「signal」の検索結果

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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State management for Angular 18-21: signals (signal/computed/effect), services-as-state, NgRx Store + Effects + Selectors, NgRx Component Store, NgRx Signals (newer signal-based store). RxJS essentials — operators, async pipe, takeUntilDestroyed, signal/observable interop. Use this skill to: - Pick the right state tool (signals / services / NgRx variant / vue-query equivalent). - Use signals correctly (signal/computed/effect — when each). - Build a Pinia-style service-as-state singleton. - Set up NgRx Store + Effects + Selectors. - Use RxJS without leaking subscriptions (async pipe, takeUntilDestroyed, Subject patterns). - Bridge signals ↔ observables via toSignal / toObservable. Do NOT use this skill for: - General Angular conventions (see angular-conventions). - Routing state (see angular-routing). - Form state (see angular-forms). - Testing state (see angular-testing).

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Deprecated alias. This skill was renamed to `deepline-scoring`. Use when a user or script still invokes `/niche-signal-discovery`: immediately load and follow the `deepline-scoring` skill instead. Triggers: ICP analysis, niche signals, won vs lost analysis, differential signals, signal discovery, ICP signal report, account scoring signals, lead scoring, first-party signals, buyer signals.

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Set up signal capture for a Simulink Real-Time application using `slrealtime.Instrument`. Use when choosing which signals a real-time application captures and where the data goes — test-pointing or canvas-badging a signal before build (`TestPoint`, `DataLogging`), constructing an Instrument from an `.mldatx`, calling `addSignal`, `addInstrumentedSignals`, `addFileLogSignals`, `validate`, `connectCallback`, `copy`, or `generateScript`, attaching an instrument to `tg.Instruments` via `addForFileLog`, `addForSDI`, or `addForMATLAB`, setting up live-streaming signals, dynamic file-log workflows, or persisting an instrument as M-code. Selects and routes signals only — not for configuring, building, running, or connecting applications onto targets.

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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 implementing signals in C# — [Signal] delegates, EmitSignal patterns, async signal awaiting, and event-driven architecture. Triggers on declaring, emitting, connecting, or awaiting a signal from a C# script, and on a signal crossing between C# and GDScript in either direction.

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Implements CPRA Section 1798.135 opt-out preference signal handling, covering Global Privacy Control (GPC) technical detection, automated signal honoring, cross-device consistency, and the relationship between browser signals and explicit consumer choices. Activate for GPC, opt-out preference signal, CPRA 1798.135, browser privacy signal, Do Not Sell queries.

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Use when searching for buying signals, detecting intent data, scoring prospect readiness, or monitoring accounts for trigger events. Triggers: 'scan for signals', 'find buying signals', 'who is ready to buy', 'intent signals', 'signal scoring', 'account signals'.

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"Apply signaling theory (Spence, 1973) to analyze how agents communicate private information through costly, credible signals under information asymmetry. Use this skill when the user needs to evaluate whether a corporate action serves as a credible signal, analyze dividend or IPO signaling, assess separating vs pooling equilibria, or when they ask 'why do firms pay dividends', 'is this signal credible', or 'how does underpricing signal quality'.".

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Why expensive, wasteful, effortful, or self-deprecating actions build trust and desire — Rory Sutherland on signalling. Use when the problem is credibility or desirability rather than meaning: the user asks 'why don't people trust us', 'how do we look more credible/committed/premium', 'is our guarantee/warranty worth it', 'should we spend on brand advertising', 'how do we justify a higher price', or wonders why a cheaper-looking competitor wins. Covers costly signals (advertising spend, guarantees, over-delivery), price-as-quality-signal (Veblen goods), and the Pratfall Effect (admitting a real flaw to gain trust). Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「信頼されない」「安心してもらえない」「強気な価格で売りたい」「広告費に意味はある？」）にも使う。中文咨询（“不被信任”“想以高价出售”“广告费有意义吗？”）也适用。

Per-trade smart-money signals — each result is a discrete buy or sell event from a tracked smart-money wallet, with trigger price, current price, max gain since trigger, and exit rate. BSC and Solana only. Use for: "smart money buy signal on $X", "any whale just bought $Y", "alpha signals in the last hour", "copy-trade-worthy signals", "trigger price and max gain on these trades", "on-chain trading signals from smart money".

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Detect buying signals across TAM companies and watchlist personas. Three-phase architecture: (1) free diff-based signals from existing data (headcount growth, tech stack changes, funding rounds), (2) Apify-powered signals (job postings, LinkedIn content analysis, profile changes), and (3) post-processing with dedup, scoring, and lead status updates. Writes signals to Supabase signals table for downstream activation.

