'regression' 검색 결과

Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.

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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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Use when autonomously improving a measurable scalar metric through bounded, git-tracked iterations: modify one scoped change, verify, keep or revert. Triggers on /mk:loop, 'optimize coverage/bundle size/lint count', 'iterate until the metric improves'. NOT for subjective cleanup (see mk:cook), known-root-cause bugs (see mk:fix), behavioral grading (see mk:evaluate), or shipping (see mk:ship).

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Reflect subskill for simmer. Records iteration results in trajectory table, tracks best candidate, handles regression rollback, and passes ASI forward to the next round. Supports both single-file and workspace modes. Do not invoke directly — called by simmer orchestrator after each judge round.

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Review code changes for correctness defects, regressions, security risks, data-integrity problems, concurrency hazards, and missing meaningful tests. Use for pull requests, branches, commits, patches, or working-tree reviews; do not use for implementation unless the user separately requests fixes.

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Design, implement, and evaluate risk-based software tests across unit, integration, contract, end-to-end, and regression layers. Use when adding tests, reproducing bugs, improving coverage, diagnosing flaky tests, or defining a test strategy; do not use to change production behavior unless the user also requests implementation.

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Optimizes application performance. Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing.

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Analyze web performance URLs with the PerfGraph CLI and interpret its deterministic reports, causal chains, and prioritized remediation data. Use when the request names perfgraph, or asks to measure or diagnose loading speed, Core Web Vitals, Lighthouse performance, bottlenecks, or web performance regressions.

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Use when the user reports a hard bug, unexpected failure, or performance regression that needs root-cause analysis.

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Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.

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Review local git changes before opening a PR or merging. First review all local tracked and untracked files; if none exist, review the current branch against a requested base branch or the Git Town parent branch. Use zero to three review subagents only when risk, diff size, language specificity, or uncertainty justifies the token cost, then report high-confidence bugs, regressions, risky assumptions, and missing tests. Use when the user asks for code review, local review, branch sanity check, pre-PR review, or review of work-in-progress changes.

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Bug hunter. Reproduces bugs with failing tests (RED), then fixes with TDD. Accepts a prompt, issue URL, or bug description. Use when: bugfix, fix bug, debug, broken, regression, failing, doesn't work, fix this.

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Use when writing Playwright E2E tests — browser automation, visual regression testing, Page Objects, fixtures, and reliable test patterns.

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Use when a diff may lack tests — missing assertions, uncovered branches, over-mocking, no regression test for a bug fix — dispatched by /review-changes, /do-and-judge, /judge, even without 'tests'.

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Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

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[Implementation] Use when a workflow step or the user asks for root-cause fixes with regression coverage. --target={ci|issue|logs|review|test|types|ui} scopes the issue.

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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.

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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.

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Re-audit code changes to identify side effects, regression risks, and unhandled edge cases before merging or deploying. Use after a change is complete and before merging to a shared branch, before deploying a hotfix, after complex multi-module refactoring, or when a previous review may have missed side effects in async, state-mutating, or security-sensitive code paths.

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Automate terminal UI (TUI) apps with agent-tui for testing, inspection, demos, and scripted interactions. Use when automating CLI/TUI flows, regression testing terminal apps, verifying interactive behavior, or extracting structured text from terminal UIs. Also use when asked what agent-tui is, how it works, or to demo it. Do not use for web browsers, GUI apps, or non-terminal interfaces.

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Implement minimal network events and component state with authority, conversion, dirtying, and regression coverage.

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Reproduce failures, trace authoritative flow, prove the broken assumption, and add targeted regression coverage.

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Load this skill when configuring or reviewing CI/CD pipelines, GitHub Actions workflows, or automated testing setups. Ensures accessibility regressions are caught before code reaches production by enforcing quality gates, structured reporting, and layered automated + manual testing across all pages and user preferences.

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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.

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