「embed」の検索結果

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD).

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。

Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.

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。

The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA aud

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。

Use when working with Cohere — cohere AI platform management covering models, embeddings, reranking, datasets, fine-tuning, and connectors. Use when monitoring API usage, analyzing model performance, reviewing fine-tuning jobs, managing datasets and connectors, or troubleshooting Cohere API issues.

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。

Use when working with Chroma — chroma vector database management, collection inspection, embedding analysis, and query performance monitoring. Covers collection metadata, document counts, distance metrics, index health, and tenant/database configuration. Read this skill before any Chroma operations.

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。

Embedding database for RAG and semantic search.

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。

F16 on BitNet-embedding-270M prefill (8 threads) Supports I2_S conversion with optimized kernels on x86 CPUs Lossless inference with 2 bits per weight

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。

Self-Retrieval generates relevant passage $p$ using the knowledge embedded within its parameters, which is different from dense retrieval or generativ

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。

Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-tools.

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。

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

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。

MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".

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。

Turn any codebase into a beautiful, interactive single-page HTML course that teaches how the code works to non-technical people. Use this skill whenever someone wants to create an interactive course, tutorial, or educational walkthrough from a codebase or project. Also trigger when users mention 'turn this into a course,' 'explain this codebase interactively,' 'teach this code,' 'interactive tutorial from code,' 'codebase walkthrough,' 'learn from this codebase,' or 'make a course from this project.' This skill produces a stunning, self-contained HTML file with scroll-based navigation, animated visualizations, embedded quizzes, and code-with-plain-English side-by-side translations.

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。

Expert en IA embarquée (TensorFlow Lite, ONNX, quantization, deployment on edge devices)

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。

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants.

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。

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

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。

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

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。

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

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。

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

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。

Use when adding interactive 3D scenes from Spline.design to web projects, including React embedding and runtime control API.

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。

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

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。

Generate Japanese PDFs using reportlab or fpdf2 with CJK font embedding, vertical text, business document templates, and inkan image placement. Triggers on requests to create 日本語PDF, 請求書PDF, 領収書PDF, PDF帳票, 縦書きPDF.

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。

Turn any URL — landing page, blog post, product page, documentation page, changelog, public PDF, or web article — into a finished AI-generated video using the Ozor MCP. Use this skill whenever the user gives a link and wants a video from its content: 'make a video from this URL', 'video from my landing page', 'turn this blog post into a video', 'video from https://...', 'video from our homepage', 'generate a video from this product page', 'create a video from this article'. This skill runs analyze_document with the url parameter (no scraping needed on our side), presents the resulting plan to the user, then generates → exports → embeds. Do NOT use this skill for a short text brief (use ozor-generate) or for an attached local file (use ozor-document-video).

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。

Turn a real document (PDF, PPTX, DOCX, Keynote export, slide deck, proposal, whitepaper, report) into a finished AI-generated video using the Ozor MCP. Use this skill whenever the user attaches, uploads, references, or points to a document file and wants a video from it — e.g. 'make a video from this pitch deck', 'turn this PDF into a video', 'video from my proposal.docx', 'generate a video from this PowerPoint', 'create a video from this report', 'this whitepaper → video'. This skill runs the document-to-video pipeline: analyze_document → present the plan to the user → update_plan if needed → generate_from_plan → export_video → wait_for_export → get_embed_code. It always uses the plan-based flow so slide visuals are preserved. Do NOT use this skill for plain-text creative briefs (use ozor-generate) or for URLs/landing pages (use ozor-url-to-video).

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。

Bridges UIKit and SwiftUI by wrapping UIKit views and view controllers in SwiftUI with UIViewRepresentable and UIViewControllerRepresentable, embedding SwiftUI in UIKit with UIHostingController, and coordinating delegate callbacks. Use when integrating camera previews, map views, mail compose, document scanners, PDF renderers, text views with attributed text, or other UIKit-only or third-party UIKit SDK surfaces into a SwiftUI app, or when migrating a UIKit app to SwiftUI incrementally.

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。