「aria」の検索結果

Advogado criminalista especializado em Maria da Penha, violencia domestica, feminicidio, direito penal brasileiro, medidas protetivas, inquerito policial e acao penal.

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。

Implement WCAG 2.2 compliant interfaces with mobile accessibility, inclusive design patterns, and assistive technology support. Use when auditing accessibility, implementing ARIA patterns, building for screen readers, or ensuring inclusive user experiences.

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, implement, and audit inclusive digital products using WCAG 2.2 Level AA standards. Use this skill to generate semantic ARIA for Web and accessibility traits for Web and Native platforms (iOS/Android).

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。

Identify poisoned training data and backdoored models across the ML pipeline.

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。

Analyzes UEFI bootkit persistence mechanisms including firmware implants in SPI flash, EFI System Partition (ESP) modifications, Secure Boot bypass techniques, and UEFI variable manipulation. Covers detection of known bootkit families (BlackLotus, LoJax, MosaicRegressor, MoonBounce, CosmicStrand), ESP partition forensic inspection, chipsec-based firmware integrity verification, and Secure Boot configuration auditing. Activates for requests involving UEFI malware analysis, firmware persistence investigation, boot chain integrity verification, or Secure Boot bypass detection.

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。

Formally verify programs by writing Lean 4 proofs. Trigger this skill whenever the user wants to formally verify code, generate Lean 4 proofs, prove properties about algorithms or smart contracts, verify invariants, convert program logic into formal specifications, or anything involving Lean 4 and formal verification. Also trigger when the user mentions "qedgen", "lean proof", "formal proof", "verify my code", "prove correctness", "formal verification", or wants mathematical guarantees about their implementation.

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 when building styled UI with @nuxt/ui v4 components (Button, Modal, Form, Table, etc.) - provides ready-to-use components with Tailwind Variants theming. Use vue skill for raw component patterns, reka-ui for headless primitives.

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。

Create or change immutable business values with meaningful invariants, equality, or behavior, such as order numbers and money. Use when primitive values obscure domain rules; avoid wrapping every scalar.

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。

대화형 System-wide Refactoring — 변경 파일에서 Extract Method·Domain Logic 이동·Pull Up 등 클래스 경계를 넘는 구조 변경 후보를 찾아 승인 후 기법별 커밋으로 적용. "구조 리팩토링", "도메인 로직 이동", "클래스 분리", "system-wide", "/system-wide-refactoring" 요청 시 사용. 단, 메서드 내부 정돈(guard clause·extract variable 등)은 /tdd-tidy가 적합. /system-wide-refactoring [commit-ref]로 호출.

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。

Refactor code by renaming variables, methods, classes, and concepts for clearer intent. Use when asked to improve names, clarify naming, perform a naming pass, or run `/refactor:names`.

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。

Refactor code to remove dead code, useless comments, unused variables, and unused imports. Use when asked to declutter a class, clean up noisy code, remove cruft, or run `/refactor:declutter`.

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。

Load when orchestrating Multi-Agent Debate (MAD), adversarial code reviews, or pull-request deliberation to enforce Response Anonymization, expert persona assignment, and weighted post-hoc voting.

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。

Load for Blazor/MudBlazor UI changes involving forms, dialogs, focus, keyboard navigation, landmarks, ARIA, color contrast, RTL-safe styling, or WCAG 2.2 AA tests; not for backend-only changes.

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。

Throwaway design probe for logic, state models, UI variations, and affordances before production work. Use when the user wants to prototype, sanity-check a model, make something playable, compare UI directions, or explore a design before ln-spec/ln-plan/ln-scope.

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 data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready 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。

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

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。

Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.

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。

Knowledge base from FAA-HDBK-006D (2020), the FAA's System Reliability, Maintainability, and Availability (RMA) Handbook for National Airspace System (NAS) hardware and software. Use for RMA figures of merit (MTBF, MTBO, MTTR, the availability variants), the bathtub curve and probability distributions, the four-stage RMA lifecycle mapped to the FAA Acquisition Management System (AMS), the six-task RMA technical-management/acquisition process, service-thread criticality and the NAS-RD severity-to-target table, the RMA analysis toolbox (RBD, FMEA/FMECA, FTA, Ishikawa, Monte Carlo, Bayesian, FRACAS, ALT, reliability-growth/recovery tests), software reliability (early prediction + reliability growth), and tailorable example RMA requirements for a Program Requirements Document. Scope limits: it is FAA/NAS-specific guidance (not a requirement, by its own statement), centred on hardware-plus-software availability — it brackets out human/service/facility/communications availability, names but does not reproduce the math derivations or external standards (MIL-STDs, IEEE 1633, RTCA DO-178C/DO-278A, SAE JA1011), is thin on detailed worked numerical examples and on safety-case method, and predates any current revision beyond 006D.

