「regression」の検索結果

Review code changes on this card for likely bugs, regressions, and missed edges

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。

Playwright-powered browser automation and E2E testing for SolanaOS Hub. Covers test configuration, agent-driven test generation (planner/generator/healer), NanoHub route testing, Convex API validation, wallet flow testing, IPFS Hub verification, and CI/CD integration. Use when asked about E2E tests, browser automation, Playwright setup, NanoHub testing, visual regression, or Hub route verification.

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 or debug application use cases through their public Action API, including persistence, authorization failures, atomic rollback, and post-commit reactions. Use when adding regression tests for business operations.

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 or fix domain rules, state transitions, value objects, and domain services directly. Use for business-rule regression tests, rejected transitions, and model behavior without HTTP.

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-driven development with red-green-refactor and behavior-focused tests. Use when the user explicitly wants test-first work, a regression test before a fix, or integration tests that drive a feature through a public interface.

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。

Disciplined diagnosis loop for hard bugs, regressions, flaky failures, and performance problems with unclear cause. Use for root-cause debugging after a concrete symptom exists; do not use for routine implementation or speculative 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。

Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical 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。

Post-fix verification — re-scan the repo, gate on `--exploits weaponized --severity high`, recheck the specific CVE against the new installed version, write the verdict to `.vulnetix/memory.yaml`. Use when confirming a fix landed, validating a version bump did not introduce regressions, or producing a clean-scan attestation for compliance.

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 diagnosing a bug, exception, regression, wrong result, failing flow, or unknown root cause in repository code using callers/callees, execution flows, recent changes, and impact analysis; not for implementing a known fix without 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。

Load when asked to review a local diff/change set for correctness, regressions, blast radius, affected flows, and missing tests using the knowledge graph; use `review-pr` for intent/checklist/merge-gate evidence.

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。

Called by /plot to upgrade regression and summary tables to top-journal standards.

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 panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

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 Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

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。

Comprehensive results analysis for empirical research: generate publication-quality descriptive statistics and balance tables, interpret regression coefficients with economic magnitude and effect sizes, assess identification assumption diagnostics, and produce structured results memos. Use when asked to create summary statistics, Table 1, balance tests, interpret results, assess economic significance, or write results narratives.

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。

Review someone else's pull request by measuring which of its own guards its own test suite actually catches, via a small mutation matrix. Use when reviewing a PR, checking whether a regression test is real, or asked 'is this change actually tested?', 'does this test do anything', 'mutation test this'.

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 after substantial web projects or repeated design defects to convert evidence into reusable skill improvements through root-cause analysis, regression scenarios, and controlled proposals without silently changing the core OS.

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 website needs rendered screenshot review, Playwright visual regression, multi-viewport checks, horizontal overflow detection, console-error checks, focus/state inspection, or a repeatable visual QA evidence set.

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 or interpret bounded multi-page Unlighthouse audits for local Lighthouse evidence. Use for site-wide performance, accessibility, best-practice, and SEO regression checks when field data or API quotas are insufficient.

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 the user asks to "track where my emails are actually landing after I send", "read my seed-list inbox vs spam vs promotions results", "trend my Gmail Postmaster / Microsoft SNDS reputation", or "did placement drop after my last send"; produces a per-provider inbox/spam/promotions placement read, a domain/IP reputation trend from Postmaster + SNDS, a send-over-send delta with named regressions, and a reusable SEND-S placement snapshot on your own exported telemetry. Not for the pre-send SPF/DKIM/DMARC auth pre-flight — use deliverability-qa; not for computing the EQS or running the vetoes — use email-quality-auditor. 邮件收件箱落点监测/收件箱vs垃圾邮件/Postmaster声誉趋势

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。

コミット/PR の「全部置換した」「-N%削減」等の完了主張を grep で反証できる残骸や best/typical/worst 試算で検証し、抽出・集約リファクタでは並列度(Promise.all)/fast-path/遅延評価の性能特性退行と、Map/Set 集約キーの cross-kind 衝突による検出漏れも監査する

Ensure code changes keep logs/metrics/traces useful for debugging failures and 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。

Use only when the user explicitly asks for TDD, a failing test, or a regression test, OR when the bug has an obvious cheap local test target. Skip when the test path is unclear, expensive, integration-heavy, or not requested.

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 for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime 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。