「egress」の検索結果

Debugs crashes, regressions, flakes, and production bugs systematically. Use when diagnosing stack traces, logs, or profiling data; not perf tuning or incident command.

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。

Designs coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, tool grading, regression, or cost. Not for general LLM evals (ai-evals).

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。

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。

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。

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。

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。

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。

[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。

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。

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。

Provision and drive a `pu` box — an Incus container used as a clean Linux host for CI, builds, and evidence capture. Use when you need to run something on a fresh remote box instead of the user's machine: `nix run` a build, run CI against a real host, capture screenshots/video off-machine, or reproduce on a pristine environment. Covers create/connect/scp/destroy, running remote commands, copying artifacts back, and the no-egress failure mode. Triggers on "pu box", "spin up a box", "run this on a box", "ephemeral host", "pu create/connect/destroy".

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.

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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".

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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。