「find」の検索結果

Find emails, phone numbers, and enrich profiles using ContactOut. LinkedIn enrichment, people search, company enrichment, and batch 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。

Find decision-makers at a specific company using Apollo, Crustdata, Fiber, and PDL people search via Gooseworks MCP. Given a company name and target titles, returns a list of contacts with name, title, LinkedIn URL, and location.

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 leads by scraping engagers from a competitor's top LinkedIn posts. Given one or more company page URLs, scrapes recent posts, ranks by engagement, selects the top N, extracts all reactors and commenters, ICP-classifies, and exports CSV. Use when someone wants to "find leads engaging with competitor content" or "scrape people who interact with [company]'s LinkedIn posts".

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 or idea, define the Ideal Customer Profile, and route to the right next step — either mapping the TAM or finding leads/prospects directly. The entry point for any "find me leads", "map my market", or "who should I sell to" request. Auto-loads when a user provides a company URL or idea and asks for leads or market mapping.

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。

Discover newsletters in a target niche relevant to your ICP, evaluate audience fit, estimate reach and CPM, and output a ranked shortlist of sponsorship opportunities. Uses web search to find newsletters, then scores each against ICP alignment criteria. Use when a marketing team wants to reach an existing engaged audience for less than the cost of building their own, or when testing a new channel before committing.

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 newsletters relevant to a target audience/industry for sponsorship opportunities. Discovers newsletters through web search, newsletter directories, and industry research. Returns newsletter name, author, estimated audience, topic focus, sponsorship rates (if available), and contact info.

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 asked to run Design Error Detection (quick defect scan), find design errors in a Simulink model, perform root cause analysis on DED findings, fix division-by-zero, overflow, dead logic or out-of-bounds defects detected by SLDV, or diagnose why missing coverage cannot be achieved (dead logic blocking coverage objectives). Do NOT use for requirement verification, test generation, Inf/NaN detection, active logic analysis, or coverage measurement.

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, show, open, or identify shipped MathWorks example models that demonstrate a concept, block, or application domain, and return the command to open them.

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。

Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

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。

Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

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。

Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.

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。

Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

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。

Find and query PI assets using MATLAB's Industrial Communication Toolbox. Two paths: (1) direct PI tag lookup via piclient + tags when user provides a tag name or description (R2022a+), (2) Asset Framework navigation via afclient when user needs element/attribute hierarchy (R2026a+). Use when working with PI AF servers, PI Data Archive tags, asset hierarchies, element templates, attribute lookup, or historical data from OSIsoft PI systems.

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。

TikTok Creator Marketplace strategy and campaign management. Finding creators, negotiation tactics, campaign execution, performance tracking, and relationship building. Use when the user asks about TikTok Creator Marketplace, finding TikTok creators, creator partnerships, or influencer campaigns.

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。

E-commerce content marketing strategy planner. Generates content calendars, topic ideas, and platform-specific strategies by analyzing customer reviews, trends, competitor content, and SEO opportunities. Two modes: (A) Build — create a full content strategy from scratch, (B) Audit — analyze existing content and find gaps. Supports TikTok, Instagram, YouTube, Pinterest, blog/SEO, and Amazon A+. No API key required. Use when: (1) planning content for a new product launch, (2) building a content calendar, (3) finding viral content ideas, (4) analyzing competitor content strategies, (5) extracting customer pain points for content topics.

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。

Loop: find gaps → fix → repeat until agents find no gaps or a metric goal is hit. Shared loop primitive for PVL, EVL, and standalone quality runs.

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 a full codebase review, categorize findings by severity, file GitHub issues, then fix each issue in an isolated git worktree and submit PRs. Use this skill when the user asks to audit the codebase, do a comprehensive code review, find and fix security/quality/reliability issues, or run a proactive health check across the entire repository.

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。

Discover profitable business ideas from scratch. Analyzes market gaps, trending niches, user skills, and competitive landscapes with a competitive intelligence protocol including 4-filter benchmark stress test and Blue Ocean differentiation grid. Use when the user has no idea what to build, wants to explore opportunities, needs market research, competitive benchmarking, or says 'find me a business idea', 'what should I build', 'market research', 'find opportunities', or 'competitive 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。

Find Claude Code sessions tracked by the Agent Monitor by project (cwd), model, status, or date, then rank the matches by cost or recency. Pulls the session list and the distinct cwd / facet values so filters use real values rather than guesses. Use when locating a session — "find my EstateWise sessions", "which Opus runs errored this week", "most expensive sessions in /repo".

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 wants to find YouTube content on any topic: searching for videos or channels, finding creators who cover a subject, discovering tutorials, talks, or expert discussions, or looking up a channel by name or handle. Also use proactively when the user wants to research a topic and YouTube is a good source. Not for 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。

Audit and generate in-app user guidance — onboarding flows, empty states, tooltips, feature tours, contextual help, defaults, and inline hints. Browses the app to find where new users would get stuck, then produces the actual content and code to fix it. Pairs with ux-audit: audit finds problems, this skill builds the solutions. Triggers: 'onboarding', 'help content', 'empty states', 'user guidance', 'first run experience', 'feature tour', 'app is confusing', 'new user experience', 'make the app welcoming'.

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 plan cold start, get first users, or launch a new product with zero traction. Also use when the user mentions "cold start," "cold start problem," "first users," "seed users," "finding users," "finding early users," "Fiverr Upwork," "comment outreach," "Twitter search users," "product launch strategy," "0 to 1 growth," "early-stage acquisition," "launch channels," "get first customers," "Product Hunt launch," "AppSumo," "LTD," "indie hacker," "bootstrapping," or "solo founder." For directory listing copy and submissions, use directory-submission. For Product Hunt day-of execution, use product-hunt-launch. For GTM motion design, use gtm-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。

Search all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language. Use whenever the user asks which Japanese NLP resource to use, or wants to find one: tokenizers / morphological analyzers, BERT or LLM models, embeddings, NER, text classification, datasets / corpora, dictionaries, tutorials, or Hugging Face models. Trigger phrases include '日本語の形態素解析ライブラリ', 'おすすめの日本語tokenizer', '日本語BERTモデル', '日本語の感情分析データセット', '日本語LLM 一覧', 'which Japanese embedding model', 'Japanese NER library'.