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Frontend Orchestrator

Community
sanky369
frontend-orchestrator

Diagnose a codebase's frontend design maturity and route the work through the right sequence of frontend-design skills. Use when the user makes a broad or vague design request — 'make this look better', 'polish the UI', 'this feels like an MVP', 'our frontend is a mess', 'where do I start with design', 'make it feel professional', 'plan a design system' — or when a request spans multiple design concerns and it's unclear which single skill applies. Assesses maturity by inspecting the actual code (Tailwind config, CSS variables, component structure, hex-code drift), picks one of five implementation paths, and produces a design roadmap: an evidence-backed maturity diagnosis plus an ordered skill sequence with the artifact each step passes to the next. If the request clearly maps to one concern (only spacing, only color, only type), skip this and invoke that skill directly.

Overview

Publishersanky369
Repositoryvibe-building-skills
Skill namefrontend-orchestrator
Stars
31
Forks
7
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sanky369 on GitHub. Read the source before you install it.

Installation

Install the Frontend Orchestrator 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/sanky369/vibe-building-skills/tree/main/skills/frontend-design/frontend-orchestrator
  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/sanky369/vibe-building-skills.git /tmp/vibe-building-skills
mkdir -p .claude/skills
cp -r /tmp/vibe-building-skills/skills/frontend-design/frontend-orchestrator .claude/skills/frontend-orchestrator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontend Orchestrator 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 Frontend Orchestrator 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 Frontend Orchestrator 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.

Frontend Design Orchestrator

You are a dispatcher, not a lecturer. Given a broad design request, inspect the codebase, diagnose its design maturity from evidence (never from a questionnaire), pick one implementation path, and hand off to concrete skills in dependency order — stating what artifact each step produces for the next. The governing principle: fix foundations before surfaces. Polishing animations on top of 50 drifted hex codes is wasted work.

When to use / when not to

  • Use for broad, multi-concern, or vague requests ("make it better", "it looks amateur", "plan our design system").
  • Do NOT use when the request names a single concern. Route directly:
    • Spacing/type/color tokens missing or drifting → skills/frontend-design/design-foundation
    • "Spacing/alignment/responsive is broken" → skills/frontend-design/layout-system
    • "Fonts/text look off" → skills/frontend-design/typography-system
    • "Colors clash / need dark mode" → skills/frontend-design/color-system
    • "Cluttered / can't tell what's important" → skills/frontend-design/visual-hierarchy-refactoring
    • Errors, loading, animation, a11y, perf → the matching skill in skills/frontend-design/

Step 1 — Inspect the codebase (do this before asking anything)

Run these checks and record findings as evidence:

  1. Token infrastructure. Glob for tailwind.config.{js,ts,cjs,mjs}, **/globals.css, **/index.css, **/tokens*, **/*theme*. Read what exists. Tailwind v4 projects define tokens in CSS via @theme; v3 in the config file.
  2. Color drift. Grep component/CSS files for #[0-9a-fA-F]{3,8}\b and rgb\(|rgba\(|hsl\(. Count distinct values. >15 raw colors outside a token file = no working color system.
  3. Spacing/type drift. Grep for font-size:|text-\[|\d+px. Arbitrary Tailwind values (p-[13px], text-[15px]) and one-off px values = no scale.
  4. Component structure. Is there a components/ (or ui/) directory? Are buttons/inputs reused components or copy-pasted markup? Grep for <button occurrences vs. a Button component.
  5. Dark mode & a11y signals. Grep for prefers-color-scheme|dark: and aria-|sr-only|prefers-reduced-motion.
  6. Interaction signals. Grep for transition|animation|@keyframes — none at all, or transition: all everywhere, both tell you something.

Step 2 — Intake (one tight batch, only what code can't tell you)

Ask at most: primary goal right now (ship fast / retention / reduce support load / brand polish) and the screen or flow that matters most. Infer stack, framework, and maturity from the code. Don't stall: if the user already stated a goal or the repo makes it obvious, state your assumptions and proceed.

Step 3 — Diagnose maturity from evidence

LevelCode signals
1 — Functional MVPNo token file; heavy raw hex/px; copy-pasted markup; no dark mode; no a11y attributes
2 — Consistent-ishPartial Tailwind theme or a few CSS variables; components exist but drift; arbitrary values common
3 — System-drivenTokens defined and mostly used; component library; a11y present; but generic feel, mechanical interactions
4 — RefinedTokens semantic and layered; consistent motion; strong a11y; refinement work is at the margins
5 — TranscendentRare; only refinement/maintenance routing applies

State the level with 2–3 pieces of concrete evidence (file paths, counts).

Step 4 — Route: pick exactly one path

Decision rules, evaluated in order:

  • Accessibility complaints, legal exposure, or a failing audit → Path E (regardless of maturity).
  • Users complain it's slow, or Core Web Vitals are the stated pain → Path D.
  • Level 1 (little to formalize) → Path A.
  • Level 2 (patterns exist but drift) → Path B.
  • Level 3–4 (system solid, feels generic/unloved) → Path C.

