Operational Enterprise Ai logo

Operational Enterprise Ai

Community
boraoztunc
operational-enterprise-ai

Create or redesign enterprise AI, automation, security, and operations product pages that explain system boundaries, approvals, auditability, exceptions, and rollback. Use for dark cinematic heroes, hairline grids, metric pauses, expandable solution rows, case-study evidence, security proof, and qualified demo or waitlist handoffs.

Overview

Publisherboraoztunc
Repositoryskills
Skill nameoperational-enterprise-ai
Stars
422
Forks
63
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by boraoztunc on GitHub. Read the source before you install it.

Installation

Install the Operational Enterprise Ai 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/boraoztunc/skills/tree/main/operational-enterprise-ai
  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/boraoztunc/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/operational-enterprise-ai .claude/skills/operational-enterprise-ai
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Operational Enterprise Ai 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 Operational Enterprise Ai 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 Operational Enterprise Ai 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.

Operational Enterprise AI

Build credibility by showing what the system does, where it stops, who approves actions, and how failures recover.

Establish the Story

  1. Open with the operational problem and one restrained product image or system trace.
  2. Use a white interlude to quantify the problem with verified metrics.
  3. Explain each capability as a workflow with permissions and controls.
  4. Address security, governance, exceptions, and rollback before the conversion ask.
  5. Move from verified case-study evidence into a qualified demo or waitlist handoff.

Replace source brands, customers, numbers, security badges, screenshots, and claims. Do not invent compliance or performance evidence.

Build the Visual System

  • Use near-black, warm white, muted gray, and one restrained spectral treatment.
  • Pair a high-x-height sans-serif with compact mono labels and tabular numerals.
  • Use hard grid lines, square media, low radii, and minimal shadow.
  • Keep data legibility ahead of atmosphere.
  • Reserve white chapters for operational explanation and metric pauses.
  • Avoid glowing AI orbs, particle fields, neon gradients, and generic cyber-security imagery.

Compose the Page

  • Header: show product, solutions, security, case studies, and one qualified action.
  • Hero: state the system boundary and pair it with one deliberate operational visual.
  • Metrics: use only verified numbers with scope, source, and timeframe.
  • Solution rows: summarize workflow, permissions, action, approval, output, audit, exception, and rollback.
  • Product demo: show real or clearly labeled sample data and deterministic state changes.
  • Security: connect controls to concrete risks; do not use unsupported badges.
  • Case studies: separate verified implementation facts from marketing interpretation.
  • Testimonials: use grayscale portrait evidence only when licensed and real.
  • FAQ: resolve ownership, data handling, integrations, review, failure, and procurement questions.
  • Final CTA: qualify who the product is for and explain what happens after submission.

Implement Operational Solution Rows

  • Keep summary, permissions, action, approval, output, audit, exception, and rollback in a stable data model.
  • Use semantic disclosure with equivalent hover and focus cues.
  • Support Enter and Space expansion and keep text equivalents for diagrams.
  • Design loading, unavailable integration, insufficient permission, stale data, denied approval, partial completion, rollback, and error states.
  • Keep the buyer able to evaluate scope and risk without animation.

Implement the Case-Study Handoff

  • Connect each verified use case to the relevant demo or waitlist context.
  • Preserve attribution and separate facts, quotes, and inferred outcomes.
  • Prefill only non-sensitive intent data and only with consent.
  • Design filters, links, form fields, loading, disabled, duplicate, validation, network error, and success states.
  • Explain who receives the request, expected response, and data use.

Motion Defaults

  • Use 160–220ms for controls and 500–760ms for section entrances.
  • Favor slow background media, precise metric reveals, and restrained row transitions.
  • Use hard black-to-white handoffs instead of gratuitous smooth scrolling.
  • Keep parallax below 5%, pause offscreen work, and clean up observers.
  • Render settled states immediately under reduced motion.

Validate

  • Review every metric, case study, quote, badge, and compliance statement against evidence.
  • Test solution rows, filters, forms, errors, focus return, and browser history with keyboard and touch.
  • Verify diagram text alternatives, contrast, 200% zoom, mobile order, loading, stale data, and reduced motion.
  • Confirm the visitor can state what the product controls, what requires approval, and how rollback works.
  • Remove unsupported security, availability, performance, accessibility, or compliance claims.

Avoid

  • Magical automation claims with no permissions or exception model.
  • Fake dashboards, metrics, customers, badges, or case-study outcomes.
  • Glowing AI orbs, particle clouds, and cyberpunk decoration.
  • A generic sales leap from feature list to contact form.
  • Hiding risk, audit, failure, or rollback details behind vague copy.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Operational Enterprise Ai AI skill do?

Create or redesign enterprise AI, automation, security, and operations product pages that explain system boundaries, approvals, auditability, exceptions, and rollback. Use for dark cinematic heroes, hairline grids, metric pauses, expandable solution rows, case-study evidence, security proof, and qualified demo or waitlist handoffs.

Why use Operational Enterprise Ai on TypingMind?

Because you install it once and use it with any model. Operational Enterprise Ai 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 Operational Enterprise Ai in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boraoztunc/skills/tree/main/operational-enterprise-ai. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Operational Enterprise Ai?

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 Operational Enterprise Ai?

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

Is the Operational Enterprise Ai AI skill free?

Yes. It is published on GitHub by boraoztunc under the Apache-2.0 license. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