「aria」の検索結果

Connect to and query WRDS (Wharton Research Data Services) from any research project. Use this skill whenever the user needs to download, query, or explore data from WRDS — including Compustat, CRSP, FactSet, I/B/E/S, or any other WRDS-hosted database. Also trigger when the user mentions WRDS tables, WRDS libraries, or wants to look up variable definitions or coverage in WRDS datasets. Do NOT trigger for general SQL or database questions unrelated to WRDS.

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 whenever the user asks to run Stata commands, estimate econometric models, work with .dta files, run a .do file, generate Stata output, or do any statistical analysis where Stata is involved. Also trigger when the user mentions Stata variables, Stata syntax, or econometric tasks where Stata is the natural tool, including regressions, IV estimation, diff-in-diff, RDD, panel data, clustering, summary statistics, and margins. Stata runs through pystata on StataNow 19.5 BE; configure once with stata_setup, then drive everything with stata.run() and exchange data directly with pandas. Prefer this skill over subprocess calls or .do-file shelling for Stata work, including cases where the user does not say pystata.

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。

Style every matplotlib figure like the Stata 18/19 default (stcolor) scheme — Arial embedded as TrueType, white background, recessive light-gray grid below the data, no top/right spines, unframed legends, and the validated blue/red/gray palette. Use whenever a task generates or restyles charts, plots, or figures for papers, reports, or slides, even if the user doesn't mention Stata — this is the house style for all publication figures. Also use when asked to make figures "look like Stata", match the stcolor scheme, or restyle existing matplotlib output.

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。

Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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。

Structured 6-phase debugging. Build feedback loop first, reproduce deterministically, hypothesize with ranked falsifiable theories, instrument one variable at a time, fix with regression test, cleanup. Use when a bug exists, tests fail unexpectedly, or behavior is wrong and cause is unknown.

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。

Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing.

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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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。

Scan all GDDs against the entity registry to detect cross-document inconsistencies: same entity with different stats, same item with different values, same formula with different variables. Grep-first approach — reads registry then targets only conflicting GDD sections rather than full document reads.

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。

Behavior Designer patterns for AI behavior trees including task creation, shared variables, conditionals, and debugging.

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。

Unity 6 multiplayer and networking guide. Use when building multiplayer games, working with Netcode for GameObjects, NetworkManager, NetworkObject, NetworkBehaviour, RPCs (ServerRpc, ClientRpc), NetworkVariables, or Unity multiplayer services (Relay, Lobby). Covers client-server architecture, state synchronization, and scene management. Based on Unity 6.3 LTS documentation.

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 creating, configuring, reviewing, or debugging Vite-based frontend projects, vite.config.ts, env variables, dev server proxy, HMR, plugin ordering, library mode, dependency pre-bundling, bundle splitting, or Vite build performance. Use framework-specific skills for React/Vue component architecture; Chinese triggers include Vite configuration, vite.config, Vite build, Vite performance.

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。

用于审查或改进前端无障碍、语义结构、键盘支持、焦点管理、ARIA 标签、屏幕阅读器行为、WCAG 2.2 问题、触控无障碍或辅助技术回归；中文触发词包括 无障碍、accessibility、a11y、WCAG、屏幕阅读器。

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 behavior-preserving frontend refactoring focuses on variable meaning, field naming, derived state, or reference/value identity, and the selected technique needs family-specific mechanics, risks, and JavaScript/TypeScript/React/Vue adaptation.

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 reviewing or improving frontend accessibility, semantic structure, keyboard support, focus management, ARIA labels, screen reader behavior, WCAG 2.2 issues, touch accessibility, or assistive-technology regressions; Chinese triggers include accessibility, accessibility, a11y, WCAG, screen reader.

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 optimizing or reviewing large lists, virtual scrolling, windowing, react-window, TanStack Virtual, variable-height rows, dynamic measurement, infinite scroll, grid virtualization, or scroll performance; Chinese triggers include virtual lists, large list optimization, scroll performance.

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 synchronizing frontend project documentation with source-of-truth files such as package.json, lockfiles, config, env examples, routes, API clients or server routes, schemas, components, tests, CI, build/deploy config, ADRs, changelogs, or migration notes. Do not use for general prose polishing only; Chinese triggers include document synchronization, update README, docs sync, environment variable documentation, command list, API Document synchronization.

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 behavior-preserving frontend refactoring focuses on conditional complexity, repeated branches, guards, special cases, or internal invariants, and the selected technique needs family-specific mechanics, risks, and JavaScript/TypeScript/React/Vue adaptation.

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。

OpenUI generative UI with Python FastAPI backend. OpenAI and Anthropic SDK variants.

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 a task needs the judgment of an Embedded/Firmware Engineer — analyzing worst-case execution time for a hard real-time deadline, choosing static over dynamic memory allocation to avoid fragmentation on a long-uptime device, keeping an interrupt service routine minimal and deferring work to the main loop, synchronizing a variable shared between an ISR and main context to prevent a torn read, or verifying a hardware register interaction against the datasheet's exact timing rather than observed bench behavior.

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 a task needs the judgment of an Economist — estimating a demand elasticity or causal treatment effect from observational or experimental data, building a cost-benefit or welfare analysis for a policy or pricing decision, forecasting a macro or market aggregate under stated assumptions, or stress-testing whether a "the data shows X" claim survives a confounding-variable check.

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 a task needs the judgment of an Environmental Health Specialist (Registered Sanitarian) — classifying a food-establishment temperature violation as critical vs. priority-foundation, determining pool closure and hyperchlorination protocol after a fecal/vomit incident, evaluating a septic percolation test against site-limiting conditions, deciding whether a complaint pattern meets the threshold for an outbreak investigation, or issuing an imminent-health-hazard closure.

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 a task needs the judgment of an Epidemiologist — investigating a disease outbreak and identifying its source, designing a case-control or cohort study to test an exposure hypothesis, calculating attack rates/odds ratios/relative risk from surveillance data, evaluating whether a surveillance system is sensitive and timely enough to catch a real signal, or judging whether an observed case cluster is a true outbreak or expected background variation.

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 a task needs the judgment of a Cutting/Slicing Machine Operator — calculating a portion weight overweight margin from measured weight variability to minimize giveaway while staying compliant, verifying a thickness setting against a new product lot's actual density rather than reusing a prior lot's setting, diagnosing declining cut quality as a blade-condition issue rather than compensating with thickness/speed, or adapting cut pattern to irregular input to improve yield.

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 a task needs the judgment of a Crushing/Grinding Machine Operator — recognizing that classifier cut point, not mill residence time alone, determines what reports to finished product in a closed-circuit grinding operation, monitoring circulating load as a distinct diagnostic variable, matching grinding media size to the target size reduction, or verifying product size via actual sieve/laser analysis rather than mill parameters.

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。