「signals」の検索結果

Transmit and capture RF waveforms using Wireless Testbench with NI USRP radios (X410, X310, N310, N320, N321, N300, X300, E320). Use when generating test signals, transmitting over the air, capturing IQ data, performing loopback tests, configuring multi-antenna setups, or troubleshooting dropped samples and gain settings. Covers basebandTransceiver, basebandTransmitter, basebandReceiver, continuous and once transmit modes, foreground and background capture, and UseRadioBuffer options. Also use when the user mentions transmit waveform, capture signal, IQ data, loopback, RF gain, sample rate, or antenna configuration.

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Generate GNSS baseband waveforms (GPS, Galileo, NavIC) with physically realistic or user-specified channel impairments using the Satellite Communications Toolbox. Use when generating GPS L1 C/A, L1C, L2C, L5, Galileo E1, E1C, E5a, E5b, E5, or NavIC L5, S, L1 signals. Covers gpsWaveformGenerator, galileoWaveformGenerator, satelliteScenario, Doppler/delay from orbital dynamics or custom values, navigation data encoding with ephemeris, and RINEX integration. Triggers on: GPS waveform, Galileo waveform, NavIC waveform, GNSS signal, satellite scenario, GNSS simulation, receiver test signal, baseband GNSS, L-band satellite signal, navigation signal generation.

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Read BEFORE writing any code that adds Additive White Gaussian Noise (AWGN) to signals and converts between SNR, Eb/No, Es/No, and per-subcarrier SNR for communications simulations, using awgn(), convertSNR(), berawgn(). The default MATLAB patterns for AWGN (e.g., 'measured' option, manual SNR formulas) produce subtly incorrect results. This skill specifies the correct calling conventions, required function usage, and critical anti-patterns that must be avoided.

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Extract features from 1D signals using signalTimeFeatureExtractor, signalFrequencyFeatureExtractor, and signalTimeFrequencyFeatureExtractor. Use when computing time-domain features (amplitude, energy, shape factors), frequency-domain features (spectral location, power, bandwidth, PSD), or time-frequency features (spectral shape, instantaneous, ridges, wavelet, EMD-derived) on a per-frame basis. Use when the user asks to "extract features", "compute spectral features", "build a feature table for a classifier", "get per-frame statistics", "run feature extraction on this signal", or describes a vibration / biosignal / radar / sensor signal needing features for downstream ML or analysis. Includes optional GPU acceleration via canUseGPU and gpuArray. Does not cover filter design, audio-specific feature extraction (use audioFeatureExtractor in Audio Toolbox instead), batch dataset orchestration, or 2D / image features.

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Use when importing or exporting vehicle data from/to log files (MDF/MF4/DAT, BLF, ASC/TXT), decoding CAN/CAN FD/LIN messages to signals via DBC, ARXML, or LDF databases, writing timetable data to MDF or BLF files, or calling blfread, blfinfo, blfwrite, mdfRead, mdfWrite, mdfCreate, mdfInfo, canSignalImport, canMessageImport, canMessageTimetable, canFDMessageTimetable, canSignalTimetable, or linMessageTimetable.

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Build RoadRunner HD Map entities in MATLAB — lanes, boundaries, markings, junctions, signs, signals, barriers, parking. Use when creating driving scenes from scratch, authoring road networks for simulation and testing automated driving systems, or assembling RRHD maps from Lanelet2 or other HD map sources.

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RoadRunner asset path lookup tables for map format conversions in MATLAB. Maps lane markings, signs, signals, barriers, objects, and lane types to RoadRunner asset paths. Use when converting map formats to RRHD, resolving asset paths, or assigning visual assets to HD Map objects.

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Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.

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Clarify intent before RIPER-5 phase delegation. Scores ambiguity (4 signals); generates structured multi-choice questions for Tier 2. Two-mode: SIMPLE and DEEP.

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Optimize e-commerce checkout flow to reduce cart abandonment. Friction analysis, payment method optimization, trust signals, and checkout UX best practices.

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Competitor pricing strategy analysis and market positioning. Price mapping, pricing gaps identification, elasticity signals evaluation, and strategic pricing optimization. Use when the user asks about competitor pricing, price analysis, pricing strategy, or competitive pricing research.

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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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Audit and optimize e-commerce landing pages for conversion. CTA placement, trust signals, page structure, copy optimization, and A/B testing strategy for product pages, collection pages, and campaign landing pages.

