「egress」の検索結果

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。

Complete methodology for computing publication-quality cumulative abnormal returns with proper event-study test statistics, matching the robustness of Kaspereit's eventstudy2 for Stata. Covers dateline construction, event-date mapping, estimation and event windows, thin-trading adjustment, OLS with Theil prediction error correction, abnormal return computation, CAR/CAAR/AAR accumulation, boundary contamination guards, and common tests such as Patell, BMP, Kolari-Pynnonen, generalized sign, Wilcoxon, and GRANK-T. Use when the user mentions abnormal returns, event windows, market-model regressions, CARs, CAAR, AAR, eventstudy2, thin trading, trade-to-trade returns, or event-study test statistics.

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。

Convert between LaTeX and Microsoft Word for academic manuscripts in either direction, and assemble .tex from mixed sources. Fires on: converting .tex/LaTeX to Word/.docx ("tex to docx", "latex to word", "tex2docx", "convert to word", "pandoc convert"); converting .docx manuscripts to LaTeX for editing and back ("convert to latex", "manuscript", "footnotes", "reference doc", the docx-to-tex-to-docx round-trip / academic paper editing pipeline); high-fidelity delivery where plain pandoc loses tables, mangles cross-references, or fails on custom macros — booktabs/regression tables, OMML equations, cleveref, \estauto, \@@input, \thanks, TikZ, native Word tables, longtable, siunitx; and building .tex from mixed PDF/docx/LLM-generated sources. Replaces and reroutes the retired skills manuscript-editing-template-latex, latex-to-docx-fidelity, tex2docx, and latex-from-mixed-sources.

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。

N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.

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。

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 authoring or reviewing frontend unit, component, or light integration tests close to UI code, including React Testing Library, Vue Test Utils, hooks/composables, props/emits, callbacks, accessible queries, user-event interactions, mocks, loading/error/empty states, and regression coverage. For layer planning, real-browser journeys, or existing validation failures, choose the matching testing or validation workflow first; Chinese triggers include component testing, Component unit testing, unit testing, lightweight integration testing.

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 setting up or reviewing Storybook component documentation, design-system presentation, isolated component state previews, stories, addons, decorators, MDX docs, component-state interaction checks, visual baselines, or Chromatic. For real-browser cross-page journeys or broader coverage planning, choose the matching testing workflow first; Chinese triggers include Storybook, component documentation, Design System, visual regression.

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 TypeScript project standards across frontend apps, libraries, SDKs, CLIs, monorepo packages, tsconfig, strictness, module/moduleResolution, path aliases, project references, declaration files, package exports, public API types, DTOs, advanced generics, discriminated unions, type guards, type narrowing, or type-level regressions. Prefer framework project skills for React/Vue/Next/Nuxt component architecture; Chinese triggers include TypeScript project specification, TS project specification, TypeScript type safety, type modeling, generics, discriminative unions, type narrowing, tsconfig, declaration files.

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 planning or reviewing a frontend testing strategy, selecting the right test layer by risk, mapping coverage across static checks, unit tests, component tests, integration tests, E2E, Storybook/visual regression, a11y, security, performance, or CI gates. Do not use to write individual component/E2E tests or merely run existing validation commands; Chinese triggers include test strategy, test layering, test plan, coverage matrix.

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, maintaining, debugging, or reviewing real-browser end-to-end tests with Playwright or Cypress, including Page Object models, CI artifacts, traces, flaky tests, cross-page visual regression, and critical user journeys such as login, payment, permissions, or CRUD. For layer planning or tests close to UI components, choose the matching testing workflow first; Chinese triggers include E2E, end-to-end testing, Playwright, Cypress.

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 Equal Opportunity Representative or Officer — running an adverse-impact analysis on hiring or promotion selection rates, conducting an availability analysis to set affirmative action placement goals, evaluating a compensation-equity regression for unexplained pay gaps, or preparing an affirmative action plan (AAP) for an OFCCP audit.

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 Construction and Building Inspector — deciding whether a phase of work passes a hold-point inspection (foundation, framing, rough-in, insulation, final) or gets red-tagged, writing a correction notice that will hold up on appeal, resolving a conflict between the approved plans and field conditions, applying a specific IRC/IBC section to a borderline condition (egress, smoke/CO detector placement, GFCI/AFCI locations), or deciding whether schedule pressure from a contractor changes the call.

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 evaluation frameworks for LLM systems — harness design, graders, datasets, regression tracking, and human-in-the-loop eval. Use when you need systematic measurement of model or agent quality.

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。

Test frontend code: unit, component, E2E, visual regression, and accessibility tests with a sane pyramid. Use for reliable UI quality.

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。

Statistical methods for biology and medicine — study design, hypothesis testing, regression, and survival analysis.

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 AI agents rigorously — task benchmarks, success criteria, failure taxonomy, cost/latency tracking, and regression testing. Use when you need to know whether an agent actually works, not whether it demos well.

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。

Set up Muse's built-in Tailscale connector, join a tailnet or Headscale network, check status, and reach private machines through the TCP tunnel proxy. Read for Tailscale, VPN, MagicDNS, network egress, exit-node, or browser routing questions and supported limits.

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 rehabilitation recovery tracking system -- evaluate standardized outcome instruments (FIM, Barthel Index, SF-36, DASH, LEFS, PROMIS), functional assessment scoring accuracy, SMART goal and milestone tracking, regression detection with alert workflows, pain scale calibration (NRS.

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 public sector and government resource allocation systems for budget optimization algorithms (zero-based, incremental, performance-based), service demand forecasting (ARIMA, Prophet, regression), equity-based distribution scoring (CDC SVI, environmental justice indices.

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 litigation analytics and case outcome prediction systems -- ML outcome models (logistic regression, gradient boosting, neural nets with temporal train/test splits), settlement range modeling (Monte Carlo simulation, comparable case matching, BATNA analysis).

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 and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta, remote egress requires explicit consent, and unavailable reranking falls back deterministically.

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。

Run the Yuzu Erlang gateway eunit test suite correctly. Sources the Erlang toolchain, uses --dir to work around rebar3 auto-discovery bug (#337), and explains how to interpret the output. Use when the user asks to run gateway unit tests, verify a gateway change, check for regressions in eunit, or chase an eunit flake.

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。