>- 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.
npx skills add https://github.com/google-labs-code/stitch-skills --skill stitch::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.
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 tohttps://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>] \
[--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:
> `bash
> 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>]
> `
| Extension | MIME Type |
|:---|:---|
| .png | image/png |
| .jpg, .jpeg | image/jpeg |
| .webp | image/webp |
| .html, .htm | text/html |
| .md | text/markdown |
The script auto-detects MIME type from the file extension.
--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. When uploading extracted HTML from a web app, set this to the route path of the page (e.g., '/dashboard', '/settings/profile', '/inbox') so that the screen name/title in Stitch clearly identifies the route.--generated-by: Optional. Specify how the uploaded file was generated (e.g., 'stitch::extract-static-html' skill, 'Claude Code', 'Codex', 'Gemini' etc.).agent-im 会话技能 - 通过 liblib.tv 的 AI 能力生成和编辑图片/视频。覆盖场景包括:生成(文生图、文生视频、图生视频、做动画、画一个xxx、来段xxx)、编辑修改(把xxx换成yyy、去掉xxx、加上xxx、改成xxx、调整xxx、局部修改、改镜头)、风格转换(风格迁移、转绘、换风格)、视频续写延长、复刻视频/TVC/宣传片、短剧/短漫剧生成、音乐MV生成、产品广告/展示片制作、分镜/故事板设计、教育视频/短视频制作。当用户提到 liblib、libtv、上传参考图/视频、查看生成进度时也应触发。关键判断:只要用户的请求涉及 AI 图片或视频的创作、生成、编辑、修改,无论措辞如何(如"画只猫"、"做个海报"、"把纸船换成爱心"、"这个视频帮我改一下"、"帮我复刻这段视频"、"用这首歌做个MV"、"一句话生成短剧"),都必须触发此技能。
This skill should be used when the user asks to "generate video prompts", "create Seedance prompts", "write video descriptions", mentions "Seedance", "seedance", "即梦", "即梦平台", "视频提示词", "视频生成", "AI视频", "短剧", "广告视频", "视频延长", or discusses video prompt engineering, AI video generation, or Seedance 2.0 workflows.
Best practices and techniques for writing effective AI video generation prompts. Covers: Veo, Seedance, Wan, Grok, Kling, Runway, Pika, Sora prompting strategies. Learn: shot types, camera movements, lighting, pacing, style keywords, negative prompts. Use for: improving video quality, getting consistent results, professional video prompts. Triggers: video prompt, how to prompt video, veo prompts, video generation tips, better ai video, video prompt engineering, video prompt guide, video prompt template, ai video tips, video prompt best practices, video prompt examples, cinematography prompts
This skill is a practical, 'use-it-while-debugging' reference for getting a LiveKit + Letta voice agent working reliably.
Download screenshot baselines from the latest CI run and commit them. Use when asked to update, accept, or refresh component screenshot baselines from CI, or after the screenshot-test GitHub Action reports differences. This skill should be run as a subagent.
| Turn vague taste, screenshots, URLs, product notes, or "make it feel like this" references into a grounded DESIGN.md plus an implementation handoff. Use it before prototypes, decks, redesigns, or image remix work when the user needs a reusable visual direction rather than a one-off prompt.
This skill helps users automatically extract channel-level and video detail data from a specific YouTube channel via BrowserAct API. Agent should proactively apply this skill when users express needs like extracting channel video data, getting latest or popular videos from a YouTube channel, tracking competitor channel content, extracting video metrics such as views likes comments, retrieving subscriber count and channel info, monitoring posting cadence of a YouTube channel, gathering video data for content strategy analysis, getting earliest videos of a YouTube creator, analyzing engagement signals across a full channel, and downloading structured YouTube video details without manual scraping.
| Environment diagnostic skill for SenseNova-Skills project. Checks that sn-image-base is properly installed and configured, validates dependencies and environment variables. Prompts user to configure missing required variables and saves them to .env file. After configuration, reloads environment and suggests agent restart if needed.
Take google-labs-code/stitch::upload-to-stitch from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.