「search run」の検索結果

Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"

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 a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"

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 a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"

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。

End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study or survey, analyze interview data, run AI-moderated interviews, or collect survey responses. Covers the full lifecycle: planning studies, creating discussion guides, running AI-moderated interviews (real or synthetic) via Cookiy, designing and distributing surveys, and synthesizing results into reports.

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。

Analyze current trends and challenges in Japanese NLP for a topic. Surveys the existing awesome-japanese-nlp-resources dataset and augments it with up-to-the-minute web research to produce a combined trend + issue report. Use only when the user explicitly wants a trend/landscape report, a challenges/limitations report, or a general research overview of a Japanese NLP topic (this combines the bundled dataset with live web research). Trigger phrases include '日本語LLMの最新トレンド', '〜の動向をまとめて', '最近の日本語NLPの流れ', '日本語LLMの課題', '〜の問題点・限界', '未解決の論点', 'trend report on Japanese embeddings', 'latest Japanese speech models', 'challenges in Japanese NER', 'limitations of Japanese embeddings'. For a simple lookup use the search skill; this one runs web research.

Plan and execute user research including research planning, recruiting, interview design, qualitative synthesis, and translating findings into product decisions. Use this skill whenever the user wants to plan user research, design interviews, recruit participants, conduct discovery, run formative research, or synthesize qualitative findings. Triggers on user research, UX research, user interviews, discovery research, generative research, formative research, qualitative research, user insights, research synthesis, recruitment, interview guide, jobs to be done. Also triggers when product decisions are being made without user input and the user wants to fix that.

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 you want multi-source, multi-step research on a topic — competitor research before a sales call, market research for a new business idea, positioning angles, due diligence on a partnership or podcast guest, tech decision research (which DB, which auth), or any "I need to actually understand X." Combines web search, URL fetch, agent-browser, last30days (Reddit/X/YouTube/HN/web recency), memory, and Notion. Outputs a structured brief with citations, contradictions, gaps, and recommended next steps. Archives every research run to ~/.config/makerskills/deep-research/archive/ so past work is searchable. Triggers on "/deep-research," "research X," "investigate X," "do a deep dive on X," "look into X," "what's actually happening with X," "due diligence on X," "validate this market." Differs from a one-shot web search: this is multi-pass with verification.

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。

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

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。

Work with the @upstash/search TypeScript/JavaScript SDK, a serverless full-text and semantic search database with built-in reranking. Use when adding search to an app or site, creating a search index, upserting documents with searchable content and filterable metadata, running keyword, semantic, or hybrid search queries, reranking results, filtering with SQL-like or structured filter syntax, paginating with range, fetching or deleting documents, resetting an index, or checking index info. Also use when the user asks for site search, product, document, or knowledge-base search, or a managed search service that needs no cluster to run.

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 wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.

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。

Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.

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。

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

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。

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

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。

Find academic papers across up to 8 sources (OpenAlex / Semantic Scholar / CrossRef / PubMed / arXiv for English, OpenReview for AI venues, plus native-Chinese retrieval via NSSD 国家哲社文献中心 + yiigle 中华医学期刊) with adjustable depth — Quick scan (5 min) to Audit prep (3 hr). Use when the user wants to find papers, run a literature search, gather references, scope a research topic, search Chinese-language / 中文原生 literature (中文文献/中文核心/CSSCI/C刊/国内研究/国内文献/中华××期刊/心理学报/经济研究), or filter results by journal tier (中科院分区/一区/几区, Q1, JCR/SJR quartile, 影响因子/impact factor, 期刊分区, 顶刊/top journal, '按分区筛'), or export paste-ready professional search strategies / 检索式 for external databases (PubMed / WOS / Scopus / Embase / 知网 CNKI / 万方 / SinoMed — '写检索式', '导出检索式', 'WOS 检索式', '知网专业检索', 'search strategy', 'boolean search strategy'). Triggers on search verbs ('find papers', 'literature search', 'papers about X'), review types ('scoping review', 'systematic review', 'SR prep', 'literature review', 'lit review', 'help me write a lit review'), Chinese ('找文献', '找论文', '论文搜索', '学术检索', '文献检索', '文献综述', '综述前期', '求文献', '中文文献', '中文核心', 'CSSCI', 'C刊', '国内研究', '找中文的'). Outputs Shadcn HTML report + BibTeX/RIS/CSV + PRISMA-S log. Do NOT use for: concept explanations ('what is X' / 'X 是什么', e.g. '影响因子怎么算'), writing ('帮我写' / 'help me write a paragraph'), single-paper interpretation or PDF download with metadata (use paper-downloader-portable), or when the user already has a literature set (use literature-set-review).