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Find evidence-backed potential first customers, beta users, or design partners from public demand signals. Use for product-specific prospect research, qualified shortlists, source-grounded outreach drafts, and follow-up searches refined by the user's feedback and local prospect history. Does not send outreach automatically.

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Scan recent Claude Code activity for errors and failure signals across all sessions using Agent Monitor data — APIError events and PreToolUse→PostToolUse gaps (tools that started but never completed) — then group failures by tool and model and rank them by frequency. Use when checking for errors or asking "what's failing right now".

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When the user wants to configure canonical URLs, fix duplicate content, or consolidate URL signals. Also use when the user mentions "canonical," "canonical URL," "duplicate content," "duplicate content fix," "preferred URL," or "URL consolidation." For GSC duplicates, use google-search-console.

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When the user wants to optimize for entity recognition, Knowledge Graph, or entity-based SEO. Also use when the user mentions "entity SEO," "entity optimization," "Knowledge Graph," "Knowledge Panel," "entity signals," "brand entity," "entity linking," "entity relationships," or "entity-first content." For structured data, use schema-markup.

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Use when structuring Godot 4.7 game code: folders, scenes and nodes, signals or an event bus autoload, custom Resources for item data, components (health, hitbox, hurtbox), an inventory, save and load, InputMap and key rebinding for keyboard, mouse and gamepad, GDScript static typing and style, or GDScript vs C#; or when code is spaghetti, a folder scan breaks in the export, a save loads wrong, the gamepad does nothing, or @onready is null.

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Plan and execute off-page SEO including link building, digital PR, brand mentions, citation building, and external authority signals. Use this skill whenever the user wants to build backlinks, plan a digital PR campaign, list local citations, run guest post outreach, develop linkable assets, recover lost links, or audit a backlink profile for risk. Triggers on link building, backlinks, digital PR, brand mentions, citation building, guest post, outreach, linkable asset, broken link building, HARO, podcast outreach, off-page SEO, anchor text, link velocity, toxic backlinks, disavow. Also triggers when the user is trying to grow domain authority or earn coverage.

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How to design a content hub that earns topical authority. Pillar topic selection, cluster planning, internal linking architecture, URL structure, pillar and cluster page anatomy, topical authority signals for SEO and AEO/GEO, and the maintenance discipline that distinguishes intentional hubs from accidental orphans. Triggers on pillar content, content hub, topic cluster, topical authority, content architecture, hub and spoke, pillar page, cluster page, content silo, internal linking strategy. Also triggers when a content set is not ranking despite individual piece quality, when a pillar was launched without a cluster, or when content has accumulated without an architecture.

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Audit the brand, UX, and site design of the leading sites in a vertical as a set of observable cross-site patterns, producing the experience bar a new build must meet or beat. Use this skill whenever the user wants to assess the competitive design field, capture vertical conventions, identify the design and UX bar of a category, or set the experience standard before a build. Triggers on competitor experience audit, UX audit, site design audit, brand audit, design conventions, what makes these sites good, vertical conventions, storefront patterns, merchandising patterns, layout density, primary task, design bar, category conventions, retail vs marketing register, what does the field do. Also triggers when an audit of the competitive field has captured technical signals (SEO, accessibility) but never named the brand, UX, or design conventions, and the build downstream will read as off-vertical without that bar.

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Validation-first design for Cavekit — every kit requirement must be automatically verifiable. Six-gate validation pipeline, phase gates, merge protocol, completion signals, acceptance criteria patterns. Use when designing acceptance criteria, wiring CI gates, or debugging why an agent is producing output that nobody can verify.

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Diagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput. Read-only. Surfaces host-side signals (Xid, ECC, NVLink, EFA reachability, FSx saturation) and routes to the appropriate sibling skill (hyperpod-node-debugger, hyperpod-nccl, hyperpod-version-checker, hyperpod-issue-report) for any remediation. Triggers on uneven NCCL across nodes, straggler node, FSx slow, checkpoint slow, dataloader slow, filesystem bottleneck, FSx throughput, cross-AZ latency, topology mismatch.

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Design and generate CI/CD pipelines from project stack signals across GitHub Actions, GitLab CI, CircleCI, and Buildkite. Use when bootstrapping CI, migrating pipelines, adding deployment gates, or optimizing build times.

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。