「search」の検索結果

Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu 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。

Run programmatic Tavily search and extraction while filtering raw results outside the main agent context. Use for multi-step or high-volume research where titles, snippets, and selected passages should be curated before synthesis.

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。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

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 read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires 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。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

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。

Search and analyze Reddit discussions for market research, product feedback, and community insights using Xpoz. Use when asked to "search Reddit", "what does Reddit think about X", "Reddit feedback on X", "subreddit analysis", or "Reddit market research".

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 AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid 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。

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance 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。

提案書の論点・仮説に基づくデスクトップリサーチを実行するスキル。WebSearch/WebFetch、Browser Use、Deep Researchプロンプト生成の3層で情報収集し、論点ごとに主張・根拠・ソースを整理した調査レポートと仮説検証シートを出力する。「デスクリサーチを実行して」「初期調査をして」「デスクトップ調査を行って」「追加調査をして」などのリクエスト時に使用。ワークフローのStep 3（初期デスクトップ調査）およびStep 10（デスクリサーチ）に対応。

Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid 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。

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

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 AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid 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。

Research AI coding assistant products, audiences, user stories, social proof, hacks, and unmet needs for apps like Cursor, Claude Code, Codex, Warp Code, Devin, Windsurf, GitHub Copilot, Cline, Aider, and homegrown agent stacks. Use when designing or positioning agentic developer tools, Port Daddy Agent Harbor, swarm consoles, background coding agents, or competitive/product research for AI software engineering. NOT for implementing the coding agent runtime, generic market research, or UX mockups without developer workflow evidence.

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。

Working playbook preloaded into the researcher helper; the coach updates its Lessons after each round. Use only when acting as researcher or coaching it. NOT for general research tasks.

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。

When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.

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。

Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.

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 AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid 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。

Perform structured investment research (投研分析) for a company/stock/ETF/sector using a repeatable framework: fundamentals (basic/财务报表与商业模式), technical analysis (技术指标与关键价位), industry research (行业景气与竞争格局), valuation (估值对比/情景), catalysts and risks, and produce a professional research report + actionable plan. Use when the user asks for: equity/ETF analysis, earnings/financial statement breakdown, peer/industry comparison, valuation ranges, bull/base/bear scenarios, technical trend/support-resistance, or a full research memo.

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。

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance 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。

Optional vendor skill for Parallel CLI — agent-native web search, extraction, deep research, enrichment, FindAll, and monitoring. Prefer JSON output and non-interactive flows.

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。

Phase 1 of the Forge pipeline. Gather real context from the codebase, answer everything the code can answer through exploration, then grill the user one question at a time (with a recommended answer) for only what code cannot answer, and write a research document. Use when starting /forge-research or the research step of /forgemaster, or when the user wants a research-first investigation before planning a feature.

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 web researcher using advanced search techniques and synthesis. Masters search operators, result filtering, and multi-source verification. Handles competitive analysis and fact-checking. Use PROACTIVELY for deep research, information gathering, or trend analysis.

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。

MUST USE when user wants to 调研/research/搜索/search/查/找/look up anything on the internet — e.g. 全网调研 X / 帮我调研一下 X / 查一下 X / 搜搜 X / 看看大家怎么评价 X / X 上有什么讨论 / research this topic。 Also MUST USE when user mentions any platform or shares any URL/链接: 小红书/xiaohongshu/xhs, Twitter/推特/X, B站/bilibili, Reddit, Facebook, Instagram, V2EX, LinkedIn/领英/招聘/求职/jobs, YouTube, GitHub code search, 小宇宙播客, 雪球/股票行情, RSS feeds, or any web URL. 15 platforms, multi-backend routing (OpenCLI / per-platform CLIs / APIs). Zero config for 6 channels. Run `agent-reach doctor --json` to see which backend serves each platform right now. NOT for: 写报告/数据分析/翻译等内容加工（本 skill 只负责从互联网获取内容）； 发帖/评论/点赞等写操作；已有专门 skill 的平台（先用专门 skill）。 【路由方式】SKILL.md 包含路由表和常用命令，复杂场景需按需阅读对应分类的 references/*.md。 分类：search / social (小红书/推特/B站/V2EX/Reddit/Facebook/Instagram) / career(LinkedIn) / dev(github) / web(网页/文章/RSS) / video(YouTube/B站/播客) / finance(雪球/股票)。

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。

Search local documents, files, notes, and knowledge bases. Index directories, search with BM25/vector/hybrid, get AI answers with citations. Use when user wants to search files, find documents, query notes, look up information in local folders, index a directory, set up document search, build a knowledge base, needs RAG/semantic search, or wants to start a local web UI for their docs.

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。