「ml」の検索結果

Send emails via SMTP (Gmail, Outlook, etc.). Supports attachments, HTML content, and multiple recipients. Use when user asks to send email, compose email, or email someone.

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。

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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。

Manage Obsidian vaults with full support for Obsidian Flavored Markdown — wikilinks, embeds, callouts, YAML properties, Dataview queries, Canvas, and Bases. Organize notes using MOCs (Maps of Content), atomic note principles, and consistent folder/tag taxonomies. Always ask the user where to save before creating notes.

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。

从零开始创建令人惊叹、动画丰富的 HTML 演示文稿，或通过转换 PowerPoint 文件生成。适用于用户想要构建演示文稿、将 PPT/PPTX 转换为 Web 页面、或为演讲/路演创建幻灯片的场景。帮助非设计师用户通过视觉探索而非抽象选择来发现他们的审美偏好。

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。

从零开始或通过转换 PowerPoint 文件创建令人惊叹、动画丰富的 HTML 演示文稿（Presentations）。适用于用户想要构建演示文稿、将 PPT/PPTX 转换为 Web 页面或为演讲/路演创建幻灯片（Slides）的场景。通过视觉探索而非抽象选择，帮助非设计师发掘其审美偏好。

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。

使用Nutrient DWS API处理、转换、OCR识别、提取、编辑、签名和填写文档。支持PDF、DOCX、XLSX、PPTX、HTML和图像格式。

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。

从零开始或通过转换PowerPoint文件创建令人惊艳、动画丰富的HTML演示文稿。当用户想要构建演示文稿、将PPT/PPTX转换为网页格式，或为演讲/推介创建幻灯片时使用。帮助非设计师通过视觉探索而非抽象选择发现他们的美学。

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。

使用 Nutrient DWS API 进行文档处理、转换、OCR、提取、脱敏、签名以及表单填充。支持 PDF、DOCX、XLSX、PPTX、HTML 和图像格式。

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。

从头开始创建令人惊叹、动画丰富的 HTML 演示文稿，或通过转换 PowerPoint 文件生成。当用户想要构建演示文稿、将 PPT/PPTX 转换为网页版，或为演讲/路演创建幻灯片时使用。帮助非设计师通过视觉探索而非抽象选择来发现他们的审美。

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。

使用 Nutrient DWS API 处理、转换、OCR、提取、脱敏、签名和填充文档。支持 PDF、DOCX、XLSX、PPTX、HTML 和图像。

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。

A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.

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 formatting references for journal submission, converting between citation styles (APA, MLA, Vancouver, Chicago), generating bibliographies for manuscripts, or ensuring consistent reference formatting. Automatically formats citations and bibliographies in 1000+ academi...

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。

Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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。

Scrum-inspired paper review, revision, and R&R workflow. Handles docx/tex/md/PDF in English or Chinese. Auto-detects manuscript stage, estimates sprint count, runs multi-lens review (Contribution/Rigor/Writing/Editor), generates prioritized revision backlog, exports MD/DOCX/PDF/HTML reports. Use when asked to review a paper, revise based on reviewer comments, handle R&R, respond to peer review, plan paper revision sprints, or when user types /ps or /papersprint.

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。

Build, modify, or review Stripe Apps and extensions using the relevant documentation and reference guides. Use for custom Stripe Dashboard UI, stripe-app.yaml, @stripe/ui-extension-sdk, @stripe/extensibility-sdk, script extensions, extension interfaces, custom workflow actions, and apps that authenticate to Stripe or react to events. Also use when deciding whether a Stripe extension can support the user's billing or workflow requirements.

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。

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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。

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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。

Research what Chinese internet users actually said in the last 30 days across Weibo, Xiaohongshu (RED), Bilibili, Zhihu, Douyin, WeChat public accounts, Baidu and Toutiao: engagement-weighted ranking, cross-platform clustering, cited Markdown/JSON/HTML reports. Also shows today's cross-platform hot-search board (全网热榜, --hot) and can add Hacker News / GitHub / Reddit or an installed upstream last30days for X/YouTube/TikTok.

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 this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

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Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.

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 whenever you generate or review a chart, plot, table, or paper figure in this workspace, including work delegated by paper-writing, literature-survey, and experiment skills. Applies the Open Science publication style, enforces readable final-size layout for figures and tables, and rejects generic diagram-tool output as a publication figure. Interactive Plotly/HTML may be used for exploration, but paper delivery requires a static publication-ready export.

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 ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

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。

Migrate Spring Boot 2.x applications to Spring Boot 3.x. Use when updating pom.xml versions, removing deprecated JAXB dependencies, upgrading Java to 17/21, or using OpenRewrite for automated migration. Covers dependency updates, version changes, and migration checklist.

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。

Align Python version and repo-declared dependencies (requirements.txt / environment.yml) before installing packages for NLP research code reproduction.

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。