「serving」の検索結果

Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with Unity Catalog, Lakehouse/Lakeflow, Lakebase, AI Runtime/model serving, or external connectors, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).

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。

Draft, audit, or minimally revise English- or Chinese-language academic prose to reduce formulaic, vacuous, mechanically repetitive, or process-leaking language while preserving claims, evidence strength, logical relations, manuscript-wide terminology identity, document-level pattern variation, and scholarly register. Use for papers, abstracts, grants, cover letters, and reviewer responses when the user asks to de-AI, humanize, audit AI-like phrasing, or rewrite text without changing meaning. English is primary; Chinese is supported. Not for detector evasion, policy circumvention, pure translation, non-academic copy, or adding facts, citations, examples, or author experiences that the source does not contain.

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。

Recognize when disagreements reveal valuable context, preserve multiple valid approaches instead of forcing premature resolution

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。

Methodology for minimizing test count while maximizing behavioral coverage - behavior definition, anti-pattern catalog, consolidation patterns, stopping criterion, coverage-preserving 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。

Minimizes test count while preserving coverage. Detects byte-identical pairs, parametrize-inflation, language-guarantee tests, AST-shape tests, stale migration nets. Approval gate before any change.

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。

Translates or rewrites Chinese text into English using a 'critical dialogue' tone, characterized by engaging questions, analytical commentary, and a conversational flow while preserving citations and facts.

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 a user wants Codex to directly operate PowerPoint and reconstruct an entire reference image as editable PowerPoint objects while preserving its full layout, labels, connectors, and visual relationships. Do not use for paper-to-deck generation or VBA-only output.

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 user asks to refresh CLAUDE.local.md, regenerate project context, or after a brain-update session flush. Rewrites only the sentinel-delimited auto-generated section from the brain DB, preserving manual behavior rules.

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。

Resolve an in-progress Git merge or rebase conflict while preserving the relevant intent of both sides. Use when conflict markers or an interrupted merge/rebase need investigation and resolution.

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。

Teach a skill or concept at the user's current level, optionally preserving a lightweight multi-session learning record.

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。

Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing 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。

Ultra-compressed commit message generator. Cuts noise from commit messages while preserving intent and reasoning. Conventional Commits format. Subject ≤50 chars, body only when "why" isn't obvious. Use when user says "write a commit", "commit message", "generate commit", "/commit", or invokes /caveman-commit. Auto-triggers when staging 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。

Format supplied prose with configurable word-prefix emphasis while preserving its text. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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。

Implements permissions and tenant or owner scoped resource access while preserving approved grants; not authentication or role redesign.

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。

Generate, derive, optimize, and diagnose structured AI image prompts for luminous cinematic Eastern xianxia environments under one inherited master canon with measurable divine scale, fixed five-layer space, 40–60% content-bearing breathing air, monumental Eastern architecture, railing-free edges, and coherent immortals and costume. Cover one-location scenic landmarks, immense inhabited celestial realms, cloud-borne palace cities, high-key Eastern sky-megastructure imagery, and the explicit `仙界大境原典` creative route. Use for Chinese fantasy scenery, places where immortals live, heavenly capitals, colossal celestial gates, architectural fragments above cloud seas, measurable divine scale, unusual framing, color and lighting direction, style-preserving variants, prompt rewrites, diagnosis, or direct image generation. Support 16:9, 21:9, 4:3, 3:2, 4:5, and 9:16; default to 16:9 for one prompt and never infer a nine-image series from a vertical ratio.

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。

Conducts hypothesis-driven, evidence-preserving threat hunts with bounded negative conclusions and telemetry-gap handling. Use for hunt hypotheses, scoped offline analysis, or analytic follow-up; not unscoped scanning, malware execution, or generic intelligence summaries.

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。

Controls authorized cybersecurity exercises through inject scheduling, safety gates, stop and restart decisions, communications, and synthetic-data boundaries. Use when serving as exercise control or white-team coordinator, not as compliance assessor or whitehat operator.

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 user asks to review or clean up R code for tidyverse style, standardize formatting or names, remove redundant comments or wrappers, or make a behavior-preserving style pass. Also use when writing substantial new R package code if the user requests tidyverse style or the package already follows it. Not for routine small edits or general correctness, security, or test-quality reviews.

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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。

Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and real-time versus async inference. Use as the entry point for SageMaker hosting requests, before image, IAM, and endpoint implementation skills.

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。

Implement a production SageMaker endpoint with autoscaling, CloudWatch alarms, and tags. Use after the serving image and IAM role are known; use the deployment planner first when architecture is undecided.

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 to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

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。

Rewrite AI-generated copy in Spanish and English so it sounds direct, concrete and human while preserving meaning, facts and numbers exactly. Use when drafts feel robotic, verbose or translated and need short active sentences without em-dashes.

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。

Lossless session context compression for token efficiency. Extracts entities, decisions, and state into compact format before context window fills. 10-30x reduction in context size while preserving all actionable information.

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。