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Ds Ai Rails

Community
bradfrost
ds-ai-rails

Assess, set up, and verify how an AI development environment uses an organization's actual design system. Use when someone wants to keep AI on their system's rails, connect system knowledge and tools to coding agents, establish reuse and gap policies, or check whether those guardrails work. Focuses on the workflow and its evidence; ordinary page building or a full system inspection alone does not require this skill.

Overview

Publisherbradfrost
Repositoryskills
Skill nameds-ai-rails
Stars
51
Forks
1
Bundled files
9
LicenseMIT
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.

  • 9 bundled files

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

  • Open source

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

Installation

Install the Ds Ai Rails 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/bradfrost/skills/tree/main/skills/design-systems/ds-ai-rails
  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/bradfrost/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/design-systems/ds-ai-rails .claude/skills/ds-ai-rails
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ds Ai Rails 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 Ds Ai Rails 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 Ds Ai Rails 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.

Keep AI on the rails of the design system

Your team has invested in components, tokens, accessibility, documentation, and ways of working. Help people put that investment to work! Establish a usable connection between their design system and the AI tools building websites, then show what that connection can actually be trusted to do.

Use the public name above consistently. Present the work directly in second person or neutral language; avoid first-person assistant narration. Keep the purpose clear: help AI use the sturdy foundations of the actual design system instead of hallucinating UI.

Work with the organization's ecosystem and vocabulary. Recipes, patterns, modules, templates, and pages can live in different places under different owners. No particular framework, MCP, vendor, package layout, or other skill is required.

Use the user-facing workflow for the approved presentation copy. Read the relevant stages as the work progresses. Ask only unanswered questions, show only applicable branches, and fill every finding from evidence. The procedural references below govern what to inspect and do.

Choose the work

Infer the mode from the request and existing context; ask only when the answer changes what you should do.

ModeWhat you doWhere it ends
AssessInspect the available environment and propose ordered improvements. Default for readiness questions.Evidence, a practical plan, and unresolved team decisions. No settings or product changes.
Set upAssess enough to make the requested changes, then prepare or apply scoped instructions, configuration, and checks.Reviewable changes, activation status, and verification within available access.
VerifyExercise an existing setup or recheck it after changes.Observed results, failures, interventions, and exact limits of the claim.

Start with one system, one consuming project or sanctioned sandbox, and one AI environment unless the user specifies a wider scope. Distinguish open exploration, a prototype using the system, and production work. Record what may diverge or be mocked and what must be revisited before promotion. A legacy page change does not authorize migrating the whole product.

Establish the context

Read accessible project instructions, system profiles, manifests, catalogs, and tool settings before interviewing the user. Reuse maintained profiles rather than creating a competing source. Ask only for missing information that changes the work: the user need, relevant system and environment, required reuse, permitted extension paths, ownership, and access.

For an assessment or new setup, read Discovery. For verification, refresh the relevant discovery evidence instead of repeating a whole assessment. Record sources and versions; an old successful connection does not prove the current one works.

During assessment, use the techniques review to connect observed weaknesses to relevant approaches from the State of AI in Design Systems study. Select what fits the team; technique counts are not a readiness score. For a conversational dry run, walk through the user-facing exchange without implying that an environment was inspected.

Keep these distinctions throughout the run:

  • Knowledge: which assets and guidance exist, who maintains them, and what version the project actually consumes.
  • Policy: what the organization wants agents to do, including where they may compose, extend, or experiment.
  • Enforcement: what observed mechanisms detect or block a violation on each surface. Instructions alone are advisory.
  • Quality: whether the resulting experience works for its users. Compliant imports do not prove good design or working behavior.

Establish a working policy

Read Policy and gap routing before proposing a reuse policy or deciding what to do with an uncovered need.

This work often surfaces decisions a team has never made explicitly. Acknowledge that! Help people make a scoped working choice when they have the authority, while recording what still needs team discussion. A pilot agreement is not automatically organization-wide policy.

