2,318 件 ・ 関連度順
概要と使いどころ
Generate Bluetooth Low Energy (BLE) PHY waveforms. Read BEFORE writing any BLE waveform code to avoid hallucinating API patterns. Covers bleWaveformGenerator, bleIdealReceiver, bleCTEIQSample, bleAngleEstimate, bluetoothTestWaveform for LE1M/LE2M/LE500K/LE125K. Bluetooth Toolbox R2022a+.
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。
Design and validate digital filters in MATLAB. Use when cleaning up noisy signals, removing interference, filtering signals, designing FIR/IIR filters (lowpass/highpass/bandpass/bandstop/notch), or comparing filters in Filter Analyzer.
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。
Analyze data using MATLAB. Use when the task involves tables, timetables, time-series data, numeric arrays, sensor matrices, or gridded data — including but not limited to exploring, row filtering, sorting, cleaning, transforming, aggregating, smoothing, padding, trimming, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products 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。
Build custom PCB structures with pcbComponent, shapes, Boolean ops, feeds, and multi-layer stackups for non-catalog geometries. TRIGGER: user asks to build, modify, or customize a pcbComponent — add/remove shapes, edit polygons, place feeds, add metal layers, cut slots, or create non-catalog RF structures. Also when modifying geometry of an existing catalog-designed component (e.g., adding pads, removing elements, editing vertices). Invoke BEFORE writing pcbComponent code — layer/shape/feed API is non-obvious. SKIP: designing catalog components like filters/couplers/txlines (use the specific matlab-design-pcb-* skill), material/stackup definition only (use matlab-manage-pcb-material), EM analysis (use matlab-analyze-em), importing PCB files (use matlab-read-pcb-layout).
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 finite element analysis in MATLAB PDE Toolbox — geometry creation, model setup, solve, and post-processing in one skill. Use when building geometry from primitives or file import, setting up femodel with BCs/loads/materials, solving thermal/structural/EM problems, and extracting or visualizing results. Covers fegeometry, multicuboid, multicylinder, multisphere, decsg, boolean ops, mesh generation, femodel, all AnalysisTypes (thermalSteady, thermalTransient, structuralStatic, structuralTransient, structuralModal, structuralFrequency, electrostatic, magnetostatic, dcConduction, harmonic EM), materialProperties, faceBC, faceLoad, cellLoad, vertexLoad, solve, interpolation, von Mises stress, principal stress, reaction forces, heat flux, pdeplot3D visualization. Triggers on: PDE Toolbox, finite element, FEA, thermal analysis, structural analysis, electromagnetic analysis, femodel, mesh, boundary conditions, stress, displacement, heat transfer, post-processing.
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 this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).
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。
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
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。
Optional design-improvement pass for when you have spare usage to drain. Finds the shallowest modules in the code the spec touches, researches a deeper design, and proposes refactors that shrink interfaces and hide decisions — behavior held constant, tests green before and after. Proposes §I/§V/§T edits, never silent rewrites. Triggers when the user says "deepen this", "improve the design", "this module feels shallow", "pull complexity down", "use spare budget on the codebase", or invokes /ck:deepen. Leans on the codebase-design skill's deep-module vocabulary when present.
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 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.
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。
Audit active project plan files for staleness, completion, and routing truth. Use when cleaning up plans, reconciling active work, or archiving completed artifacts.
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。
Crabbox/Testbox remote proof: portable provider routing, untrusted isolation, Linux/macOS/Windows/WSL2, live E2E, diagnostics, cleanup.
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 build processing, find latest builds, and clean up old builds with asc. Use when managing build retention or waiting on processing.
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。
Audit a second-brain vault for structural problems and repair them: broken wikilinks, orphan pages, empty stubs, frontmatter that violates the schema, near-duplicate pages, and stale index entries. Use this skill whenever the user asks to lint, clean, audit or check the health of their vault, says pages feel messy or links are broken, after a large bulk import, or when a scheduled maintenance run fires. Do NOT use for ingesting new sources, for answering questions from the vault, or for rewriting page content that is structurally fine but reads badly.
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。
Clean a raw transcript before ingestion: punctuate, paragraph, label speakers, fix mistranscribed technical terms, and split long recordings by topic. Use this skill when the user drops an auto-generated transcript, subtitle file, podcast or lecture transcript, meeting recording text, or asks to process a video or audio source. Do NOT use for text that is already prose, for summarising a transcript, or for the ingestion itself.
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。
Merge two pages that describe the same thing, preserving every distinct claim, redirecting inbound links, and recording the merge. Use this skill when the user approves a duplicate merge, says "these are the same page", "merge X into Y", or asks to clean up near-duplicates that a lint run proposed. Do NOT use to merge pages on your own judgement, to delete a page outright, or to split one page into several.
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。
Generate Drizzle ORM schemas for Cloudflare D1 databases with correct D1-specific patterns. Produces schema files, migration commands, type exports, and DATABASE_SCHEMA.md documentation. Handles D1 quirks: foreign keys always enforced, no native BOOLEAN/DATETIME types, 100 bound parameter limit, JSON stored as TEXT. Use when creating a new database, adding tables, or scaffolding a D1 data layer.
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。
React 19 performance patterns and composition architecture for Vite + Cloudflare projects. 50+ rules ranked by impact — eliminating waterfalls, bundle optimisation, re-render prevention, composition over boolean props, server/client boundaries, and React 19 APIs. Use when writing, reviewing, or refactoring React components. Triggers: 'react patterns', 'react review', 'react performance', 'optimise components', 'react best practices', 'composition patterns', 'why is it slow', 'reduce re-renders', 'fix waterfall'.
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。
Audit and enforce the core/client boundary in multi-client projects. Detects where shared platform code is tangled with client-specific code, finds hardcoded client checks, config files that replace instead of merge, scattered client code, migration conflicts, and missing extension points. Produces a boundary map, violation report, and refactoring plan. Optionally generates FORK.md documentation and restructuring scripts. Triggers: 'fork discipline', 'check the boundary', 'is this core or client', 'platform audit', 'client separation', 'fork test', 'refactor for multi-client', 'clean up the fork'.
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。
Design and run risk-targeted robustness, sensitivity, error, and baseline checks for an approved mathematical model, emitting compact machine evidence in lean mode and a final report in submission mode.
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Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.
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。
Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream work.
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。
Triage new issues in the Sentry `javascript` project by archiving non-actionable noise. Use when asked to "triage issues", "triage the javascript project", "archive non-actionable issues", "triage new frontend issues", or "clean up the sentry/javascript queue". Operates only on the sentry/javascript project, only archives (never resolves), and always archives with `untilEscalating`.
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。
Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Use when asked to "simplify code", "clean up code", "refactor for clarity", "improve readability", or review recently modified code for elegance. Focuses on project-specific best practices.
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。
Organize a Power BI or Analysis Services semantic model end to end so it is ready for report authors, Excel users and Copilot - table groups, display folders, Title Case business names and naming conventions, hierarchies, sort-by columns, hiding, Analyze in Excel (IsAvailableInMDX), descriptions, format strings, data categories, context annotations, DAX formatting and UDFs, calculated-object justification, duplicate removal, perspectives, translations and security roles. Use whenever the user asks to "organize", "tidy", "clean up", "polish", "prepare", "standardize" or "make report-ready" a model or one table in it (for example "organize the date table"), to "fix naming conventions", "rename fields", "make names human readable" or "fix abbreviations", even when they name only one of these aspects, and whenever a model is driven through the Tabular Editor 3 MCP or the te CLI for that purpose. Not for DAX performance tuning (dax skill) or model design and star-schema changes (semantic-model skill).
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。