「serving」の検索結果

Implement and finalize one ready rolling-wave slice in one invocation while preserving the lifecycle boundary between implementation and independent review. Use when the user wants a slice delivered end to end without manually invoking implement-slice and finalize-slice, wants main-local implementation by default with workers only when delegation pays, followed by mandatory fresh-context finalization, or says deliver, implement and finalize, or take the slice through review.

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。

Preview and permanently delete a NeatContext context while preserving any externally linked knowledge folder. Use only when the user explicitly invokes this skill or clearly asks to delete a context.

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。

Preview and permanently delete a NeatContext context while preserving any externally linked knowledge folder. Use only when the user explicitly invokes this skill or clearly asks to delete a context.

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。

Migrate a UI component from one framework to another, preserving behavior and 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。

Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.

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。

Structural editor that proposes cuts, reorganization, and simplification while preserving comprehension. Use when user requests structural review or editorial review of structure

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。

Rebases a feature branch onto its latest base, resolving conflicts while preserving the intent of local changes, optionally force-pushing and watching CI afterward. TRIGGER when: the user wants to rebase a feature branch, update a branch against its base, or replay local work on top of the latest upstream. DO NOT TRIGGER when: merging a release branch into dev → merging-branches; only watching CI on an already-open PR → monitoring-pull-requests.

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。

Bun runtime, package manager, bundler, and test runner. Use when running scripts with bun, managing packages, serving HTTP with Bun.serve, querying databases with Bun.sql/bun:sqlite/Bun.redis, shell scripting with $, using S3/file I/O, writing tests with bun:test, bundling or compiling to executable, or using any Bun-specific API (spawn, glob, semver, FFI, workers, plugins, HTMLRewriter).

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。

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.

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。

Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.

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。

Executa inferência de LLM em CPU, Apple Silicon e GPUs consumer sem hardware NVIDIA. Use para edge deployment, Macs M1/M2/M3, GPUs AMD/Intel ou quando CUDA não está disponível. Suporta quantização GGUF (1,5-8 bits) para redução de memória e aceleração de 4-10× vs PyTorch em CPU.

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。

Otimiza inferência de LLM com NVIDIA TensorRT para máxima vazão e latência mínima. Use para implantação em produção em GPUs NVIDIA (A100/H100), quando você precisa de inferência 10-100x mais rápida que PyTorch, ou para servir modelos com quantização (FP8/INT4), batching em voo e escalabilidade multi-GPU.

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。

Construa sistemas de ML em produção com PyTorch 2.x, TensorFlow e frameworks modernos de ML. Implementa model serving, feature engineering, A/B testing e monitoramento.

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。

[Code Quality] Use when you need to simplify and refine code for clarity, consistency, and maintainability while preserving all functionality.

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。

Translate motion, interaction, and visual experience into implementable constraints while preserving accessibility and performance. Apply when working on animations, transitions, scroll effects, or micro-UX.

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 editing Chinese web fiction, narrative prose, or story drafts that sound AI-generated, overly polished, formulaic, explanatory, or too symmetrical; use to detect AI-writing fingerprints and revise only wording while preserving plot, facts, characters, names, and intent.

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。

GStack report-only QA method for checking web or UI behavior, documenting blockers, and preserving evidence without modifying code.

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 splitting content work between a raw drafting model/window, an editor-in-chief, and the user's final taste review. It keeps prompts loose enough for judgment while preserving facts, account boundaries, and publish gates.

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。

Control the user's existing Safari 26 tabs through a persistent JavaScript REPL with a Playwright-style browser API. Use when a task must inspect, navigate, click, fill, select, or verify a page in Safari while preserving the user's current logins and browser state.

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。

Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.

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。

Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.

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。

Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.

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。

Collect volatile forensic evidence from a compromised system following order of volatility, preserving memory, network connections, processes, and system state before they are lost.

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。

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash 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。