「signals」の検索結果

Discover rising category conversations, formats, sounds, questions, and creator patterns across social platforms, then separate durable demand signals from short-lived noise.

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。

Evaluate whether a creator, influencer, or brand account reaches the right audience using public profile, aggregate demographic, geography, language, content, comment, and commerce signals. Use for creator comparisons, sponsorship fit, market fit, and audience-quality 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。

Mine comments and replies on social posts, videos, and ads for recurring customer language, questions, objections, desired outcomes, complaints, product requests, purchase signals, and creative opportunities. Use for voice-of-customer research grounded in linked source evidence.

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。

Test product-demand hypotheses using public customer language, search and social signals, competitor offers, reviews, comments, creator content, and social-commerce evidence before committing to a product, launch, offer, or campaign.

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。

Monitor existing customer accounts for upsell and cross-sell signals: team growth on LinkedIn, new job postings, product usage patterns, funding announcements, and public company news. Produces a weekly expansion opportunity list with context and talk tracks. Chains web search, LinkedIn profile monitoring, and job posting 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。

Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X. Surfaces trending narratives, high-engagement topics, and early signals of emerging conversations before they peak. Chains linkedin-profile-post-scraper and twitter-mention-tracker. Use when a marketing team wants to ride trends rather than create them from scratch, or when a founder wants to know which topics are resonating with their audience.

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 outbound prospecting: detect intent signals, research companies, find decision-maker contacts, personalize messaging, launch campaign.

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。

Monitor web sources for Series A-C funding announcements. Aggregates signals from TechCrunch, Crunchbase (via web search), Twitter, Hacker News, and LinkedIn. Filters by stage, amount, and industry. Returns qualified recently-funded companies ready for outreach.

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 a prospect list of companies with decision makers, verified contact info, and hiring/intent signals. Use when asked to find leads by industry, build an account list with specific titles, prospect companies that are actively hiring, or create a targeted outreach list filtered by company size, location, and hiring activity.

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。

Detect buying signals from multiple sources, qualify leads, and generate outreach 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。

Scrapes LinkedIn job postings using the JobSpy library (python-jobspy). Use this skill whenever the user wants to find jobs on LinkedIn, search for open roles, pull job listings, build a job pipeline, source job targets for GTM research, or monitor hiring signals. Even if the user just says "find me some jobs" or "what roles is [company] hiring for", use this skill. It runs a local Python script that outputs a CSV of job postings with title, company, location, salary, job type, description, and direct 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。

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。

Daily industry intelligence scanner. Scans web, social media, news, blogs, and communities for industry-relevant events, trends, and signals. Produces a comprehensive intelligence briefing plus strategic GTM opportunity ideas. Orchestrates existing scraping skills — does not reimplement data collection.

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 GTM analysis of any target company. Researches what a company is currently doing across all go-to-market dimensions — content/blog, founder LinkedIn activity, SEO/traffic, hiring signals, social/community presence, customer acquisition channels, podcast appearances, review sites, competitive positioning, and partnerships. Produces a structured report identifying what's working, what's missing, and where white space exists for new strategies. Use before designing GTM strategies for a client or prospect.

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。

Reverse-engineer a company's sales and marketing tech stack from public signals. Detects CRMs, cold email tools, people databases, ad pixels, email delivery services, and outbound sending domains via DNS records, website source inspection, Apify technology profiling, blacklist checks, and public spam complaint searches. Works on single companies or batches. Outputs a structured markdown report per company.

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。

Add, configure, and manage faults on Simulink, Simscape, and System Composer model signals for robustness analysis and safety validation. Use when injecting faults (stuck, noise, gain, offset) onto block inports/outports, enabling fault simulation, or analyzing fault effects. Covers sensor failures, signal corruptions, actuator faults, FMEA validation, and robustness testing. Requires Simulink Fault Analyzer.

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Building or editing Simulink (.slx) models that stream signals in frames using DSP System Toolbox — audio, vibration, radar, comms. Use when the prompt names a Simulink block (Discrete FIR Filter, Buffer, Unbuffer, Rate Transition, Downsample, Upsample, Sample-Rate Converter, FIR Decimation, Time Scope, Spectrum Analyzer, From Multimedia File) or an action (frame-based processing, InputProcessing, buffering, overlap, windowing, STFT, short-time Fourier, decimate, downsample, upsample, resample, multirate, anti-aliasing, tunable filter). Prevents silent numerical errors from sample-vs-frame mismatch, wrong block choice, or missing anti-aliasing.

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Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting outputs.

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。

Author or upgrade Model Advisor checks for Simulink and System Composer models. Covers the full lifecycle: guideline authoring, check specification, Model Advisor check implementation, and qualification testing. Use when creating new checks (edit-time, standard batch, config-parameter, auto-fix), converting legacy StyleOne/StyleTwo/StyleThree checks to modern DetailStyle, creating a guideline for modeling, enforcing a modeling rule, or testing/qualifying a check. Triggers on any modeling constraint (e.g., "blocks shall...", "signals must match...", "parameters shall be set to...").

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Configures Simulink simulations non-destructively using SimulationInput objects — parameter overrides without modifying the model, batch sweeps via parsim, custom input signals via Dataset, and simulation data retrieval via logsout. Use when running sim()/parsim() with setVariable, setBlockParameter, setExternalInput, or when performing parameter sweeps and multi-run analysis. Not needed for one-shot simulations without configuration.

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Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.

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。

Increase widget visibility on Apple Watch using RelevanceKit. Use when providing contextual relevance signals for watchOS widgets, declaring time-based or location-based relevance, combining multiple relevance providers, helping the system surface the right widget at the right time on watchOS 26, or routing mixed RelevanceKit/WidgetKit/HealthKit/MapKit Smart Stack scope.

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Deep competitive intelligence for any market. Analyzes competitors' products, pricing, customer sentiment, GTM strategy, and growth signals using real web data. Produces battle cards, pricing landscape, and feature matrix. Use when the user wants to understand their competitive landscape, analyze competitors, compare products in a market, or research who they're competing against. Triggers for "who are my competitors", "competitive analysis", "competitor research", "battle cards", "pricing comparison", "competitor pricing", "market players", "competitive intelligence", "competitive landscape", "who else is in this space", "competitive moat", or any request to profile, compare, or map competitors in a category. Works standalone — no prior startup-design session needed.

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 reviewing a page or site for organic search, or diagnosing why something isn't ranking: crawl and index signals, on-page elements, content quality, and how to order the fixes.

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。