Total 51,098 skills, AI & Machine Learning has 8558 skills
Showing 12 of 8558 skills
Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
Search global patents with natural language queries. Prior art, patent landscapes, and innovation tracking via Valyu.
INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.
基于多模态AI的图片识别与分析。当用户想分析、描述、从图片URL中提取信息、image recognition, image analysis, image description, image content understanding, OCR text recognition, visual Q&A时触发此技能。当用户提到图片识别、图片分析、图片描述、识别图片内容、分析产品图、从图片中读取文字、描述图片、提取视觉内容或理解照片内容时触发。当用户提供图片URL并就其视觉内容提问时，即使未明确说"图片识别"，也应触发此技能。
Split text into contextual chunks for RAG/embedding pipelines. Document segmentation and section extraction using window, tfidf, punctuation, or hybrid strategies chosen by intent.
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.
Complete reference for writing, running, and iterating on evals (automated conversation tests) for ADK agents. Covers eval file format, all assertion types, CLI usage, and per-primitive testing patterns.
Explains the ADK Dev Console — what each tab shows, how to read Agent Steps, traces, and other UI features visible at localhost:3001 during adk dev
dontbesilent Goal Clarification. Audit vague goals into checkable deliverables using Wittgenstein's philosophy of language. Triggers: /dbs-goal, /goal, "help me clarify my goal", "I want to build a personal IP", "my goal is to become...", "I want to be more..." Goal clarification using Wittgenstein's philosophy of language. Audits fuzzy goals into checkable deliverables. Trigger: /dbs-goal, "help me clarify my goal", "I want to become...", "my goal is..."
Generates image prompts for Seedream 5.0/4.0 (Jimeng AI), and can call the API to generate images and automatically download them to the output/ directory. Workflow: describe your idea → the agent outputs a prompt for review → user confirms → the agent runs generate.py. It covers text-to-image, image editing, multi-image fusion, character consistency, knowledge cards, posters, PPT backgrounds, e-commerce images, avatars, and group/storyboard generation. Activate this tool when the user mentions terms like seedream, jimeng, AI image generation, text-to-image, image-to-image, seedream prompt, prompt keyword, one-click image generation, knowledge card, poster design, e-commerce image, character consistency, or image generation.
Generate complete presentations with AI - from outline to polished slides
Generate, inpaint, and outpaint music with ACE Step on RunComfy via the `runcomfy` CLI. ACE Step is StepFun-AI's open-weights music foundation model — tag-driven composition (genre, mood, instruments), multilingual lyrics with section markers, 5 s to 4 min stereo output, $0.0002–0.0003 per second (≈ 27× cheaper than ElevenLabs Music). Four endpoints: ACE Step text-to-audio (the default), ACE Step 1.5 text-to-audio (50+ language lyrics, refined structured-lyric handling), ACE Step audio-inpaint (regenerate a time range inside an existing track), ACE Step audio-outpaint (extend an existing track before or after). Triggers on "ace step", "ace-step", "acestep", "ACE music", "open music model", "cheap AI music", "inpaint audio", "audio inpaint", "extend music", "audio outpaint", "lengthen track", "music with tags", or any explicit ask to generate or edit music with ACE Step.