「regression」の検索結果

Maintain and extend the open-websearch MCP server. Trigger when changing search engines, proxy or TLS behavior, tool registration, live parsing behavior, or compatibility and regression behavior inside this repository.

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。

Evaluation harness for testing agent and skill quality through structured benchmarks, regression tests, and quality scoring.

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 whenever you run statistical analysis for the social sciences (regression, hypothesis tests, econometrics) or read Stata (.dta) / SPSS (.sav) data in this workspace. Enforces an execute-don't-interpret boundary (surface estimates, don't volunteer causal claims), checks the analysis against a preregistration plan for HARKing, verifies reproducible seeds, and reproduces .dta/.sav estimates via R. Flags integrity risks; never certifies the analysis is sound.

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。

Vet a perf measurement (limiter, tuning, limits, errors, repeatability, relevance, and whether the work happened) before you report or act on it. Use when you run a benchmark or report a speedup or regression you measured.

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。

Apply before you trust, report, or act on a number you measured: a speedup, a regression, a throughput, a latency, or an eval result. Find what limits it, and rule out that it measured something other than the work you think.

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。

Rigorous visual validation expert specializing in UI testing, design system compliance, and accessibility verification. Masters screenshot analysis, visual regression testing, and component validation. Use PROACTIVELY to verify UI modifications have achieved their intended goals through comprehensive visual 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。

Root-cause a failing or wrong empirical result with a disciplined reproduce → minimise → hypothesise → instrument → fix loop, instead of guessing-and-poking. Use when the user says "why is my regression wrong", "this number changed", "my script errors out", "the result won't reproduce", "debug this", "this estimate looks wrong", or "it worked yesterday". Tuned for research code (R/Stata/Python): type coercion, NA/merge blow-ups, factor levels, clustering/SE choices, weighting, collinearity/convergence, seeds, package-version drift. Use `--no-fix` to localize the root cause without editing shared or load-bearing 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。

Compare two implementations of the same thing — a port (R↔Python↔Stata), a reimplementation, a replication package, a refactor, or a new version against the old — so that agreement means something. Freeze inputs first, inventory every expected output, test the comparator itself, compare every channel (not just the headline number), and give each divergence a stable ID and a smallest witness. Use for cross-language parity, replication, upgrade/regression gates, or whenever "the numbers match" is about to license a claim.

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。

AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).

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 /debug to find and fix a bug's root cause: a test failing for an unclear reason, /check verify finding a failure, or behavior being wrong. Runs a reproduce, localize, hypothesize, test, fix, verify loop, makes the minimal fix, and hands a regression test to /test. No features, no extra refactors.

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。

Find and fix a bug in the octane monorepo. Use when given a failing test, a regression, or a report that behavior differs from expectation, including deciding whether a React mismatch is an intentional divergence.

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。

Tests/benchmarks a third-party LLM endpoint (OpenAI- or Anthropic-compatible): availability, fidelity, speed, concurrency, protocol compliance, quality regression. Use before hand-rolling a curl loop, when onboarding a provider, or debugging "system prompt 不生效" / a tok/s claim — 测评/压测一个模型或渠道. Not for TTS/voice-clone supplier eval (use the audio skill).

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 a blameless postmortem for an incident caused by an AI agent or LLM feature — hallucinated facts shipped to users, runaway tool use, prompt injection, cost blowouts, or wrong actions taken autonomously. Use when asked to write up an AI incident, analyse why an agent did something wrong, or produce corrective actions after an LLM failure. Produces a structured postmortem with trace reconstruction, a root-cause layer analysis, and corrective actions including a permanent regression case. For non-AI production incidents use incident-postmortem.

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 a reproducible failure, fix its cause, and verify the regression. Use for crashes, incorrect behavior, and failing 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。

Write tests that pin real behavior instead of implementation details, config values, or lucky samples. Use when adding tests for new behavior, writing a regression test, fixing a brittle or flaky test, reviewing a test diff, or when a test breaks after a refactor or config change that didn't change 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。

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

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 before prescription: reproduce, hypothesize, isolate, fix root cause, add a regression test; refactor mode maps deps, coupling and blast radius. Triggers: bug, error, fix, broken, not working, crashed, stack trace, exception, refactor, restructure, coupling, dependency map.

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。

Simplify code, plans and systems without losing required outcomes; delete machinery that earns nothing; for code, AST-measured complexity with regression gates. Triggers: simplify, refactor, complexity, overengineered, dead code, useless guard.

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 optimizing the performance of an existing native scalar expression in the datafusion-comet-spark-expr crate (native/spark-expr/) — casts, string/JSON/array/math kernels that run per-row or per-batch. Covers benchmarking, keeping output bit-identical, and the no-regression gate. Not for adding new expressions (use implement-comet-expression) or wiring upstream functions (use wire-datafusion-function).

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 building or revising exhibits for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — impulse-response figures, fan charts, model-fit overlays, and regression/moment tables — to AEA house standards and macro conventions. Formatting and clarity; it does not generate the underlying estimates.

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 causal identification argument is the bottleneck for an American Economic Journal: Applied Economics (AEJ: Applied) manuscript — RCT, difference-in-differences/event study, regression discontinuity, IV, or shift-share. Stress-tests the data-to-causal-estimate mapping to the AEJ: Applied credibility bar before exhibits are finalized; it does not write the prose or build the package.

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 building exhibits for an Academy of Management Annals (Annals) review — the signature framework figure, who-studied-what synthesis tables, and search/coverage exhibits that summarize across a literature. Designs review exhibits; it does not produce original-estimate regression tables (an Annals review reports no new data of its own).

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。