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Design Frameworks

Community
glebis
design-frameworks

Fit an already-decided interface to an existing design system or UI toolchain through a capability-probed preset, using the system's own CLI, registry, MCP server, manifests, tokens, and checks instead of inventing parallel conventions. Use after the product job, evidence, concept, and prototype are settled enough to implement, or when bringing an existing build back into framework compliance. Not for choosing the product concept, visual brand, or research method. Triggers on design frameworks, design system adapter, use Astryx, use shadcn, use Storybook, fit this to our component library, existing design system, component registry, framework compliance, agent-ready design system, "implement with our design system".

Overview

Publisherglebis
Repositoryhumane-agentic-design
Skill namedesign-frameworks
Stars
38
Forks
3
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 glebis on GitHub. Read the source before you install it.

Installation

Install the Design Frameworks 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/glebis/humane-agentic-design/tree/main/humane/skills/design-frameworks
  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/glebis/humane-agentic-design.git /tmp/humane-agentic-design
mkdir -p .claude/skills
cp -r /tmp/humane-agentic-design/humane/skills/design-frameworks .claude/skills/design-frameworks
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Frameworks 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 Design Frameworks 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 Design Frameworks 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.

Design Frameworks

Announce at start: "I'm using the humane:design-frameworks skill to fit this implementation to the project's existing design system without changing the product decision above it."

This skill is an implementation adapter. It translates a settled-enough design decision into the components, patterns, tokens, themes, templates, and checks an existing system actually provides. It does not decide what the product should be.

The Humane method stays above every preset:

job → evidence → concept → prototype → review → learning

A preset may report that the system cannot express the accepted concept. It may not silently alter the job, acceptance criteria, content hierarchy, interaction model, or prototype to make the available components look sufficient.

Quick Reference

NeedRead
Preset profiles, selection, and fallback behaviorreferences/presets.md
Common objects, capability rules, fit-plan and finding shapesreferences/contract.md
Astryx native CLI/API commands and write boundariesreferences/astryx.md
shadcn registry, CLI/MCP, and source-ownership boundariesreferences/shadcn.md
Storybook manifests/MCP as a project-local catalog and test overlayreferences/storybook.md

Read only the selected preset reference. Read contract.md when producing a machine-readable manifest, fit plan, operation receipt, or validation result.

1. Establish the implementation brief

Before touching the framework, name the immutable inputs you are fitting:

  • the job or outcome reference;
  • the evidence references supporting it;
  • the accepted concept and acceptance criteria;
  • the prototype or existing implementation being fitted;
  • unresolved decisions and review findings that remain open.

References are enough; do not copy the whole Humane corpus into an adapter object. If these inputs do not exist, ordinary framework help can still proceed, but label Humane traceability Not available. Never invent research or claim that a framework-compliant build has validated the product idea.

2. Select, then verify, a preset

The user's explicit preset wins. Otherwise inspect the project read-only: package.json, lockfile, framework config, component directories, token files, Storybook config, MCP configuration, and installed local binaries. Use the selection table in presets.md.

A preset name is a bootstrap hint, not proof. Probe the native surface and record what this version and project actually expose. If the probe contradicts the preset reference, the probe wins and the difference is a limitation. Never guess a capability from the system's brand or from a remembered release.

Prefer authoritative surfaces in this order when several expose the same fact:

  1. a project-local machine manifest or documented programmatic API;
  2. typed JSON from the system's installed CLI;
  3. a schema-governed registry or generated project manifest;
  4. negotiated MCP tools and resources;
  5. installed package types and authored project documentation;
  6. remote prose documentation, as a cited fallback only.

Do not download a CLI, start a server, connect a remote registry, or install an MCP server merely to probe. Those are separate network or installation actions and need the user's approval.

3. Discover before composing

Build a small catalog for the requested scope, not a dump of the whole system. Inspect the native IDs, import paths, props, examples, variants, dependencies, token requirements, theme behavior, and ownership model of likely resources.

