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Found 4,631 Skills
OmniStudio Data Mapper (formerly DataRaptor) creation and validation with 100-point scoring. Use when building Extract, Transform, Load, or Turbo Extract Data Mappers, mapping Salesforce object fields, or reviewing existing Data Mapper configurations. TRIGGER when: user creates Data Mappers, configures field mappings, works with OmniDataTransform metadata, or asks about DataRaptor/Data Mapper patterns. DO NOT TRIGGER when: building Integration Procedures (use sf-industry-commoncore-integration-procedure), authoring OmniScripts (use sf-industry-commoncore-omniscript), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
Root Entry Skill for Meitu DesignKit Capabilities. Routes user intentions to: designkit-edit-tools (General Image Editing), designkit-ecommerce-product-kit (Multi-step E-commerce Product Kit).
Model cloud-native applications with Radius using Bicep. Use when asked to create an application definition, scaffold app.bicep, configure environments, or create custom resource types.
Overview The Amazon Agent is a high-performance tool designed to turn massive e-commerce datasets into structured, usable intelligence. It allows users to extract data from Amazon to monitor pricing,
Analysis of Lanhu design drafts and Axure prototypes. Directly read prototype pages, design drafts, and slice resources of Lanhu projects via lanhu MCP Server. Trigger scenarios: - Need to obtain Axure prototype pages from Lanhu for requirement analysis - Need to view Lanhu UI design drafts and design parameters - Need to extract slice resources from Lanhu design drafts - Need to collaborate via Lanhu team message board - Need to parse Lanhu invitation links Trigger words: Lanhu, lanhu, design draft, prototype, Lanhu link, design image, slice
When users ask for weekly reports, weekly summaries or work collections, collect multi-channel evidence around the target time range, merge them into work topics, and generate structured weekly reports. It is applicable to scenarios where multiple information sources such as collaboration platforms, Git, Agent, local documents coexist. Trigger phrases: "周报", "周总结", "工作汇总", "上周工作", "周工作", "weekly report", "work summary".
Use this skill when loading and managing resources in PixiJS v8. Covers Assets.init, Assets.load/add/unload, bundles, manifests, background loading, onProgress, caching, spritesheets, video textures, web fonts, bitmap fonts, animated GIFs, compressed textures, SVG as texture or Graphics, resolution detection, per-asset data options, and forcing a specific loader with the parser field (for extension-less URLs). Triggers on: Assets, Assets.load, Assets.init, loadBundle, manifest, backgroundLoad, Spritesheet, Cache, LoadOptions, unload, parser, loadParser, loadWebFont, loadBitmapFont, loadVideoTextures, GifSource, VideoSourceOptions.
Enhanced skill template with progressive disclosure, bundled resources, and quality rubrics. Use when creating new skills that need structured tiers, reference files, validation rubrics, or advanced bundling patterns beyond the basic template.
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
Use whenever researching a technical question — a library, tool, API, error, version, or "what's the best way to X" — or whenever you're about to answer from memory. Forces multiple real searches over primary sources (official docs, source code, high-vote Stack Overflow, maintainer blogs) instead of one search plus training-data filler, and rejects SEO content-farm slop. Trigger on "research X", "look into", "what's the best library for", "how does X work", "is this still true", "find out".
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
Build backend AI with Vercel AI SDK v6 stable. Covers Output API (replaces generateObject/streamObject), speech synthesis, transcription, embeddings, MCP tools with security guidance. Includes v4→v5 migration and 15 error solutions with workarounds. Use when: implementing AI SDK v5/v6, migrating versions, troubleshooting AI_APICallError, Workers startup issues, Output API errors, Gemini caching issues, Anthropic tool errors, MCP tools, or stream resumption failures.