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When the user wants to improve E-E-A-T, add trust signals, or optimize for expertise and authority. Also use when the user mentions "E-E-A-T," "E-E-A-T signals," "experience expertise authority trust," "author bio," "YMYL," "trust signals," "expertise signals," "authority signals," "citations," "references," or "credibility." For headings, use heading-structure.

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Expert blueprint for signal-driven architecture using "Signal Up, Call Down" pattern for loose coupling. Covers typed signals, signal chains, one-shot connections, and AutoLoad event buses. Use when implementing event systems OR decoupling nodes. Keywords signal, emit, connect, CONNECT_ONE_SHOT, CONNECT_REFERENCE_COUNTED, event bus, AutoLoad, decoupling.

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Angularの画面やサービスを作り、状態管理・フォーム・画面遷移の設計を支援します。使用バージョンに合わせて実装方針を選び、テストやビルド確認まで扱います。

• Angularアプリを新規作成したいとき
• フォームと状態管理を実装したいとき
• 画面遷移とアクセス制御の設計

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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Smart triggered RF capture on NI USRP radios with Wireless Testbench — record only when a signal of interest appears, not continuously (triggered spectrum sensing). Use energy detection to capture when signal power rises above the noise floor, or preamble detection to cross-correlate against a known sequence (WLAN L-LTF, 5G NR PSS/SSS, LTE PSS, Zadoff-Chu, custom protocols) and capture only when that protocol's preamble is detected. Use when implementing triggered (wake-on-signal) capture or spectrum sensing, capturing protocol-based signals via cross-correlation, calibrating detection thresholds (plotDetectionSignals / plotThreshold), scanning frequency bands for activity, or building transmit-then-detect workflows.

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Implement signal-based reactive state management in Angular v20+. Use for creating reactive state with signal(), derived state with computed(), dependent state with linkedSignal(), and side effects with effect(). Triggers on state management questions, converting from BehaviorSubject/Observable patterns to signals, or implementing reactive data flows.

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Multi-timeframe crypto technical analysis with scored trading signals, structured trade plans, and position sizing via Binance public API. Use when user asks to "analyze BTC", "what's the signal on ETH/USDT", "crypto analysis", "trade setup for SOL", "scan crypto pairs", "check the trend on BTC", "multi-timeframe analysis", "generate a trading signal", "what's the bias on ETH", "TA on BTC/USDT", "is it a good time to buy BTC", or "position size for a crypto trade".

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Focused Signals scout for PostHog projects running Replay Vision scanners — the standing LLM probes that watch session recordings and write `$recording_observed` events. Watches two promises: that enabled scanners are actually observing (throughput / success-rate cliffs, exhausted quota — a silent watch gap), and that what the scanners see in aggregate gets surfaced (a monitor's `yes`-rate or a scorer's score stepping away from its own baseline, a classifier tag or a recurring summarizer theme concentrating across many sessions). It is the agentic pull complement to the per-session push path: scanners with `emits_signals` already emit one signal per session into this same inbox, so this scout never repeats them — it adds the cross-session shape the per-session probe can't see. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

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How to query the document_embeddings table for raw signal data using HogQL. Use when you need to perform semantic search over signals, fetch every signal that contributed to a specific report, or list signal types. For browsing the curated report layer (the Inbox) — listing reports, filtering by status/source, drilling into a single report by ID — use the `inbox-exploration` skill first; drop into this skill afterwards if the user wants the underlying observations.

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Debug the signals pipeline locally end-to-end. Covers emitting test signals from fixtures, monitoring Temporal workflows via the REST API, reading sandbox agent logs from object storage, inspecting Docker sandbox containers, and diagnosing common failures (stale ClickHouse embeddings, agentsh network denials, inactivity timeouts). Use when a signal isn't reaching the inbox, a signal-report-summary workflow fails, or a sandbox task run times out.

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How to explore and make sense of PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to understand what scouts they have, how each one is behaving, and whether the fleet is actually working. Covers surveying the fleet and its schedules, reading recent scout runs and drilling into a single run's reasoning, inspecting the durable scratchpad memory the fleet has built up, tracing a run to the findings it emitted, and assessing a scout's health and performance over time (cadence, success rate, emit rate, signal-to-noise). Read-only and exploratory — to write or tune a scout, use `authoring-signals-scouts` instead. Trigger on "what are my scouts doing", "how is my <x> scout performing", "show me recent scout runs", "why did this scout find/emit nothing", "what has the fleet learned", "explore scout run <id>", "is my scout working".

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。

予測市場の価格が示す確率を出典と時刻付きで調べ、取引量や結果の判定ルール、他の情報源との比較から、製品や業務の判断材料として使えるか評価するスキル。

• 製品で予測市場データを使いたいとき
• 市場の確率と社内指標の比較
• エージェントの記憶や通知への利用検討