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概要と使いどころ
Produce pre-meeting lead intelligence briefs with Firecrawl. Use when the user needs company research, person research, recent news, talking points, pain points, or outreach preparation before a sales call, partnership meeting, investor conversation, or customer interview.
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Extract market, financial, earnings, industry, and company metrics with Firecrawl. Use when the user asks for market research, industry trends, public company data, financial comparisons, earnings research, or structured market 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。
Deep research with web search, source verification, and fact-checking. Runs before humanize + kami in the document pipeline. Use when user asks to research a topic, verify facts, gather sources, or do deep web investigation. Covers source validation, citation, and evidence hierarchy.
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 market research, competitive analysis, investor due diligence, and industry scans. Use when the user wants market sizing, competitor comparisons, fund research, or tech scans.
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 YouTube topics, analyze competitor videos, deconstruct viral content, and query the YouTube Data API. Use when researching a video topic before planning, analyzing video transcripts for viral patterns, searching competitor channels, or fetching video and channel stats via the YouTube Data API v3.
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。
Comprehensive competitor analysis, competitive intelligence, and comparison page creation. Use when analyzing competitive landscape, creating competitor comparison pages (alternative pages, vs pages, competitor A vs B pages), performing market research, building SWOT analysis, creating feature matrices, pricing comparisons, competitor teardowns, battle cards, or positioning maps. Covers research workflows, SEO-optimized comparison content, data-grounded reports with citations, 7-layer analysis framework, review mining, and sales enablement. Triggers include competitor analysis, competitive analysis, competitor teardown, vs page, alternative page, competitor comparison, market research, competitive intelligence, SWOT analysis, battle card, competitor pricing, market landscape, feature comparison, positioning map.
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, expand, and cluster keywords for content strategy. Use when planning content topics, building keyword lists, expanding seed keywords, clustering by topic and intent, or mapping keywords to funnel stages. Triggers on "keyword research," "keyword expansion," "keyword clustering," "content topics," "what to write about," "topic ideation," "content planning," "6 circles method," "seed keywords," "keyword difficulty," "search volume," "intent mapping," "pillar content strategy," or "content calendar planning." For SEO optimization and auditing, see seo-and-aeo-strategy.
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 user research and research ops. Use when running interviews, usability tests, surveys, or synthesis to de-risk product decisions; not experiments or review mining.
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。
Builds repeatable deep-research workflows for verified synthesis. Use when producing evidence-backed briefs, comparisons, dossiers, or research pipelines.
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。
Query Walmart US keyword research through the Nexscope research proxy. Use when the user requests this public marketplace or patent data; do not use for seller-console or store-authorized 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。
AI Overview (AI Mode) scraping via Google Search. Returns AI-summarized key points for a single keyword, ideal for deep research, technical Q&A, long-tail product selection, and cross-border consumer preference analysis using the latest web information. Single-round only; follow-ups require the agent to summarize context and issue a new request. Triggered by: Google AI, AI Overview, AI Mode, Google AI search, AI search, deep research, consumer preference analysis, web summary, long-tail product research, cross-border market insights.
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。
Design a complete lesson study cycle from research question through collaborative planning to research lesson. Use when planning jugyou kenkyuu or collaborative teacher inquiry into practice.
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 research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
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。
Conduct technical research on technologies and architecture. Use when the user says they would like to do or produce a technical research report
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。
Conduct market research on competition and customers. Use when the user says they need 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。
Conduct domain and industry research. Use when the user says wants to do domain research for a topic or industry
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。
Internal Harness instruction source for auto-paper-research. Route through visible Harness aliases instead of invoking directly.
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 the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.
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。
GPS-aligned genealogical research assistant for family history, ancestor research, record analysis, source-information-evidence classification, conflict resolution, citations, FAN research, privacy-sensitive document analysis, and proof statements, summaries, or arguments. Use for census, vital, probate, land, military, church, newspaper, DNA-plus-documentary, and other genealogy evidence tasks. Not for GEDCOM creation (use gedcom-creator), general history questions, DNA-kit shopping, or generic writing 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。
Draft, confirm, and synchronize research status, evidence, blockers, and review-ready GitHub summaries using the Research Memory skill, GitHub conventions, and file templates.
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。
Kundenforschung datensparsam und einwilligungsorientiert planen, durchführen und auswerten. Nutze diesen Skill für Interviewleitfäden, Umfragen, Usability-Tests, die Auswertung vorhandener Gespräche, Support-Tickets oder Bewertungen, und für Personas, Segmente und Jobs-to-be-Done. Klärt vorab Zweck, Rechtsgrundlage, Einwilligung, Aufzeichnung, Pseudonymisierung und Aufbewahrung, wertet nur innerhalb des Stichprobenrahmens aus und trennt Befund von Entscheidung. Beachtet DSGVO, BDSG und Beschäftigtendaten; Österreich und Schweiz werden getrennt behandelt.
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 explicitly invokes /loop-research or names this skill. Authors a loop-research run. Do not use unless named. Do not execute or resume generated runtime node files.
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。
Survey open legal questions and research gaps in the project. Use when asked "what legal questions are open", "where are our research gaps", or "list unresolved legal 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。
Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with 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。