Keep provenance attached. A normalized Button without its package, registry, version, or source locator is not safe evidence. Preserve native fields the adapter does not understand; for DTCG tokens, never infer type or purpose from a group name and never discard unknown extensions.

4. Produce a fit plan before a patch

Map the implementation brief to native resources and return:

  1. the selected components, patterns, templates, themes, and tokens;
  2. how each choice serves an acceptance criterion or prototype behavior;
  3. proposed commands and file operations, with exact targets;
  4. conflicts and deviations, including capabilities the framework lacks;
  5. the native checks that will run after implementation;
  6. anything that remains Not verified or Not supported.

Planning is read-only. If a native preview or diff exists, use it. Otherwise the operation is not safely applicable through this skill until an exact preview can be produced another way.

5. Apply only an approved preview

Show the preview and ask before the first write. Approval covers only the shown operations. Re-plan if a command resolves different files, dependencies, environment variables, configuration, or migrations at execution time.

Always use the framework's supported CLI or public API for framework-owned changes. Ordinary application source that composes public framework components may use the project's normal editing path, but only after an exact file diff is part of the approved preview. Do not reproduce the framework's installer, registry resolver, codemod, or theme compiler in this skill. Capture the native receipt or the resulting diff.

These operations are never implicit: dependency installation, network fetches, --force/overwrite, eject/swizzle, codemods or migrations, theme replacement, agent-instruction edits, lockfile changes, generated-file writes, and deletion.

6. Validate in two lanes

Framework compliance and Humane validity are different claims.

Framework lane: run only checks the discovered system actually provides — schema validation, doctor/integration checks, type generation and type-checking, builds, story interaction tests, accessibility checks, token/reference checks, or project tests. Record the command/tool and evidence.

Humane lane: trace the result back to the implementation brief. Confirm that the accepted job, concept, hierarchy, interactions, and prototype behavior did not drift during adaptation. Product review and learning still belong to their own Humane owners; this lane checks traceability, not whether the product idea was good.

Use pass, fail, warn, not-run, or not-supported. Unsupported and unrun checks are never passes. A clean build does not prove usability, and a successful review does not prove framework compliance.

Never override

  • Humane corpus, evidence, concept, prototype notes, review findings, or learning artifacts;
  • accepted product intent, information hierarchy, interaction model, or copy;
  • project tokens, themes, framework config, agent instructions, or lockfiles;
  • source-owned, ejected, swizzled, or manually modified components;
  • generated files or unrelated source;
  • secrets, credentials, registry tokens, or environment values;
  • unknown registry metadata or DTCG extensions;
  • an accessibility requirement to make a preset fit.

When the framework and the implementation brief conflict, stop at the fit plan and name the decision the user must make. The adapter does not get a tie-breaker.

Output

For read-only discovery, return Preset, Observed capabilities, Resources inspected, Limitations, and Next safe action.

For implementation, return the Fit plan, the approved Operation receipts, and Validation split into Framework and Humane lanes. State every write and every check not run. Then offer the declared handoff to humane:review; do not run it automatically.

Use these fit-plan headings so a human can approve the boundary without reading the contract JSON: Brief refs, Preset and observed version, Selected native resources, Criterion mapping, Exact proposed operations, Conflicts and deviations, Checks after apply, Not verified, and Approval needed.

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 Design Frameworks AI skill do?

Fit an already-decided interface to an existing design system or UI toolchain through a capability-probed preset, using the system's own CLI, registry, MCP server, manifests, tokens, and checks instead of inventing parallel conventions. Use after the product job, evidence, concept, and prototype are settled enough to implement, or when bringing an existing build back into framework compliance. Not for choosing the product concept, visual brand, or research method. Triggers on design frameworks, design system adapter, use Astryx, use shadcn, use Storybook, fit this to our component library,...

Why use Design Frameworks on TypingMind?

Because you install it once and use it with any model. Design Frameworks 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 Design Frameworks in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/glebis/humane-agentic-design/tree/main/humane/skills/design-frameworks. 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 Design Frameworks?

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 Design Frameworks?

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

Is the Design Frameworks AI skill free?

Yes. It is published on GitHub by glebis 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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