# Automate AI Workflows

AI automation often needs repeatable scripts, scheduling, retries, and monitoring. This skill provides workflow patterns for inference.sh jobs, content pipelines, and batch processing.

## Install

```bash
npx skillstore add inference-sh/ai-automation-workflows
```

## Metadata

- Status: approved
- Slug: inference-sh-ai-automation-workflows
- Skillstore revision: r1
- Version status: missing
- Tree hash: 13eaa038e39d8f4e12db07c721bcc10f5620ec4330e58cbd445a6090db0770e6
- Author: inference-sh
- GitHub username: inference-sh
- License: MIT
- Repository: https://github.com/inference-sh/skills/tree/main/skills/ai-automation-workflows/
- Ref: a06681402992ceae98ba04d54cfd4ab004862696
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: blocked
- Manual install advisory: allowed\_with\_warning
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network
- Quality score: 38
- Quality tier: warning
- Public page: https://skillstore.pages.dev/skills/inference-sh-ai-automation-workflows
- Manifest: https://skillstore.pages.dev/api/skills/inference-sh-ai-automation-workflows/manifest

## Capabilities

- Provides Bash patterns for batch, sequential, parallel, conditional, and retry workflows using inference.sh CLI.
- Shows cron scheduling examples for recurring content generation and report scripts.
- Demonstrates logging, retry, fallback, and alert wrappers for command-line AI jobs.
- Includes a Python subprocess wrapper for calling infsh app run with JSON input.
- Provides templates for content calendars and text file analysis pipelines.

## Use Cases

- Plan Content Automation: Create repeatable workflows for image generation, captions, weekly themes, and scheduled publishing preparation.
- Process Text Files: Design AI-assisted pipelines that analyze local text files and save structured summaries for review.
- Prototype Scheduled Jobs: Draft AI job templates with retries, logging, failure alerts, and cron-based execution plans.

## Prompt Templates

### Create a Batch Workflow

```
Use ai-automation-workflows to design a batch image generation workflow for five product themes. Include rate limits and output naming.
```

### Build a Scheduled Job

```
Create a daily AI content job plan for my team. Include inputs, outputs, scheduling, logging, and manual review steps.
```

### Add Monitoring

```
Improve this AI workflow for production use. Add retries, timeout guidance, failure alerts, and safe log handling.
```

### Design a Multi-Step Pipeline

```
Design a multi-step AI pipeline for research, drafting, image generation, and social summaries. Include privacy and approval guardrails.
```

## Limitations

- Requires an inference.sh account and an installed infsh command.
- Examples need security review before production use.
- Does not manage API credentials, access controls, or secret storage.
- Some templates send local or generated data to third-party AI services.

## Best Practices

- Review every command before execution, especially installers, cron changes, and webhook calls.
- Redact secrets and customer data before sending content to AI services.
- Start with manual runs before enabling scheduled jobs.

## Anti Patterns

- Piping remote installers directly into a shell.
- Sending local files to external models without consent or redaction.
- Creating cron jobs without logging, ownership, and cleanup guidance.

## Security Audit

- Audited at: 2026-07-06T15:34:13.941\+00:00
- Summary: The audit confirms a critical remote installer piped to shell, cron-based persistence guidance, a risky dynamic command wrapper, and local file content sent to external AI services. Most other shell detections are Markdown examples or safe local substitutions rather than hidden execution. The skill needs security hardening before publication.

## Stats

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- Downloads: 13
- Favorites: 0
- Popularity score: 0