「vector」の検索結果

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

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Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.

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After Effects animation rendering for web and React applications. Use this skill when implementing Lottie animations, JSON vector animations, interactive animated icons, micro-interactions, or loading animations. Triggers on tasks involving Lottie, lottie-web, lottie-react, dotLottie, After Effects JSON export, bodymovin, animated SVG alternatives, or designer-created animations. Complements GSAP ScrollTrigger and Framer Motion for scroll-driven and interactive animations.

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小遥搜索 MCP 工具 - 本地文件智能搜索（语义/全文/图像/语音/混合搜索）

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Use when rigging a character, creature or prop in Maya: "rig this character", placing or orienting joints, Orient Joint doing nothing, IK/FK switch and matching, pole vector flips or elbow drift, space switching, foot roll, spline IK spine, offsetParentMatrix, blendMatrix or matrix rigging, controls and lockdown for animators, Quick Rig, HumanIK, AdvancedSkeleton or mGear, a game skeleton for Unreal or Unity, or a slow rig (parallel evaluation, profiling, GPU Override not kicking in).

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Use when building a visual identity or brand kit with Scenario: a logo, wordmark, or icon as editable SVG, a color palette and typography spec, logo variants (mono, reversed, favicon), and brand applications like social templates, avatars, banners, or merchandise mockups. Also for a rebrand or refreshing an existing mark. Keywords: brandkit, brand kit, visual identity, logo, monogram, wordmark, SVG, vectorize, color palette, typography, brand guidelines, brand board, launch kit.

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Use when editing an existing image on Scenario through MCP with a tool model, not a new generation: upscale or enhance to 2x, 4K, 8K, super resolution, 3D LUT color grade, color correction, posterize, solarize, vignette, film grain, blur, sharpen, glow, chromatic aberration, oilify, cubism, crystallize, dodge and burn, tint, desaturate, expand or uncrop, reframe an aspect ratio, resize to exact pixels, slice tiles, contact sheet, split into layers, remove a background or watermark, vectorize.

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Generate cartoon and animation style video prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants cartoon, 2D animation, cel-shaded, hand-drawn, illustrated, flat animation, or motion graphics animation style video. Triggers on: cartoon, animation, cel shading, hand-drawn, illustrated, flat design animation, vector animation, retro cartoon, rubber hose, motion graphics, or any cartoon/animated style request. Use even for vague requests like "make it look animated" or "fun colorful style.

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Designs production-grade RAG systems with document chunking, embedding generation, vector store configuration, hybrid search pipelines, reranking, and retrieval quality evaluation.

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Design RAG pipelines: chunking, retrieval evaluation, and architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, or evaluating with RAGAS metrics.

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Use when implementing game math — vectors, transforms, interpolation, curves, random number generation, and common geometric recipes

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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules fr

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A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.

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Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AutoML, Prompt Flow, online/batch endpoints, vector stores/RAG, or MLflow/ONNX deployments, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Science Virtual Machines (use azure-data-science-vm), Azure HDInsight (use azure-hdinsight).

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Expert knowledge for Azure DocumentDB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Mongo-compatible APIs, vector search, cross-region replication, change streams, or the HTTPS Data API, and other Azure DocumentDB related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance).

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Expert knowledge for Azure Database for PostgreSQL development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Flexible Server, pgvector search, managed identities, Private Link/TLS, or PITR/geo-restore, and other Azure Database for PostgreSQL related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Database for MySQL (use azure-database-mysql).

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Expert knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using NoSQL/Mongo/Cassandra APIs, change feed, multi-region HA, vector search, or Cosmos DB for PostgreSQL, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Data Explorer (use azure-data-explorer).

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Expert knowledge for Azure AI Search development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building indexes, skillsets, indexers, vector/semantic search, or RAG knowledge bases with Azure AI Search, and other Azure AI Search related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Cosmos DB (use azure-cosmos-db).

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LlamaIndex 官方用户文档离线知识库，用于检索并回答 LlamaIndex Python 框架的安装、RAG、数据加载、索引、检索与查询、Agent、Workflow、模型、Embedding、向量库、评估、可观测性、部署、LlamaCloud 和 LlamaParse 等问题，也可生成有文档依据的示例代码与排障建议。当用户提到 LlamaIndex、llama-index、LlamaParse、VectorStoreIndex、QueryEngine、Retriever、AgentWorkflow 或相关集成用法时使用；纯源码贡献、内部实现审查或与 LlamaIndex 无关的通用 AI 问题不使用。

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嵌入式 CAN / CAN-FD 调试工具，用于扫描接口、监控报文、发送测试帧、记录日志、数据库文件解码和总线统计。 当用户提到 CAN、CAN-FD、DBC 解码、总线抓包、USB-CAN 联调、报文发送、总线统计、 PCAN、Vector、slcan、CAN 接口扫描、CAN ID 过滤、ASC 日志、BLF 文件时自动触发， 也兼容 /can 显式调用。即使用户只是说"看看 CAN 报文"、"发一帧试试"或"解码一下 DBC"， 只要上下文明确提到 CAN 总线通信的操作或问题就应触发此 skill。

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OWASP LLM Top 10 (2025) audit checklist for AI applications, agent tools, RAG pipelines, and prompt construction. Use when performing any security review touching LLM client code, prompt templates, agent tools, or vector stores.

日本語の概要は準備中です。原文の説明を表示しています。