agentleFS
Sign inStart free
HK-hubHK-hub/AgentSkills/stitch-upload-to-stitch/SKILL.md

stitch::upload-to-stitch

Skill6 starsChanged 9 months agoMITView on GitHub

Upload local assets (images, mockups, extracted HTML, design markdown) to a Stitch project. ALWAYS use this skill when you need to upload visual assets, HTML pages, or design docs to Stitch, particularly when direct MCP tool calls fail or truncate due to base64 token limits.

  • Reads credentials
  • Installs packages

2 other projects use a version of this, 2 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use list_projects to find the correct projectId.

    line 30
  2. Very specific:

    Use run_command to execute the Python script:

    line 55

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Upload-to-Stitch

Upload local assets (images, mockups, HTML, and markdown 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.

Steps

1. Identify Target Project

Use list_projects to find the correct projectId.

2. Get the API Key

Locate your active MCP server configuration file and extract the API key:

  • Antigravity: .gemini/antigravity/mcp_config.json or .gemini/jetski/mcp_config.json
  • Gemini CLI: ~/.gemini/settings.json or ~/.gemini/extensions/Stitch/gemini-extension.json
  • Claude Code: ~/.claude.json

Extract:

  • API Key: From the X-Goog-Api-Key header or auth argument
  • MCP URL (optional): From the httpUrl 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.

3. Run Upload Script

[!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>] \
  [--generated-by <GENERATED_BY>]

[!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.

The script automatically attempts to use the certifi package to load the CA bundle if it is installed in your python environment. If certifi is not installed, you can either install it (pip install certifi) or manually supply the SSL_CERT_FILE environment variable when running the script:

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> \
  [--api-url <STITCH_API_URL>] \
  [--title <SCREEN_TITLE>] \
  [--generated-by <GENERATED_BY>]

Supported File Types

ExtensionMIME Type
.pngimage/png
.jpg, .jpegimage/jpeg
.webpimage/webp
.html, .htmtext/html
.mdtext/markdown

The script auto-detects MIME type from the file extension.

Script Options

  • --project-id: Required. The Stitch project ID.
  • --file-path: Required. Path to the local file to upload.
  • --api-key: Required. API key for Stitch authorization.
  • --api-url: Optional. Base URL of the Stitch API. Defaults to https://stitch.googleapis.com.
  • --title: Optional. Title for the uploaded screen.
  • --generated-by: Optional. Specify how the uploaded file was generated (e.g., 'stitch::extract-static-html' skill, 'Claude Code', 'Codex', 'Gemini' etc.).

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free