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Found 486 Skills
Optimize token usage when delegating to Gemini CLI. Covers token caching, batch queries, model selection (Flash vs Pro), and cost tracking. Use when planning bulk Gemini operations.
Cross fact-checking with 4 models: Claude + Gemini + Codex. Conduct independent checks with Opus itself, Gemini Flash, Gemini Pro, and Codex (gpt-5.3-codex) → extract issues → discuss → output a consensus report.
Guides the usage of Gemini API on Google Cloud Vertex AI with the Gen AI SDK. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.
通过逆向工程的 Gemini Web API 生成图片和文本。支持文本生成、提示词生图、参考图片视觉输入和多轮对话。当其他技能需要图片生成后端，或用户要求"用Gemini生成图片"、"Gemini文本生成"时使用。
Ask Gemini via local CLI and capture a reusable artifact
App Store screenshot generation skill with two workflows: (A) AI-powered: fetches app metadata via `asc` CLI, analyzes screenshots with Claude vision, writes a ScreenPlan JSON, then generates final marketing screenshots via Gemini (`asc app-shots generate`), and optionally translates them (`asc app-shots translate`). (B) HTML-based (deterministic): writes a CompositionPlan JSON with precise device placement, text overlays, and backgrounds, then runs `asc app-shots html` to produce a self-contained HTML page with real device mockup frames and client-side PNG export — no AI needed. Use this skill when: (1) User asks to "create App Store screenshots" or "generate screenshot plan" (2) User asks to "make an HTML screenshot page" or "compose screenshots with mockups" (3) User mentions "asc-app-shots", "app-shots html", "composition plan", or screenshot marketing (4) User wants deterministic, reproducible screenshot layouts with device mockups (5) User wants AI-generated screenshots via Gemini
Use when generating images with Gemini models, choosing between Nano Banana 1/2/Pro, optimizing image generation costs, writing image prompts, or needing visual grounding with real-world reference images
Generate or edit images using Gemini's native `generateContent` via New-API. Suitable for scenarios requiring text-to-image generation, reference image editing, local PNG output, and those who want to reuse the `.sofunny-image.env` file or current shell environment variables.
Build Next.js web applications with Google Gemini Nano Banana image generation APIs (gemini-2.5-flash-image, gemini-3-pro-image-preview). Use when creating image generators, editors, galleries, or any app integrating conversational image generation with server actions, API routes, and storage. Use for "image generation app", "nano banana", "text to image", "AI image generator", or "gemini image". Do NOT use for non-Gemini models, Python/Go backends, model fine-tuning, or image classification/input tasks.
This skill should be used when generating and editing images using the Gemini API (Nano Banana Pro). It applies when creating images from text prompts, editing existing images, applying style transfers, generating logos with text, creating stickers, product mockups, or any image generation/manipulation task. Supports text-to-image, image editing, multi-turn refinement, and composition from multiple reference images.
Turn a reference Instagram Reel into a script for your own Reel, tuned to your voice and repurposed from your newsletter content. Takes a Reel URL or Notion reference link, uses Apify to scrape the video, sends it to Gemini 2.5 Flash for full transcript + hook + structure analysis, then writes a new script applying the same patterns to your newsletter topic. Use this skill whenever the user says "script a reel", "reels scripting", "turn this into a reel", pastes an Instagram Reel URL, or references their Notion outlier reels database. Requires APIFY_API_TOKEN and GOOGLE_AI_API_KEY environment variables.
Batch-translate content files using Gemini CLI as a subagent, with Claude orchestrating quality and validation