Keep assessment and covered work moving. When an unresolved decision changes whether new UI is allowed, present concrete options and pause that portion until an authorized route exists. A system gap never grants blanket permission to switch stacks or invent a replacement system.

Connect and exercise the workflow

For setup work, read Setup. Adapt the following behavior to the existing delivery process, rather than installing a new process around it:

  1. Load the applicable rules and confirm access to authoritative sources.
  2. Map the requested experience to real components, tokens, and suitable compositions. Verify APIs and usage guidance for the consumed version.
  3. Route uncovered needs under the working policy.
  4. Build with the actual dependencies and permitted extension points.
  5. Run relevant code, behavior, accessibility, and rendered-result checks.
  6. Present provenance, results, limitations, and exceptions for review.
  7. Follow the existing release process and route product learning upstream.

For a requested pilot or verification, read Verification. Use a small representative task with a meaningful interaction and an uncovered requirement. Test the checks as well as the output. Retain human judgment of the experience.

Keep the claims honest

  • Verify real APIs and rendered implementation where possible. A screenshot, design file, unused import, or agent's self-report does not prove code reuse.
  • Attribute evidence as verified, reported, or unknown, with location, version/revision, date, and scope. Give findings a status of working, partial, missing, not inspected, or not applicable. Avoid a single readiness percentage that hides an important gap.
  • Label each control advisory, detecting, blocking, or unverified. Name the exact path it covers and any known bypasses.
  • A blocked capability limits dependent work and its claims. Continue the useful independent work; use a fallback only when policy permits it and it supplies adequate evidence for the relevant version.
  • Preserve existing permissions. A request to assess is not permission to change settings. A request to set up can authorize scoped local changes; it does not grant credentials, broaden organizational policy, or authorize messages, publication, or changes to unrelated projects.
  • Separate a prepared change from an activated one, a draft issue from a filed one, and a released system fix from its adoption in the product.

Leave something the team can use

Read Artifacts when producing the outputs. Prefer the team's existing documentation and tracker; otherwise use a ds-ai-rails/ directory in the agreed project. In plain chat, provide the same records as saveable text and state what you could not inspect or run.

Assessment produces a profile and a prioritized plan. Setup adds concrete diffs, activation/rollback notes, and available verification. Verification produces a record of what was actually exercised. Include a short decision list with scoped working choices, unresolved questions, owners, and review triggers; do not create a parallel backlog when one already exists.

End with what works, what remains unverified or blocked, and the specific team decisions needed for this work. Explain the most useful next action without implying that the whole organization must be redesigned first.

This skill establishes an AI-assisted website workflow. Full system health inspections, product migrations, native rendering, runtime generative UI, and organization-wide monitoring are separate work. Reuse evidence from other skills when available; none is a prerequisite for completing this one.

Related course lessons

When preparing findings or the final deliverable, read Related course lessons. Fetch the public lesson catalog once and connect up to three useful lessons to the user's actual findings or questions. Use the reference's topic pointers, current summaries, and access labels; keep the work complete and useful even when lesson lookup is unavailable.

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 Ds Ai Rails AI skill do?

Assess, set up, and verify how an AI development environment uses an organization's actual design system. Use when someone wants to keep AI on their system's rails, connect system knowledge and tools to coding agents, establish reuse and gap policies, or check whether those guardrails work. Focuses on the workflow and its evidence; ordinary page building or a full system inspection alone does not require this skill.

Why use Ds Ai Rails on TypingMind?

Because you install it once and use it with any model. Ds Ai Rails 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 Ds Ai Rails in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradfrost/skills/tree/main/skills/design-systems/ds-ai-rails. 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 Ds Ai Rails?

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 Ds Ai Rails?

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

Is the Ds Ai Rails AI skill free?

Yes. It is published on GitHub by bradfrost under the MIT 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.

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