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。

validateSearch, search param validation with Zod/Valibot/ArkType adapters, fallback(), search middlewares (retainSearchParams, stripSearchParams), custom serialization (parseSearch, stringifySearch), search param inheritance, loaderDeps for cache keys, reading and writing search params.

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。

Deep research with hyperresearch, for Claude Code and OpenAI Codex. Use when the user asks for deep research, a research report, a literature review, or a multi-source analysis with verified citations. Checks that the hyperresearch CLI is installed, sets it up in the current project for the agent you are running in, then hands off to the hyperresearch pipeline (a tier-adaptive 16-step pipeline with a persistent source vault). Not for quick lookups one or two searches can answer.

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 runtime and scene management: verify queries, inspect scenes, debug app readiness, and diagnose recall or scene-config 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。

Bulk-refresh research entries in ./research/ using parallel research-curator agents. Use when /refresh-research is invoked, stale research needs updating, or bulk re-verification of research entries is requested. Inventories entries by review date and age, runs RT-ICA pre-flight, spawns agents in waves of 5, then validates, reviews, and commits through research-curator. Supports --all, --stale, --category, --layer, and --dry-run flags.

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 the user needs current, online, or web-derived information — news, latest releases, vendor docs, real-world examples, recent changes, or anything not in the model's training set. LLM-neutral skill that exposes a unified web search CLI behind a single command. Runs single or multi-provider in parallel, saves raw + normalized JSON to a temp directory, prints the path so callers can pipe through rg / jq / grep. Zero-config out of the box. Works identically on macOS, Linux, WSL, Git Bash for Windows, and any environment with Python 3.8+. Triggers: 'search the web', 'look up online', 'google this', 'find current information', 'latest news', 'recent docs', 'check the internet', 'web search', 'browser search', 'research this', 'what is the latest', 'who is X', 'find examples of', 'is there documentation for', 'how do people solve', 'web-search', 'web_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。

Upstash サーバーレスデータプラットフォームリファレンス。 @upstash/redis — REST API、pipelining、transactions、JSON、Search、グローバルレプリケーション。 @upstash/ratelimit — Fixed Window、Sliding Window、Token Bucket、limit、blockUntilReady。 QStash — publishJSON、schedules、queues、DLQ、URL Groups、callbacks、flow-control。 @upstash/vector — upsert、query、ANN、hybrid index、sparse index、embedding models、namespace。 Rust 製ローカル vector DB エンジン fandhe-db とは別（こちらはマネージド SaaS の JS SDK）。 @upstash/workflow — durable execution、serve、context.run/sleep/call/invoke、waitForEvent、parallel steps、agents。 Upstash Search — search、upsert、fetch、range、filtering、reranking、algorithm、@upstash/search。 Upstash Box — サンドボックス、agent、filesystem、git、browser（CDP/AI actions/recordings）、network policy、snapshots、schedules。

Run a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user wants autonomous or deep research that may access the public web. Triggers: /autoresearch, autoresearch, research this topic, deep dive into, investigate, find everything about, research and file, go research, build a wiki on.

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 keyword research, classify by search intent, cluster into topical groups, and prioritize for content production. Use this skill whenever the user asks to do keyword research, find target keywords, identify ranking opportunities, classify search intent, build a topical map, or plan a content strategy around what people search for. Triggers on keyword research, keyword strategy, search intent, keyword clustering, topic clusters, keyword difficulty, search volume, ranking opportunity, content gap, what should I write about, target keyword, primary keyword, secondary keyword, long-tail. Also triggers when planning a content calendar or new site without keywords yet defined.

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。

Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.

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 a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user wants autonomous or deep research that may access the public web. Triggers: /autoresearch, autoresearch, research this topic, deep dive into, investigate, find everything about, research and file, go research, build a wiki on.

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。