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。

Verdichtet gebündelte offene Entscheidungen zu EINEM zusammenhängenden, prosa-basierten Gesamtkonzept auf Basis aller empfohlenen Varianten. Zeigt, wie die Teilsysteme nach der Umsetzung konkret ineinandergreifen, statt den Nutzer durch Dutzende isolierte Einzelfragen zu zwingen. Der Nutzer nimmt das Bild als Ganzes ab oder gibt zielgerichtete Korrekturen, die automatisch in Einzelentscheidungen übersetzt und über decision-briefing Phase 4 ins Register zurückgeschrieben werden. Nutze diesen Skill bei "wie sieht das Ganze aus wenn wir allem zustimmen", "zeichne mir das Gesamtbild", "Gesamtkonzept der Entscheidungen", "decision draw", /decision-draw, "wie greifen die Systeme zusammen". NICHT nutzen, wenn Alternativen pro Einzelpunkt diskutiert werden sollen -- das macht decision-briefing; NICHT für den Abhängigkeitsgraphen -- das macht decision-path; NICHT für strukturierte Einzelblöcke mit Optionen und Pro/Contra (auch bei eng zusammengehörigen Gruppen) -- das macht decision-shot; NICHT zur Herleitung einer einzelnen Entscheidung -- das macht decide.

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。

Writes a DRAFT weekly KPI report from the figures the user pastes or attaches (this week, last week, optionally target and earlier weeks): a metrics table with change versus last week computed only from the stated figures, plain-language notes per metric, and numbered questions on every figure that moved more than the agreed threshold, crossed its target or broke its run. Use when the user asks to "write the weekly KPI report", "turn these numbers into this week's metrics report", "what changed versus last week", "draft the Monday numbers update" or "add commentary to this metrics table". Do not use for a recurring emailed report with a maintained trend sheet, use report-attachment-analyzer instead; for actuals versus budget, use budget-variance-explainer. Drafts for human review; never approves, authorises or signs off.

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。

Reads one spreadsheet, CSV export or pasted table together with the user's question and drafts an insight pack: a data profile, observations cited to named columns and values and framed as leads, caveats, and suggested charts and follow-up analyses. Every figure carries the label "as read, verify in source"; correlation is never stated as causation. Use when the user asks to "profile this spreadsheet", "what does this data say", "find anomalies in this extract", "give me a first read on this CSV" or "summarise this table". Do not use for a purchase order log or spend extract, use purchase-order-anomaly-review instead; for actuals versus budget, use budget-variance-explainer instead; for a report that arrives by email on a schedule, use report-attachment-analyzer instead. Drafts for human review; never approves, authorises or signs off.

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。

Reads a cash forecast (spreadsheet, model export, pasted table or the narrative that accompanies it) and returns a DRAFT assumptions sheet: every assumption the forecast rests on, stated or implied by the figures, with its value as read, its location in the model, the cash lines it drives, its stated source or "not stated", the evidence that would test it, an owner, and one neutral challenge question, so a reviewer can work through the assumptions one by one. Use when the user asks to "list the assumptions behind this cash forecast", "what is this cash flow forecast assuming", "prepare the challenge questions for the treasury review", "pull out the drivers of the 13-week cash forecast" or "build an assumptions register for the liquidity plan". Do not use for actuals against budget by line, use budget-variance-explainer instead; do not use for the case behind a capital request, use capex-request-pack instead. Drafts for human review; never approves, authorises or signs off.

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。

The SuperCollider language itself - function blocks and argument syntax, UGen rate methods (.ar/.kr/.ir), multi-channel expansion, var scoping, ~environment variables, the class library's shape, and the error messages that mean a specific mistake. Written for an agent that knows audio DSP but not this language.

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 a SuperCollider piece up while it plays - start a sound, change it in place, layer it, and stop it cleanly - using the dsh-supercollider tools (sc_exec, sc_nodes, sc_play, sc_stop-via-sc_exec) and the idioms that make a session survive (Ndef, ProxySpace, Pbind, ~variables, s.freeAll).

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。