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Design Import

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yonatangross
design-import

Scaffolds React components from a Claude Design handoff bundle and stops at files on disk: no stories, no tests, no pull request. Use when handed a claude.ai/design URL or a local bundle file; when that same scaffold should carry on through test generation, browser verification and an opened PR, run /ork:design-ship instead.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namedesign-import
Stars
298
Forks
35
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 yonatangross on GitHub. Read the source before you install it.

Installation

Install the Design Import 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/yonatangross/orchestkit/tree/main/plugins/ork/skills/design-import
  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/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/design-import .claude/skills/design-import
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Import 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 Import 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 Import 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 Import

Turn a Claude Design handoff bundle into scaffolded React components, with provenance and dedup against the existing codebase.

bash
design-import https://claude.ai/design/abc123      # From handoff URL
design-import /tmp/handoff-bundle.json             # From local file

When to use

After exporting a handoff bundle from claude.ai/design. This skill is the entry point — it does NOT open a PR, run tests, or deploy. For the end-to-end flow (import → tests → PR), use design-ship instead.

Pipeline

Handoff bundle (URL or file)
  │
  ▼
┌──────────────────────────────┐
│ 1. PARSE + VALIDATE          │  via claude-design-orchestrator agent
│    - Fetch bundle             │  Schema validation
│    - Compute bundle_id (sha)  │  Surface deviations
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ 2. RECONCILE TOKENS           │  Diff bundle tokens vs project tokens
│    - Read project tokens      │  Conflicts → AskUserQuestion
│    - Apply additions          │  Additions → write to design-tokens.json
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ 3. DEDUP COMPONENTS           │  For each proposed component:
│    Storybook MCP first        │   • exact match → reuse (skip)
│    21st.dev next              │   • similar match → adapt
│    Filesystem grep last       │   • no match → scaffold
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ 4. SCAFFOLD                   │  Delegate to design-to-code per component
│    (skipped components        │  Use bundle's tsx_scaffold as seed
│     logged but not touched)   │  Apply project tokens
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ 5. WRITE PROVENANCE           │  .claude/design-handoffs/<bundle_id>.json
│    Bundle → files → (PR)      │  PR field empty until design-ship
└──────────┬───────────────────┘
           │
           ▼
   Import manifest (stdout)

Argument resolution

python
ARG = "$1"  # First positional argument

if ARG.startswith("http://") or ARG.startswith("https://"):
    bundle_source = "url"
    bundle_input = ARG
elif Path(ARG).exists():
    bundle_source = "file"
    bundle_input = ARG
else:
    AskUserQuestion(questions=[{
      "question": "I couldn't resolve that as a URL or file. What is it?",
      "header": "Bundle source",
      "options": [
        {"label": "Paste handoff URL", "description": "claude.ai/design URL"},
        {"label": "Paste file path", "description": "Local handoff JSON"},
        {"label": "Cancel", "description": "Abort import"}
      ],
      "multiSelect": False
    }])

Phase 1 — Parse + validate

Delegate to the orchestrator agent. The agent fetches, extracts the tarball, reads the README + chats, parses the HTML prototypes, and produces a normalized payload. Do NOT reimplement parsing here — the agent owns the (real, tarball-based) schema.

python
Agent(
  subagent_type="ork:claude-design-orchestrator",
  description="Parse and normalize handoff bundle",
  prompt=f"""Parse the Claude Design handoff bundle at {bundle_input}.

  This is a gzipped tarball (NOT a JSON manifest). Layout:
    <project>/README.md          ← read first
    <project>/chats/*.md         ← read all (load-bearing)
    <project>/project/*.html     ← prototypes (may be absent if incomplete)

  Tasks:
  1. Fetch the bundle (WebFetch if URL → saved .bin path; Read if local file)
  2. Extract: `tar -xzf <bin> -C /tmp/<scratch>/`
  3. Read README.md, then every chats/*.md (intent + clarifications live here)
  4. Compute bundle_id = sha256(canonical bundle URL or absolute path)
  5. If project/ is MISSING → return status="incomplete" with the assistant's
     last unanswered question; do NOT crash. Surface "what user should do".
  6. If project/ exists → pick primary HTML:
     - Prefer the file matching the URL's ?open_file= query param
     - Else first alphabetical
  7. From the primary HTML, extract:
     - Inline `:root { --... }` CSS custom properties as design tokens
     - Component sections (named via class/id/data-screen-label)
     - Asset references (<link>, <img>) — keep as URLs, do not download
     - EDITMODE JSON block (design-time state — capture as ANNOTATION only)
  8. Produce normalized output payload (see agent spec)
  9. Write provenance to .claude/design-handoffs/<bundle_id>.json:
     - bundle_url, bundle_id, fetched_at, status, components: [], pr: null
  10. Return the normalized payload as JSON

  Surface any deviations from the expected tarball layout explicitly.
  Never expect a JSON `components[]` field — that was the old (wrong) shape.
  """
)

Phase 2 — Reconcile tokens

Read the normalized token_diff from the agent's payload.

