'features' 검색 결과

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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Define and prioritize features with strategic traceability during PRD v0.3 Commercial Model. Triggers on requests to define features, prioritize capabilities, scope MVP, map features to pricing tiers, identify parity vs. delta features, or when user asks "what features do we build?", "what's in MVP?", "which features matter?", "feature priority", "parity features", "what's our delta?". Consumes KPI- (Outcome Definition), BR- (Pricing Model, Moat), and CFD- (Market Moat Analysis) from v0.3. Outputs FEA- entries with strategic traceability and BR-FEA- governance rules. Feeds v0.4 User Journeys.

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Use SAELens to train and analyze sparse autoencoders, which separate neural network activity into features that may represent recognizable concepts. Explore pretrained features, evaluate reconstruction quality, and investigate how individual features influence language model outputs.

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• Exploring features with a pretrained SAE
• Training and evaluating a custom SAE
• Studying overlapping concepts in model activity

Use SAELens to train and analyze sparse autoencoders, which break model activations into a small set of interpretable features. Explore pretrained features, evaluate reconstruction quality, troubleshoot training, and test how individual features influence generated text.

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• Exploring features in a pretrained SAE
• Training an SAE for a selected model layer
• Investigating inactive features and reconstruction errors

Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

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Add or troubleshoot Table v9 features such as sorting, filtering, pagination, selection, merged cells, and column layout. Guidance helps check feature registration, dependencies, and row-processing settings, with references selected for the task.

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• Adding sorting, filtering, and pagination
• Investigating missing table APIs or state
• Configuring row selection, cell selection, or merged cells

Guides the creation of detailed agile feature documentation from an existing epic. Use when the user wants to split an epic into feature files, derive features with scope and acceptance criteria, or plan feature documentation for stakeholders or engineering. This should trigger for requests such as Create features from an epic; Split epic into features; Feature files from epic; Derive features from epic; Break down an agile epic into deliverable features. Part of Plinth Toolkit

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Use when you need to refactor Java code to adopt modern Java features (Java 8+) — including migrating anonymous classes to lambdas, replacing Iterator loops with Stream API, adopting Optional for null safety, switching from legacy Date/Calendar to java.time, using collection factory methods, applying text blocks, var inference, or leveraging Java 25 features like flexible constructor bodies and module import declarations. This should trigger for requests such as Review Java code for modern Java development; Apply best practices for modern Java development in Java code; Modernize Java code with records pattern matching or switch expressions; Replace legacy idioms with modern Java features; Adopt Java 8+ language features safely. Part of Plinth Toolkit

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Compare project documentation with existing code to identify missing core features. Rank the gaps and create GitHub issues with practical specifications, implementation plans, acceptance criteria, effort estimates, and dependencies.

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• Find documented features missing from the implementation
• Prioritize gaps in core product functionality
• Turn feature gaps into actionable GitHub issues

Explore features for a new product through product management, design, and engineering perspectives. Generate five ideas from each viewpoint, then select five priorities with reasoning and assumptions to test before developing the concept further.

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• Exploring features for a startup idea
• Comparing ideas from three professional perspectives
• Choosing five ideas to validate first

Review how you use Superset and identify unused features that fit your workflow. Get a short list of suggestions, learn about each option, and set up selected features such as scheduled agents, parallel workspaces, or shared tasks.

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• Find unused Superset features worth exploring
• Set up scheduled agent tasks
• Start parallel work in separate workspaces

Extract battery features for degradation analysis and health monitoring in MATLAB. Covers cycling test features, differential curves (IC/DV/DT), and measurement statistics. Use when working with battery cycling data, SOH estimation, RUL prediction, or any battery test data analysis in MATLAB. Triggers on battery* functions such as batteryTestDataParser, batteryTestFeatureExtractor, batteryMeasurementFeatures, batteryDifferentialCurves.

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Bash 5.1 release features and improvements with practical examples. Use when working with Bash 5.1 features, epoch time variables, redirection enhancements, or when user asks about Bash 5.1 changes, new features, or version-specific capabilities.

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Bash 5.2 release features and improvements with practical examples. Use when working with Bash 5.2 features, variable handling enhancements, readline improvements, or when user asks about Bash 5.2 changes, new features, or version-specific capabilities.

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Bash 5.3 release features and improvements with practical examples. Use when working with Bash 5.3 features, new command substitution, GLOBSORT, loadable builtins, or when user asks about Bash 5.3 changes, new features, or version-specific capabilities.

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REA로 배포된 앱, 바이너리, 패키지를 분석해 기능과 버전별 차이를 조사합니다. 역컴파일과 실행 중 관찰을 활용해 근거, 추론, 미확인 사항을 구분합니다.

• 배포된 앱의 기능 구현 조사
• 앱 버전 간 동작 비교
• 바이너리와 포함된 리소스 분석

기존 코드의 일부를 감사하고 설계 결정 기록, 구현 관행, 누락 기능, 기술 선택의 네 관점에서 문제를 정리합니다. 계획부터 테스트, 수정, 재감사까지 진행합니다.

• 인수한 코드를 검토할 때
• 개발 도중 ECC 절차를 도입할 때
• 기능 추가 전에 모듈을 재검토할 때

개발 전에 제품의 목적을 검토하고 추진 여부, 첫 사용의 장애물, 기능 우선순위를 정리합니다. 근거와 다음 행동을 담은 기획서나 로드맵을 작성합니다.

• 모호한 아이디어의 기획서 작성
• 개발할 기능의 우선순위 결정
• 출시 전 첫 사용 과정 점검

Explore concepts learned by language models using sparse autoencoders, which separate internal activity into a small set of features. Analyze pretrained models, train custom autoencoders, assess reconstruction quality, and experiment with feature based output adjustments.

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• Exploring features with a pretrained SAE
• Training an SAE for a chosen model layer
• Comparing feature responses across input texts

Build a single-page SaaS product landing page with features, testimonials or logos, optional pricing, and clear action buttons. Follow the colors, fonts, and layout rules in DESIGN.md, and deliver one self-contained HTML file.

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• Creating a landing page for a SaaS product
• Building a product page that follows an existing design system
• Presenting features, testimonials, and pricing together

Prepare a one-time What's new dialog for the desktop app when several meaningful features have accumulated. The skill selects changelog highlights, drafts concise copy, updates the content file, and guides testing and in-app previews.

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• Decide whether accumulated features warrant an announcement
• Draft concise copy for three or four feature highlights
• Add a new What's new content entry

Turn a product brief into a working website or prototype in Lovable. Inspect existing project code, update pages and features, and publish changes through Lovable’s official MCP server, with separate permissions for editing and publishing.

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• Creating a website from a product brief
• Building a prototype to explore an idea
• Inspecting code in an existing Lovable project

Create and edit QGIS map projects from the terminal. Add geographic features and attributes, arrange maps and labels in print layouts, export PDFs or images, and run QGIS algorithms for processing geographic data.

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• Creating a map project and adding points
• Adding geographic features and attributes
• Exporting labeled maps as PDFs or images

Reduce data with many features into fewer dimensions using UMAP. Create 2D or 3D views, prepare inputs for clustering, compare parameter settings, and project new samples while checking whether the results remain stable and meaningful.

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• Visualizing data with many features in 2D or 3D
• Preparing reduced features for HDBSCAN clustering
• Guiding dimensionality reduction with partial labels