「search skill」の検索結果

NanoClawの対話環境で、Markdownに保存したセッションの再開・分岐・横断検索・履歴圧縮・書き出しを扱い、モデル切り替えやスキル読み込みも支援します。

• 保存したセッションを再開したいとき
• リスクのある変更前に分岐したいとき
• セッション横断検索と履歴の圧縮

Turn an idea, tweet, or task into a skill in the Paperclip skills catalog (packages/skills-catalog). Use when asked to FIND or MAKE a skill and publish it as a bundled/optional catalog skill: research prior art, reference or author it, add examples, regenerate the manifest, open a PR.

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 instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.

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。

Team Mode security research skill. Orchestrates 3 vulnerability hunters and 2 PoC engineers to audit a codebase in parallel, prove exploitability, classify root causes, and calibrate severity by actual exploitability. Use for security review, vulnerability research, exploitability audit, pre-release security check, threat model validation, and `/security-research`. Triggers: 'security-research', 'security research', 'security review', 'vulnerability audit', 'exploitability audit', '보안 리뷰', '취약점 감사'.

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, flujo de trabajo académico, investigación a artículo, flujo completo de artículo, pipeline de investigación completa, publicación de investigación, flujo de trabajo completo del artículo.

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。

Analyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.

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。

Make sure to use this skill whenever the user mentions anything related to Danish job listings, job search in Denmark, finding work in Denmark, or job vacancies on Jobdanmark — even if they don't explicitly mention jobdanmark.dk. Also invoke this skill for questions about specific Danish job categories, municipalities, job types, or salaries in a job-search context. Trigger phrases include: danish jobs, jobs in denmark, find job denmark, job search denmark, danish job listings, jobdanmark, job opslag, find job, jobsøgning, ledige stillinger, stillingsopslag, job i Danmark, fuldtidsjob, deltidsjob, studiejob, praktikplads, elev, fleksjob, IT job denmark, sygeplejersker job, håndværker job, ingeniør job, pædagog job, kontor job, leder job, salg job, hotel job, kirke job, job aarhus, job københavn, job odense, job aalborg, job sjælland, job jylland, job fyn, jobkategorier denmark, ledige job, ansøgningsfrist, søg job, job opslaget, jobopslag, danish vacancies, work in denmark, employment denmark, job denmark, jobs near me denmark, apprentice denmark, internship denmark, part-time denmark, full-time denmark.

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 to search live software / tech / data / engineering job listings across many countries and markets (and remote) via the freehire.me aggregator's public API, or to look up a specific posting. It aggregates roles from ~50 ATS platforms into one schema, so a single skill covers many markets — but its faceted filtering (skills, category, seniority) is tuned tech-first, so scope triggers to technical roles. Trigger phrases: find a tech job, software job search, developer jobs, engineering vacancies, data/ML jobs, DevOps roles, remote developer jobs, "are there any <tech role> jobs in <place>", look up this freehire job posting.

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。

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my 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。

Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign 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。

Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.

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。

ARS-Codex research, writing, manuscript review, study screening, and experiments. Use for deep research, literature or systematic reviews, meta-analysis, research questions, drafts, revisions, roadmaps, abstracts, citations, integrity checks, peer review, research-to-paper. Screen records: sr-screener. Citation triggers: check citations, look over the refs, 檢查引用, 檢查參考文獻, 인용 확인, 인용 형식 검사. Korean: 논문 심사, 논문 수정, 초록 작성, 체계적 문헌고찰, 연구부터 논문까지. Español: revisión de literatura, revisar artículo, enmendar mi artículo, escribir resumen, investigación a artículo. Also use for ARS aliases: /ars-plan, /ars-outline, /ars-abstract, /ars-lit-review, /ars-citation-check, /ars-disclosure, /ars-format-convert, /ars-3w, /ars-revision-coach, /ars-revision, /ars-reviewer, /ars-mark-read, /ars-unmark-read, /ars-cache-invalidate, /ars-rebuttal-audit, /ars-full. Prompts, references, templates, and contracts live under ars/.

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。

This skill helps users automatically extract structured channel data from YouTube search results via BrowserAct API. Agent should proactively apply this skill when users express needs like finding YouTube channels about specific topics, collecting data on YouTube content creators, tracking YouTube influencers in specific industries, getting YouTube channel information for competitor analysis, searching for YouTube channels related to keywords, monitoring YouTube channel updates for specific keywords, finding YouTube channels that recently published videos, extracting YouTube channel subscriber counts, discovering YouTube vloggers in specific niches, building a YouTube channel database for market research, batch extracting YouTube channel links and descriptions, or monitoring competitor channel growth.

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。

This skill helps users automatically extract structured news data from Google News via BrowserAct API. Agent should proactively apply this skill when users express needs like searching for news about a specific topic, tracking industry trends, monitoring public relations or sentiment, collecting competitor updates, getting latest reports on specific keywords, monitoring brand exposure in media, researching market hot topics, summarizing daily industry news, tracking media activities of specific individuals, retrieving hot events from the past 24 hours, extracting structured data for market research, monitoring global breaking news.