Diff fieldAction
addedAppend to project's design-tokens.json (or Tailwind config). No prompt — additions are safe.
modifiedShow diff. AskUserQuestion: keep project value, accept bundle value, or open editor.
conflictsBlock scaffolding. AskUserQuestion to resolve before continuing.
python
if token_diff["conflicts"]:
    AskUserQuestion(questions=[{
      "question": f"Token conflict on {conflict.path}. Project says {conflict.project}, bundle says {conflict.bundle}. Resolve?",
      "header": "Token conflict",
      "options": [
        {"label": "Keep project value", "description": "Bundle adapts to project"},
        {"label": "Accept bundle value", "description": "Project adapts to bundle (writes new token)"},
        {"label": "Both — namespace bundle's", "description": f"Add as {conflict.path}.imported"}
      ],
      "multiSelect": False
    }])

Phase 3 — Dedup components

The agent already ran component-search per component. Read decisions from the normalized payload:

decisionBehavior
reuseLog "skipped (existing: )" — do nothing on disk
adaptPipe through ork:design-to-code with --adapt-from <existing-path> context
scaffoldPipe through ork:design-to-code with the bundle's tsx_scaffold as seed

Phase 4 — Scaffold

For each component with decision scaffold or adapt, invoke design-to-code. The spawns run in the background at the same time, so each one gets its own worktree (isolation="worktree", #4557): ork agents carry no frontmatter isolation, and without it every component writes into your tree at once.

python
for component in payload["components"]:
    if component["decision"] in ("scaffold", "adapt"):
        # Compose, don't reimplement — design-to-code owns the EXTRACT/MATCH/ADAPT/RENDER pipeline
        Agent(
          subagent_type="ork:frontend-ui-developer",
          name=f"scaffold-{component['name']}",
          description=f"Scaffold {component['name']} from bundle",
          prompt=f"""Use the design-to-code skill to scaffold this component.

          Source: handoff bundle {payload['bundle_id']}
          Component: {component['name']}
          Target path: {component['target_path']}
          Bundle scaffold seed:
          ```tsx
          {component['tsx_scaffold']}
          ```
          Resolved tokens: {component['tokens_resolved']}
          Decision: {component['decision']}
          {f"Adapt from: {component['existing_match']}" if component['decision'] == 'adapt' else ''}

          Write the component, mirror existing project file structure, use project tokens.
          Commit it in your worktree before you return, and end your reply with
          your branch name and commit sha. Uncommitted files are not merged.
          """,
          run_in_background=True,
          isolation="worktree",
          max_turns=25,
        )

Collect and merge the component worktrees

Wait for every scaffold agent to return before Phase 5. For each one:

  1. Check its worktree is clean: git -C <worktree> status --porcelain prints nothing. If it prints files, commit them there and note it in the report.
  2. Merge its branch into the current branch. A conflict means two components wrote the same file: stop and report both paths.
  3. Confirm component['target_path'] exists after the merge. A component whose agent failed or wrote elsewhere goes into failed_components: it is reported, not recorded as written.

Phase 5 — Provenance

Update the provenance file with the actual file paths written:

python
provenance = Read(payload["provenance_path"])
provenance["components"] = [
    {"name": c["name"], "decision": c["decision"], "path": c["target_path"]}
    for c in payload["components"]
    if c["name"] not in failed_components  # from "Collect and merge" step 3
]
provenance["imported_at"] = now()
Write(payload["provenance_path"], provenance)

Output — import manifest

Print a concise summary (not a wall of JSON):

Imported bundle <bundle_id>
  Source: <bundle_url>
  Provenance: .claude/design-handoffs/<bundle_id>.json

Components:
  ✓ PricingCard          scaffold  src/components/pricing/PricingCard.tsx
  ↻ Button               reuse     existing: src/components/ui/Button.tsx
  ⤳ Hero                 adapt     adapted from: src/components/Hero.tsx

Tokens:
  + 3 new (added to design-tokens.json)
  ~ 1 modified (user accepted bundle value)
  ✗ 0 conflicts unresolved

Next: design-ship <bundle_id>   # to open PR
      expect                     # to verify

Hooks

  • After completion, the post-design-import hook recommends expect (non-blocking, see hook for details).

Composition

SkillRole
design-to-codeOwns the actual scaffold pipeline (EXTRACT/MATCH/ADAPT/RENDER). This skill delegates to it per component.
component-searchUsed by the orchestrator agent for dedup
design-context-extractUsed if bundle is missing design_tokens block
design-system-tokensToken reconciliation reference
remember / memoryProvenance + prior-import detection

NOT this skill's job

ConcernOwned by
Open PRdesign-ship
Run testspost-design-import hook → expect
Generate Storybook storiescover (called by design-ship)
Re-prompt Claude DesignNot yet — no public API

Limitations

  • No public Claude Design API yet: bundles are one-shot exports. To iterate, re-export from claude.ai/design and re-import. The reverse-path (drift detection + refinement prompt) is tracked in M124 — Claude Design Drift Sync (Bet B): #1391, #1393, #1394, #1395, #1397.
  • Schema is provisional: Claude Design has not published a stable bundle schema. The orchestrator agent adapts to deviations but may need updates as the format stabilizes.
  • Asset URLs are referenced, not downloaded: bundle asset_urls are kept as-is. If you need them in-repo, run a separate sync step.

Frequently asked questions

What does the Design Import AI skill do?

Scaffolds React components from a Claude Design handoff bundle and stops at files on disk: no stories, no tests, no pull request. Use when handed a claude.ai/design URL or a local bundle file; when that same scaffold should carry on through test generation, browser verification and an opened PR, run /ork:design-ship instead.

Why use Design Import on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/design-import. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Design Import?

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 Import?

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

Is the Design Import AI skill free?

Yes. It is published on GitHub by yonatangross 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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