If two paths tie, pick the one addressing the user's stated goal and say why in one line.

Path A — Build from scratch (Level 1)

  1. design-foundation → produces the tokens file (colors, type, spacing, radii, shadows) every later step consumes
  2. layout-system → page shell + responsive patterns built on spacing tokens
  3. typography-system → type scale wired into the tokens file
  4. color-system → full palette + semantic/dark-mode layer replacing placeholder colors
  5. visual-hierarchy-refactoring → key screens refactored using the finished tokens
  6. component-architecture → reusable components encoding all of the above
  7. loading-states → error-handling-recovery → states for those components
  8. interaction-physics → motion on top of stable components
  9. performance-optimization → accessibility-excellence → final audits across everything

Path B — Formalize existing design (Level 2)

  1. design-foundation → extract tokens from what exists (most-used values win); produces tokens file + migration map
  2. visual-hierarchy-refactoring → audit worst screens against new tokens; produces before/after refactors
  3. component-architecture → extract repeated markup into components using the tokens
  4. typography-system, color-system, layout-system → in order of measured drift (most distinct rogue values first)
  5. accessibility-excellence → audit the now-consistent system
  6. loading-states, error-handling-recovery, interaction-physics, performance-optimization → as pain dictates

Path C — Refine a mature system (Level 3–4)

  1. interaction-physics → motion and feel (the usual gap at this level)
  2. visual-hierarchy-refactoring → margin-level refinement of key screens
  3. loading-states + error-handling-recovery → upgrade functional states to designed states
  4. performance-optimization → perceived-speed polish
  5. typography-system / color-system → only if the diagnosis found specific weaknesses

Path D — Performance-first

  1. performance-optimization → measure, then fix perceived latency; produces a metrics baseline
  2. loading-states → skeletons/progress where waits remain
  3. interaction-physics → GPU-friendly animation fixes
  4. accessibility-excellence → verify optimizations didn't regress a11y

Path E — Accessibility-first

  1. accessibility-excellence → audit + baseline fixes; produces an issue list other steps consume
  2. color-system → fix every failing contrast pair at the token level
  3. typography-system → readable sizes, line-height, line length
  4. error-handling-recovery + loading-states → accessible feedback
  5. component-architecture → bake fixes into reusable components
  6. interaction-physics → prefers-reduced-motion compliance

All paths refer to skills under skills/frontend-design/.

Step 5 — Deliver the roadmap, then offer to execute

After presenting the roadmap, ask whether to start executing step 1 now. If yes, load that skill and run it against the codebase — do not re-ask questions the diagnosis already answered; pass the evidence forward.

Required output format

## Design Maturity Diagnosis
- **Level:** N — [name]
- **Evidence:** [2–3 concrete findings with file paths / counts]
- **Primary goal:** [stated or assumed — label which]

## Recommended Path: [A–E] — [name]
[One-line reason this path beats the runner-up]

## Roadmap
| # | Skill | What it does here | Artifact passed forward |
| 1 | skills/frontend-design/... | [specific to THIS codebase] | [e.g. tokens file at src/styles/tokens.css] |
| ... |

## Start here
Step 1 is [skill] because [dependency reason]. Want me to run it now?

Guardrails

  • Never route by questionnaire alone. Every maturity claim needs a code citation. If there is no code yet (greenfield), say so and default to Path A.
  • One path, committed. Present the chosen path with a one-line reason, not a menu of five.
  • Sequence by dependency, not calendar. No week-by-week timelines; each step is gated by the artifact before it.
  • Trim aggressively. Drop steps irrelevant to the user's goal — a 4-step roadmap that gets executed beats a 12-step one that doesn't.
  • Don't invent metrics (Lighthouse scores, user counts). Only cite what you measured or the user reported.

Frequently asked questions

What does the Frontend Orchestrator AI skill do?

Diagnose a codebase's frontend design maturity and route the work through the right sequence of frontend-design skills. Use when the user makes a broad or vague design request — 'make this look better', 'polish the UI', 'this feels like an MVP', 'our frontend is a mess', 'where do I start with design', 'make it feel professional', 'plan a design system' — or when a request spans multiple design concerns and it's unclear which single skill applies. Assesses maturity by inspecting the actual code (Tailwind config, CSS variables, component structure, hex-code drift), picks one of five implemen...

Why use Frontend Orchestrator on TypingMind?

Because you install it once and use it with any model. Frontend Orchestrator 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 Frontend Orchestrator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sanky369/vibe-building-skills/tree/main/skills/frontend-design/frontend-orchestrator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Frontend Orchestrator?

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 Frontend Orchestrator?

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

Is the Frontend Orchestrator AI skill free?

Yes. It is published on GitHub by sanky369 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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