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。

Searches Threads posts by keyword or hashtag and returns matching posts with engagement metrics, extracted from SSR-embedded JSON. Use when user asks to search Threads posts, find Threads content by topic, scrape Threads search results, collect Threads posts about a keyword, monitor Threads hashtag activity, pull Threads posts mentioning a term, gather Threads content by hashtag, search for posts on Threads, extract Threads search feed, get trending posts on Threads, find Threads discussions about a subject, or fetch recent or top Threads posts by keyword.

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。

This skill helps users extract structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications. Use this skill when users want to search for products on Amazon, find the best selling brand products, track price changes for items, get a list of categories with high ratings, compare different brand products on Amazon, extract Amazon product data for market research, look for products in a specific language or marketplace, analyze competitor pricing for keywords, find featured products for search terms, get technical specifications like material or color for product lists.

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 Taobao and Tmall product listings by keyword, returning paginated product cards with title, price, shop, image, sales, and tags. Use when user asks to search Taobao, find products on Taobao/Tmall, scrape Taobao search results, get product listings from Taobao, collect Taobao items by keyword, 搜索淘宝, 淘宝关键词搜索, 采集淘宝商品, 抓取淘宝搜索结果, 淘宝天猫商品列表. Also applies to bulk keyword searches, price monitoring across keywords, and competitive product research on Taobao.

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。

This skill helps users extract structured best-selling product data from Amazon via the BrowserAct API. Agent should proactively apply this skill when users express needs like search for best selling products on Amazon, extract Amazon product data based on keywords, find top rated Amazon products, monitor Amazon competitor prices and sales, discover trending products on Amazon marketplace, extract Amazon product titles prices and ratings, gather Amazon product sales volume for market research, search Amazon best sellers in specific region, collect Amazon product reviews and promotion details, analyze Amazon product availability and badges, get Amazon product data for market 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。

Operate any Obsidian vault as a living, self-rewriting second brain (an evolution of Karpathy's LLM Wiki pattern: sources rewrite existing pages, contradictions reconcile automatically, scheduled agents maintain the vault while you sleep). Use this skill whenever the user asks Claude to read, write, update, search, or manage their Obsidian vault - including saving notes from conversation, creating daily entries, updating kanban boards, logging dev work, managing people notes, capturing decisions, tracking deals, or maintaining any vault structure. Also triggers when the user wants to bootstrap a new vault from scratch, run a vault health check, or drop a _CLAUDE.md into their vault so all Claude surfaces share the same operating rules. Includes a research toolkit (7 commands: /x-read, /x-pulse, /research, /research-deep, /notebooklm, /youtube, /podcast) for AI-powered research via Grok, Perplexity, NotebookLM, YouTube, and podcast feeds - findings save to the vault automatically following the AI-first vault rule. Use proactively whenever the conversation produces information worth preserving (decisions, people met, projects started, tasks completed, lessons learned, research findings).

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 Coordinator v12.0 - Human-Centered Edition (Systematic Review Automation) Context-persistent platform with 24 specialized agents across 9 categories (A-G, I, X). Features: Human Checkpoints First, VS Methodology, Paradigm Detection, Systematic Review Automation. Supports quantitative, qualitative, mixed methods research, and systematic review automation. Language: English. Responds in Korean when user input is Korean. Triggers: research question, theoretical framework, hypothesis, literature review, meta-analysis, effect size, IRB, PRISMA, statistical analysis, sample size, bias, journal, peer review, conceptual framework, visualization, systematic review, qualitative, phenomenology, grounded theory, thematic analysis, mixed methods, interview, focus group, ethnography, action research, paper retrieval, AI screening, RAG builder, humanization, AI pattern detection

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。

Strategic research leadership for designing high-impact research agendas, coordinating teams, and translating research into policy and institutional influence.

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 and assess datasets for a research question. Dispatches Explorer agents to search across data source categories, then Explorer-Critic to stress-test each candidate. Produces a ranked list with feasibility grades. Make sure to use this skill whenever the user wants to identify or evaluate data sources — not to search for papers or run analysis. Triggers include: "find data", "what data should I use", "find a dataset for this", "where can I get data on X", "assess datasets", "what datasets exist for", "help me find data", "is there data on this", "what are my data options", "I need data for this project", or any request to locate empirical data sources for a research question.

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。

Interactive setup wizard that configures a new project for the social-science-research plugin. Asks the user questions about their field, institution, journals, datasets, key researchers, and R color palette, then writes the answers into references/domain-profile.md and CLAUDE.md. Make sure to use this skill first whenever a user is starting fresh or wants to configure the plugin. Triggers include: "set up my project", "configure the plugin", "run setup", "initialize this project", "I just installed the plugin", "set my field", "set my institution", "configure my domain profile", or any request to personalize the plugin for a specific research context.

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。

STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me code for [technique]" — including: entropy balancing, propensity score matching (PSM), difference-in-differences (DiD), regression discontinuity (RDD), instrumental variables (IV), event studies (CAR/BHAR), survival analysis, Fama-MacBeth regressions, bootstrap, quantile regression, reghdfe/xtreg/areg, clustering standard errors, fixed effects, esttab/outreg2 table formatting, winsorization, leads/lags. Users can specify their variables (e.g., treatment, outcomes, controls) and receive adapted syntax. NOTE: This skill provides code patterns from published papers, not research design advice.

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。