Neural web search - find similar content, extract pages, and run deep research
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Neural web search - find similar content, extract pages, and run deep 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。
Research a company's ideal customer profiles and build detailed synthetic buyer personas. Identifies 4-6 distinct buyer segments through web research, then creates rich, realistic personas with demographics, motivations, skepticism profiles, decision criteria, and language patterns. Use when you need to understand who your buyers are at a deep level — their motivations, objections, and how they evaluate solutions.
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-powered web search, crawling, extraction, and deep 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。
Web search and content fetching - search the web or extract content from URLs
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。
Jina Search - fast web search returning SERP results
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Multi-platform search - YouTube, Amazon, eBay, Walmart, TikTok, Instagram, and more
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Pre-sales-call intelligence composite. Deep dives on the person AND their company before a sales call, then maps findings to your product to generate talking points, objection prep, and a call strategy. Goes beyond generic attendee research — this is sales-specific intelligence that helps SDRs, AEs, and founders walk into calls with a plan. Tool-agnostic — works with any CRM, research source, and calendar.
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。
Web search, AI answers, content extraction, and async deep research
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Search for job postings across LinkedIn and Indeed. Use when users want to find open roles, monitor hiring signals, identify companies hiring for specific positions, or research competitor hiring activity. Returns job title, company, location, salary, description, seniority level, and direct apply URLs. No login or cookies required.
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 for jobs matching your skills, experience, and preferences
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End-to-end Google Search Ads campaign builder. Performs deep keyword research (competitor SEO, review language mining, Reddit/HN community terminology, site audit), builds keyword architecture with funnel mapping and intent classification, creates ad group structure, generates headline/description variants, builds negative keyword lists, recommends bid strategy, and exports a campaign-ready CSV for Google Ads Editor import.
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Research a company or brand from its website and produce a reusable Brand Core covering products, audience, competitors, positioning, offers, messaging evidence, voice, and visual identity. Use before growth, ad, content, creator, or product work when reliable brand context is missing.
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。
Finds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs later calls need. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill such as sqldw-cli, spark-cli, eventhouse-cli or fabriciq.
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Run remote BLAST searches against NCBI servers using Biopython Bio.Blast.NCBIWWW. Use when identifying unknown sequences, finding homologs, picking the correct BLAST program (blastn/blastp/blastx/tblastn/tblastx/psiblast/megablast/dc-megablast), interpreting Karlin-Altschul E-values, avoiding the max_target_seqs trap (Shah 2019), choosing composition-based statistics, or limiting searches by organism. Covers RID lifecycle, database choice (nt/nr/refseq_select/swissprot), word-size and CBS taxonomy.
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Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
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Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
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Optimize App Store product pages for search visibility and conversion. Use for App Store Optimization (ASO), keyword research, app name/subtitle/keyword-field strategy, conversion-focused descriptions and promotional text, screenshot captions and ordering, Custom Product Pages with assigned search keywords, In-App Events, Product Page Optimization tests, localized metadata, ratings/review strategy, and in-app review prompt timing with RequestReviewAction or AppStore.requestReview. Also use when routing ASO vs App Store review, privacy/ATT, or StoreKit implementation boundaries.
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This skill should be used when user asks to "search the web", "fetch content from URL", "extract page content", "use Tavily search", "scrape this website", "get information from this link", or "web search for X".
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Etsy search optimization — long-tail keywords, tag research, competitor analysis, seasonal trends
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Cross-platform keyword research for e-commerce. Discover high-converting keywords across Amazon, Shopify, Etsy, Google Shopping, TikTok Shop, and Walmart. Analyzes search volume signals, competition indicators, commercial intent, and long-tail opportunities.
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Use when structured YouTube data is needed: pasted video/channel/playlist links, transcripts for analysis, video metadata, channel upload history, search results, or playlist contents — without Google API quotas or OAuth. Triggers on YouTube URLs, creator names, topic research, or any request needing YouTube content, even if not mentioned explicitly. Not for uploads, account management, or written-source-only 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 YouTube data is needed without Google API quotas or OAuth setup: transcripts, video metadata, channel info, search results, playlists. Triggers on pasted YouTube links, creator names, @handles, topic research, video summaries, channel browsing, or any request where YouTube content would help — even if not mentioned explicitly. Not for uploads, account management, or written-source-only research.
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When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For third-party publishing strategy (which platforms to use), use parasite-seo. For GitHub repos, README, and Awesome lists, use github. For Medium.com only, use medium-posts. For Grokipedia edits, use grokipedia-recommendations. For traditional Google SERP strategy, use seo-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。
When the user wants to track AI search traffic in GA4 or GSC. Also use when the user mentions "AI traffic," "ChatGPT referral," "Perplexity traffic," "AI Overviews," "GA4 AI sources," "AI search analytics," "track AI referrals," "AI search traffic," "Claude traffic," or "how to track AI traffic." For AI SEO strategy, use generative-engine-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。