orchestrate AI calls with the inference.sh CLI.

What do I need installed to use ai-automation-workflows?

You need:

• The inference.sh CLI (infsh) installed and authenticated (infsh login)
• A shell environment capable of running Bash scripts
• Access to the inferen-sh/skills repository (for reading SKILL.md and related guides)

The skill itself is added to your host using:

npx skills add https://github.com/inferen-sh/skills --skill ai-automation-workflows

Can I use ai-automation-workflows without Bash?

The skill’s examples are written for Bash and the infsh CLI. You can port the logic to other languages (e.g., a Python SDK or other shell), but that will require your own adaptations. The repository explicitly mentions Bash and the CLI as primary tools.

Does ai-automation-workflows support image generation only?

The main concrete examples showcase image generation with models like falai/flux-dev. However, the patterns (batch processing, scheduling, sequential pipelines) apply to any AI model you can invoke through the infsh CLI. You can swap in other apps or models as long as they are compatible with inference.sh.

How does this relate to workflow automation tools?

ai-automation-workflows gives you the building blocks for workflow automation with AI:

• Batch jobs
• Scheduled runs
• Simple pipelines

You can use these patterns directly with cron, CI, or your own scripts, or embed them inside larger automation frameworks. For more complex, multi-service orchestration, you may combine this skill’s CLI patterns with other workflow tools.

Is ai-automation-workflows production-ready?

The skill itself is an educational guide. The patterns are production-oriented, but you will need to:

• Add robust error handling and retries
• Configure logging and monitoring as required
• Secure credentials and tokens used by infsh

Use the provided scripts as starting points and harden them according to your organization’s standards.

Where do I find the source files for this skill?

The skill content for ai-automation-workflows lives in the inferen-sh/skills repository on GitHub, under:

• SKILL.md – high-level overview and examples
• guides/content/ai-automation-workflows – additional guide content and context

Open these files to see the full examples, then clone or copy what you need into your own projects.