// Upload local assets (images, mockups, extracted HTML) to a Stitch project. ALWAYS use this skill when you need to upload visual assets or full HTML pages to Stitch, particularly when direct MCP tool calls fail or truncate due to base64 token limits.
Converts Stitch designs into modular Vite and React components using system-level networking and AST-based validation.
Extract self-contained static HTML from a built web application or React components by inlining CSS and images. Use this skill whenever you need to capture a specific UI state, share a static version of a page, or prepare assets for Stitch upload, even if the user just asks to 'save the HTML' or 'mock the view'.
Convert frontend code (Vite, React, etc.) to a Stitch Design by chaining static HTML extraction, design system extraction, and file upload. **ALWAYS** use this skill when the user's intent is to move existing web apps or React components into Stitch (e.g., requests to "save", "migrate", or "upload"). You must use this skill even for simple "save" operations, as it is the only way to ensure the design system is extracted and assets are properly linked.
Manage design systems in Stitch using MCP tools. Includes retrieval of assets, creating/updating design systems in Stitch, and applying them to screens.
Extract a comprehensive design system (DESIGN.md) directly from frontend source code — React, Vue, Svelte, Angular, plain HTML/CSS, or any web framework. Analyzes component files, stylesheets, Tailwind configs, theme definitions, and design tokens to produce a rich, Stitch-compatible design system document. Use this skill whenever the user wants to reverse-engineer a design system from an existing codebase, audit the visual language of a project, extract design tokens from source files, or understand the styling patterns in a frontend repo — even if they just say "what does this app look like?" or "pull out the design from this code."
Generate new screens from text prompts or images, edit existing screens with prompts and design system tokens, and generate design variants using Stitch MCP. Includes prompt enhancement pipeline, design mappings, professional UI/UX terminology, design tokens and theme system capabilities.
| name | stitch::upload-to-stitch |
| description | Upload local assets (images, mockups, extracted HTML) to a Stitch project. ALWAYS use this skill when you need to upload visual assets or full HTML pages to Stitch, particularly when direct MCP tool calls fail or truncate due to base64 token limits. |
| allowed-tools | ["stitch*:*","Bash","Read","Write","web_fetch"] |
Upload local assets (images, mockups, HTML files) to a Stitch project using the provided upload script, which bypasses the MCP tool's base64 output token limits.
[!NOTE] The AI model cannot upload files via MCP tools directly because the base64 encoding of even a small file exceeds the model's output token limit (~16K tokens). This script reads the file and sends it directly over HTTP.
Use list_projects to find the correct projectId.
Locate your active MCP server configuration file and extract the API key:
.gemini/antigravity/mcp_config.json or .gemini/jetski/mcp_config.json~/.gemini/settings.json or ~/.gemini/extensions/Stitch/gemini-extension.json~/.claude.jsonExtract:
X-Goog-Api-Key header or auth argumenthttpUrl or endpoint argument (defaults to
https://stitch.googleapis.com)[!IMPORTANT] If you cannot find the API key in any of these locations, or if you cannot access these files, you MUST ask the user to provide the Stitch API key. Do not proceed without a valid API key.
[!WARNING] Checkpoint — User Confirmation Required. Before running the upload script, you MUST pause and present the file(s) to be uploaded (paths, sizes, and types) to the user and wait for explicit approval. Do NOT execute the upload script until the user confirms.
Use run_command to execute the Python script:
python3 <SKILL_DIR>/scripts/upload_to_stitch.py \
--project-id <PROJECT_ID> \
--file-path <PATH_TO_FILE> \
--api-key <API_KEY> \
[--api-url <STITCH_API_URL>] \
[--title <SCREEN_TITLE>]
[!TIP] macOS / SSL Certificate Troubleshooting: If the upload fails with
ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] unable to get local issuer certificate, this means your Python installation does not have root certificate authorities configured. Fix it by prependingSSL_CERT_FILEusing thecertifibundle:SSL_CERT_FILE=$(python3 -c "import certifi; print(certifi.where())") python3 <SKILL_DIR>/scripts/upload_to_stitch.py \ --project-id <PROJECT_ID> \ --file-path <PATH_TO_FILE> \ --api-key <API_KEY>
| Extension | MIME Type |
|---|---|
.png | image/png |
.jpg, .jpeg | image/jpeg |
.webp | image/webp |
.html, .htm | text/html |
The script auto-detects MIME type from the file extension.
--api-url defaults to https://stitch.googleapis.com--api-key is requiredscreenType is set to DOCUMENT; for images, IMAGE