「regress」の検索結果

Claude adapter for PlainTab test-first feature work, bug fixes, regression scripts, and behavior-focused validation.

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 implementing PlainTab features or bug fixes with test-first development, red-green-refactor, regression scripts, browser checks, or behavior-focused validation before production code 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。

Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends -- using Monte Carlo's cross-platform observability. Uses a tiered investigation approach: discover problems, bridge to affected tables, then drill into root causes. Activates when a user asks about slow pipelines, expensive queries, or performance regressions.

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。

Add durable end-to-end tests for user/API-visible behavior. Detect or scaffold the E2E framework, write tests, run the app, and store evidence. Use for E2E, Playwright/Cypress, regression tests, or quality gates. Not exploratory QA.

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。

Adding edge-case tests, repairing flaky tests, and improving coverage. Use when test gaps need filling or regressions need guarding. Supports JS/TS, Python, Go, Rust, and Java.

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。

Investigating git history, analyzing regression root causes, and performing code archaeology. Time-travels through commits to uncover truth. Use for git history investigation.

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。

Authoring Storybook stories, component catalogs, and Visual Regression integration (CSF 3.0/Factories, Storybook 10 ESM-only, React Cosmos). Use when building a component catalog.

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。

Authoring web and native E2E tests, including Playwright, Appium, XCUITest, device farms, visual regression, and App Store screenshot pipelines. Not for unit/load tests.

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。

Work a defect back to its cause before changing anything, and close it with a regression test. Use when a claimed task's criterion names a defect in a repository with a .ank/ directory.

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。

Performance regression detection. (gstack)

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 code review for bugs, regressions, tests, and standards. Use before PRs or when asked for review; interactive mode can fix locally, while mode:agent reports only for pipeline callers.

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。

[BETA] Hands-off end-to-end branch dogfood pass with browser testing, auto-fixes, regression tests, and fix commits.

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。

Diagnosis loop for bugs and failing behavior. Use for errors, stack traces, regressions, failed tests, issue-tracker bugs, stuck investigations after failed fixes, or asks to debug/fix a bug.

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。

Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.

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。

Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.

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 when a user needs to inspect, score, repair, or regression-test academic paper formatting in DOCX files, especially thesis, journal, or conference documents with a separate format specification. The skill guides Codex through local-first document handling, rule extraction, formatting repair, report review, and contribution-safe validation.

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 Spring Boot 4 / Java 17+ code with concrete files or diffs — pull requests, modules, or pasted Java/Spring sources — for migration risks, architecture boundary leaks, JSpecify null-safety gaps, security flaws, performance regressions, or Spring Data pitfalls. Not for Kotlin-only code, non-Spring frameworks, or generic review advice without code context.

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。

Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "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。

Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "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。

Expert guide for Prompt Engineering, Chain-of-Thought, few-shot prompting, structured output, prompt injection defense, and automated AI evaluations & regression benchmarking (Promptfoo, DeepEval) / Panduan ahli rekayasa prompt dan evaluasi otomatis AI.

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。

Answer a "how do I …" question about the platform with a Gherkin scenario on @datagrok-libraries/bdd that is filmed into a how-to video (grok-bdd guide), so the answer is demonstrated, sent as a video plus numbered steps, and kept as a regression test; the same scenarios, filmed in help mode, are the GIFs of the help pages

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。

Add a viewer, configure a viewer, change viewer options, find viewer, close viewer, view a scatter plot, bar chart, histogram, line chart, box plot, pie chart, heat map, correlation plot, 3D scatter, trellis, density plot, statistics, on a Datagrok TableView via the datagrok_exec tool. Use whenever the user asks to plot, chart, visualize, show a graph, draw a distribution, color by a column, swap a viewer's axis, toggle a legend / regression line / log scale, replace one viewer with another, close every chart, reset the view to just the grid, or find an existing viewer by type. Plugin viewers like "Chem space", "sequence space", "activity cliffs" are NOT viewer types — they're registered functions — route those to `grok.functions.call`. Does NOT cover filtering (separate skill `datagrok-filtering`), selection (`datagrok-selection`), grid cell rendering (`datagrok-grid-customization`), layout save/restore, or custom-viewer authoring.

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。

Real-world problem formulation, mathematical abstraction, and applied mathematics for translating between practical problems and mathematical frameworks. Covers the modeling cycle (problem identification, assumptions, formulation, analysis, validation, interpretation), Polya's framework adapted for modeling, common model types (linear, exponential, logistic, periodic, power-law), dimensional analysis (Buckingham Pi theorem), optimization (linear programming, gradient descent, constraint satisfaction), probability models (Markov chains, queuing theory, Monte Carlo simulation), statistical modeling (regression, hypothesis testing, model selection), model criticism (overfitting, underfitting, sensitivity analysis), and real-world case studies. Use when formulating mathematical models, performing dimensional analysis, optimizing systems, running simulations, or evaluating model validity.

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。

Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from problem framing through deployment. Covers classification, regression, clustering, dimensionality reduction, regularization, hyperparameter tuning, and evaluation metrics. Use when building predictive models, selecting algorithms, or understanding the machine learning 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。