「channel」の検索結果

Design and implement adaptive filters using DSP System Toolbox System objects. Use when working with adaptive filtering, system identification, noise cancellation, echo cancellation, active noise control (ANC), channel equalization, inverse system identification, or adaptive prediction. Covers dsp.LMSFilter, dsp.RLSFilter, dsp.FilteredXLMSFilter, dsp.FrequencyDomainAdaptiveFilter, dsp.AffineProjectionFilter, dsp.BlockLMSFilter, dsp.AdaptiveLatticeFilter, dsp.FastTransversalFilter, maxstep(), and algorithm selection for adaptive filtering problems. Replaces deprecated adaptfilt.* objects (removed R2020a).

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Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

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Model, simulate, and optimize Serializer/Deserializer (SerDes) systems — serial and parallel links — using MATLAB SerDes Toolbox. Design NRZ and PAM-N links (PAM3 through PAM16) — explore equalization architectures (FFE, CTLE, DFE), sweep or optimize parameters with genetic algorithms, and characterize channels from loss models, S-parameter files, or crosstalk scenarios. Process captured waveforms through equalization chains, build eye diagrams, and decompose jitter. Deliver IBIS-AMI models for Tx, Rx, Redriver, or Retimer by exporting to Simulink and compiling .ami/.ibs/.dll/.so files. Covers the full arc from initial design exploration and parameter optimization to compliance testing and compiled model validation, including custom datapath blocks for nonstandard equalization.

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B2B/wholesale setup — wholesale pricing, minimum orders, custom catalogs, net payment terms

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Google integration — Shopping feed, Performance Max, free listings, Merchant Center optimization

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Plan and execute e-commerce product launches. Pre-launch, launch day, and post-launch playbooks for Amazon, Shopify, and multi-channel sellers. Covers inventory, pricing, advertising, and review strategy.

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Build and strengthen your e-commerce brand identity. Brand positioning, messaging, visual identity guidelines, and brand consistency across sales channels and marketing touchpoints.

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Evaluate ecommerce price candidates using unit economics, historical observations, elasticity analysis, scenario modeling, and controlled experiments. Use when a seller asks what price to test, how price changes could affect contribution or revenue, how to estimate elasticity, how to optimize a bundle or tier, or how to design a price experiment across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not claim a proven optimal price without sufficient clean data, and do not change live prices without explicit authorization.

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Cross-platform PPC strategy planner for ecommerce businesses. Analyzes your product and margins, recommends the right advertising platforms (Google Ads, Meta Ads, TikTok Ads), calculates ROAS targets, allocates budget across channels, and generates platform-specific campaign briefs with ad copy and creative direction. Two modes: (A) Build — design a multi-platform ad strategy from scratch, (B) Optimize — audit existing cross-platform campaigns and reallocate budget. Works for Shopify, WooCommerce, standalone stores, and marketplace sellers expanding to external traffic. No API key required.

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Cross-platform ecommerce competitor analysis and strategic intelligence. Multi-channel presence evaluation, strategy assessment, positioning analysis, and competitive benchmarking. Use when the user asks about competitor analysis, competitive research, market analysis, or competitive intelligence across platforms.

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Design a controlled dynamic-pricing or repricing system for ecommerce products. Use when a seller asks for demand-based, inventory-based, competitor-responsive, or time-based price rules; SKU eligibility; price floors and ceilings; automation approvals; simulations; monitoring; or rollback plans across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for a one-time optimal-price calculation or to change live prices without explicit authorization.

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Build an evidence-based competitive pricing strategy for ecommerce products. Use when a seller asks how to position a price against competitors, set regular and promotional prices, protect contribution margin, design bundles or price tiers, respond to competitor moves, or coordinate prices across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for automated repricing implementation or claims of a mathematically proven optimal price without sufficient data.

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Manage project learnings — small, atomic, validated patterns that the agent should remember across all skills and sessions. Different from /money-save (which captures full session state); learnings are individual insights that get auto-loaded into every other money-* skill's context. Use when the user has just discovered something worth remembering — a customer pattern, a pricing insight, a channel that works, a failure mode. Triggered by: 'remember this', 'log a learning', 'this is a pattern', 'show learnings', 'what have we learned', '记住这个', '存入经验', '查看经验库'.

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When the user wants to plan paid ads strategy, allocate ad budget, or choose paid channels. Also use when the user mentions "paid ads," "paid media," "PPC," "SEM," "web ads," "app ads," "TV ads," "CTV," "OOH," "banner ads," "ad network," "ad alliance," "Taaft ads," "Shopify App Store ads," "Google Ads," "Meta Ads," "PMF testing," "PMF validation," "test product-market fit with ads," "ad spend," "ad budget," "ROAS," "paid acquisition," "Quality Score," or "ad-to-page alignment." For Google Ads execution, use google-ads. For Meta Ads execution, use meta-ads. For landing page alignment, use landing-page-generator.

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When the user wants indie hacker or bootstrapping founder strategy—growth, channels, Build in Public, or solo founder tactics. Also use when the user mentions "indie hacker," "indie developer," "bootstrapping," "bootstrapped founder," "solo founder," "Build in Public," "scratch your own itch," "Micro-SaaS," "first 100 users," or "solo company." For cold start, use cold-start-strategy.

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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.

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When the user wants to run app install ads, user acquisition (UA), or promote mobile apps. Also use when the user mentions "app ads," "app install ads," "UA," "user acquisition," "Google App Campaigns," "Apple Search Ads," "ASA," "UAC," "App Store ads," "Play Store ads," "CPI," or "app promotion." For store listings, use distribution-channels.

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When the user wants to plan, create, or repurpose visual content (images, infographics, social post images) across channels. Also use when the user mentions "content images," "social media images," "infographic," "visual content," "post image," "image specs," "visual repurposing," "content visuals," or "image for social post." For Pinterest, use pinterest-posts.

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When the user wants to submit a product or app to directories, curated lists, launch platforms, or app stores—and needs ready-to-paste copy per platform. Reads root contextus.md when present. Also use when the user mentions "directory submission," "get listed," "app store listing," "submit to directories," "curated list," "best tools list," "Taaft," "Product Hunt," "directory ads," "newsletter feature," "directory campaign," "tailor description per platform," "Shopify App Store," "Chrome Web Store," "navigation site," or "product directory." For Product Hunt launch day tactics (hunter, first comment, timing), use product-hunt-launch. For full 0→1 channel planning, use cold-start-strategy.

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Adds Microsoft Teams connector to a Power Apps code app. Use when sending Teams messages, posting to channels, or integrating with Teams chat.

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Use when a UE5 task involves Niagara or Chaos destruction: a Niagara fireball, projectile trail, impact explosion, muzzle flash, spell, smoke, sparks, debris, Niagara fluids or flipbooks; "Niagara is too expensive", stat NiagaraOverview, Effect Types, pooling, Data Channels, lightweight emitters, VFX scalability; judging an effect's readability, value, color, timing, white blowout or strobing; or a wall that shatters, geometry collection fracture, Dataflow, damage thresholds, floating chunks, debris cost, Chaos Visual Debugger.

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Query Datadog APM services, dependencies, performance, and traces with Pup.

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Implements concurrent Go patterns using goroutines and channels, designs microservices with gRPC or REST, optimizes performance with pprof, and enforces idiomatic Go.

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Design a delivery team's communication system — channel routing, meeting-load reduction, status structure, escalation SLAs, timezone norms. Use when the calendar is full, updates go unread, or blockers surface late.

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