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operational-enterprise-ai — Claude Code skill: enterprise product

A skill for building or reworking enterprise product pages: system boundaries, approvals, audit, exceptions and rollback.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add MengTo/Skills --skill operational-enterprise-ai -a claude-code
Open on GitHub

What operational-enterprise-ai does

The skill lays out a page structure for enterprise AI, automation, security and operations products. It walks through the story in order: the operational problem with one restrained product image or system trace, a white interlude with verified metrics, then each capability explained as a workflow with permissions and controls. Security, governance, exceptions and rollback come before the conversion ask.

Inside are the visual system and layout rules. Near-black, warm white, muted gray and one restrained spectral accent. A high-x-height sans-serif paired with compact mono labels and tabular numerals. Hard grid lines, square media, low radii. Solution rows keep a stable data model: summary, permissions, action, approval, output, audit, exception, rollback. Motion defaults are 160–220ms for controls and 500–760ms for section entrances, with parallax under 5%.

The skill tells you to replace source brands, customers, numbers, badges and screenshots, and not to invent compliance or performance evidence. So the sample metrics and case studies in the template are not something you can ship as-is — swap in your own verified ones. There is a validation pass too: test solution rows, filters, forms and error states with keyboard and touch, check diagram text alternatives, contrast, 200% zoom and reduced motion.

What it helps with

  • explain where the system's capabilities stop
  • show who approves actions and how they reach the audit log
  • describe exceptions and rollback before the demo form
  • build solution rows with permissions, output and audit
  • separate verified case-study facts from marketing interpretation

Example prompts for the agent

  • build an enterprise automation product page with the operational-enterprise-ai skill
  • rework the hero and metrics block: state the system boundary and use verified numbers
  • add solution rows covering approval, audit and rollback

How to install operational-enterprise-ai

1Skills CLI (Vercel)

npx skills add MengTo/Skills --skill operational-enterprise-ai -a claude-code

2Manually: put the files in place

~/.claude/skills/operational-enterprise-ai/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/operational-enterprise-ai/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about operational-enterprise-ai

How do I install operational-enterprise-ai in Claude?

Run npx skills add MengTo/Skills --skill operational-enterprise-ai -a claude-code. The skill lands at ~/.claude/skills/operational-enterprise-ai/SKILL.md.

What does operational-enterprise-ai do?

It helps create or redesign an enterprise product page: system boundaries, approvals, auditability, exceptions, rollback, security proof and a qualified demo or waitlist handoff.

Can I keep the template's metrics and case studies?

No. The skill requires replacing source brands, customers, numbers, badges and screenshots, and forbids inventing compliance or performance evidence.

Source text

SKILL.md · MIT
---
name: operational-enterprise-ai
description: 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.
---

# 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.

© MengTo · MIT · MengTo/Skills

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