'regression' 검색 결과

Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.

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• Check whether price and return series are statistically stable
• Test long-term relationships between two assets
• Diagnose regression problems in factor research

Turn feature requirements into test plans, step-by-step manual tests, regression suites for checking existing features, and reproducible bug reports. The skill also supports comparing implemented interfaces with Figma designs and documenting visual differences.

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• Plan tests for a new feature
• Write manual checkout test cases
• Organize regression checks before a release

Validates skill files and registration consistency, then checks project tests and system health. Compares failures with recent changes to distinguish regressions, outdated tests, intermittent failures, and infrastructure issues, producing a report that tracks results over time.

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• Validating definitions and registration for new skills
• Running daily tests and system health checks
• Classifying failures after code changes

Test user journeys affected by a branch’s changes in a real browser. Check correctness and the experience for intended users, fix small defects with regression tests, and commit fixes alongside a report documenting results and unresolved decisions.

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• Check user-facing changes before a PR is merged
• Test complete user journeys affected by a branch
• Fix small defects and add regression tests

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

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• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies

Run MNN checks and regression tests on a host computer or Android device, with stage summaries and failure logs. A separate workflow benchmarks LLM prompt processing and token generation on real iPhones and iPads, including branch comparisons.

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• Run static checks and host regression tests
• Verify selected backends on an Android device
• Benchmark LLM speed and compare branches on iPhone or iPad

Classify failures in CI, automated checks run during development, to distinguish code regressions, temporary infrastructure problems, security test failures, and inconsistent results. Record each failure's category, suggested next action, and attempt count in a structured report.

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• Sort failed CI checks by likely cause
• Identify small regressions that may qualify for automatic fixes
• Refer infrastructure and security failures to a human

Turn a Bifrost PR or GitHub issue into API regression tests that catch recurring bugs. Review existing coverage, add cases to the Postman collection, and validate collection processing and provider filtering.

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• Add regression coverage for a merged PR
• Turn an issue's reproduction steps into API tests
• Test streaming and non-streaming responses

Investigate failing macOS tests in Xcode or SwiftPM by running a focused test scope, classifying errors, and separating setup issues from code regressions. Get a concise explanation of likely causes and the next verification step.

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• Narrowing down failing macOS tests
• Distinguishing setup issues from code regressions
• Investigating assertions and crashes

Investigate bugs by tracing symptoms through code and recent changes, reproducing failures, and testing a specific cause before fixing it. Produce a focused correction, a regression test to catch recurrence, and an evidence-based debugging report.

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• Investigate errors that keep returning after fixes
• Trace a regression through recent changes
• Analyze failures that occur only in certain environments

Review regression coverage and test quality in Apache Pinot when behavior or tests change. Use it to assess whether code changes and bug fixes have relevant tests, including paths that handle different types or null values.

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• Reviewing added or modified test files
• Checking production changes without test updates
• Checking regression tests for bug fixes

Add and maintain Nexent Python unit tests with pytest. Isolate external dependencies, cover successful behavior, boundaries, and errors, and add regression assertions that detect fixed bugs. Review existing coverage and record test results separately from formal acceptance cases.

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• Add unit coverage for changed product behavior
• Write regression assertions for bug fixes
• Test error handling without external service calls

Econometrics skill for creating publication-quality LaTeX regression and summary tables. Activates when the user asks about: "regression table", "LaTeX table", "esttab", "stargazer", "modelsummary", "publication table", "format results", "multi-panel table", "journal table", "export regression results", "table formatting", "回归表格", "LaTeX表格", "结果导出", "论文表格", "回归结果格式化", "多模型表格"

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Econometrics skill for Regression Discontinuity Design (RDD). Activates when the user asks about: "regression discontinuity", "RDD", "RD design", "sharp RDD", "fuzzy RDD", "running variable", "forcing variable", "cutoff", "bandwidth selection", "local linear regression", "McCrary test", "density test", "RDROBUST", "continuity assumption", "donut hole RDD", "geographic RDD", "断点回归", "回归不连续", "运行变量", "截断值", "带宽选择", "精确断点", "模糊断点", "密度检验", "局部线性回归"

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STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me code for [technique]" — including: entropy balancing, propensity score matching (PSM), difference-in-differences (DiD), regression discontinuity (RDD), instrumental variables (IV), event studies (CAR/BHAR), survival analysis, Fama-MacBeth regressions, bootstrap, quantile regression, reghdfe/xtreg/areg, clustering standard errors, fixed effects, esttab/outreg2 table formatting, winsorization, leads/lags. Users can specify their variables (e.g., treatment, outcomes, controls) and receive adapted syntax. NOTE: This skill provides code patterns from published papers, not research design advice.

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Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.

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SEO drift monitoring — snapshot a site's SEO state and detect regressions over time. Captures a baseline (rankings/positions, indexed page count, titles & meta descriptions, canonical/robots directives, schema presence, key on-page elements) and on later runs diffs against it to surface what changed: ranking drops, pages that fell out of the index, titles/metas that were accidentally overwritten (a CMS/redeploy classic), canonicals or noindex flipped, schema that disappeared. Use this skill when the user wants to monitor SEO over time, catch regressions after a site change / migration / redeploy, set a baseline, diff against a previous state, or asks "what changed on my site's SEO" or "did my redesign break SEO". Trigger on: "SEO drift", "SEO monitoring", "track SEO over time", "did my site change break SEO", "after migration SEO", "SEO regression", "baseline my SEO", "compare SEO to last month", "my titles changed", "pages fell out of the index". For a one-time full audit use /seo-analysis.

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End-to-end R data analysis pipeline — exploration → cleaning → regression → publication-ready tables and figures. Use when user says "analyze this dataset", "run a regression on X", "explore this CSV", "full analysis workflow", "get me summary stats and a regression", or points at a `.csv`/`.rds`/`.dta` and asks for empirical results. Produces numbered R scripts in `scripts/R/` and outputs to `output/`.

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Compare this period's reliability against the prior period using Agent Monitor data — error rate (APIError/total) and tool-failure rate (PreToolUse→PostToolUse gap) — flag any regression where reliability got worse, and optionally wire a persistent alert rule so the dashboard catches the next regression automatically. Use when checking whether reliability degraded.

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Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

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Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

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Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-context.

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Chromatic is a visual testing service from the Storybook team that snapshots every story in a cloud browser and shows visual diffs for review on each pull request. Use when the user wants visual regression testing with Storybook, or mentions "chromatic," "visual regression," "Storybook testing," "UI review," "visual diff," or "component snapshot testing." For general screenshot comparison, see percy.

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Generate unit, integration, E2E, and visual regression tests following the Testing Trophy methodology (80% integration). Covers Vitest/Jest, Testing Library, Playwright, MSW for API mocking, snapshot strategy, visual regression (Chromatic/Percy/Playwright), test factories with Faker, and CI sharding. Use when user asks to write tests, set up testing framework, mock API/dependencies, improve coverage, or add visual regression. Do NOT use for performance/load testing, production monitoring, or type testing (covered by TypeScript).

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