「apa」の検索結果

Generate strategic-consultant-grade meeting minutes from transcripts or notes in Japanese OR English (matching the source language), then run a self-review loop (max 3 iterations) that checks for internal contradictions, action-item omissions, speaker-name errors, and date/day-of-week mistakes before reporting completion. Use this skill whenever the user asks to draft meeting minutes, summarize a transcript, or document a meeting discussion — including Japanese-language transcripts (議事録 / 会議メモ / 文字起こし) and English-language transcripts.

Use when working with Apache Pinot — apache Pinot management — monitor cluster health, tables, segments, ingestion jobs, query performance, and controller/broker/server status. Use when debugging ingestion issues, inspecting segment distribution, optimizing query latency, or auditing cluster capacity.

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 working with Apache Druid Deep — apache Druid deep management — monitor cluster health, datasources, ingestion tasks, segments, query performance, and coordinator/overlord status. Use when debugging ingestion failures, inspecting segment distribution, optimizing query latency, or auditing cluster capacity.

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 writing a pull request description, change summary, or release notes in Japanese for this project. Trigger words: PR 説明, プルリク, 変更サマリ, リリースノート, PR 文章, レビュー依頼, 日本語で説明, pull request.

品質管理、不適合調査、根本原因分析、是正措置、サプライヤー品質管理に関する 体系化された専門知識。規制下の製造環境で15年以上の経験を持つ品質エンジニアの 知見に基づく。FDA、IATF 16949、AS9100環境を網羅。NCRライフサイクル管理、 CAPAシステム、SPC解釈、監査方法論を含む。不適合の調査、根本原因分析、 CAPA管理、SPCデータの解釈、サプライヤー品質課題の対応に使用。

Designs and audits native-unit capacity reservation, serial admission, ambiguity holds, stop reserves, effect closure, and control-disjoint settlement handoff for consequential agent attempts. Use when scarce capacity must be conserved across retry, rework, resurrection, or compensation. NOT for scheduling work, choosing models, pricing unlike resources into one scalar, authorizing effects, grading work, paying workers, or asserting runtime containment.

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。

JavaのQuarkusサービスを設計・レビューするスキル。REST API、Panacheでのデータアクセス、Camelでのメッセージ連携や非同期処理の実装パターンを示します。

• REST APIの責務を分けたいとき
• CamelとRabbitMQの連携
• Panacheの保存処理を見直したいとき

Runtime capabilities a published Artifact page can be granted — behavior static HTML cannot provide on its own, such as the page reading live or connected data, remembering what people do on it (a poll, a sign-up sheet, a checklist, a document edited in place — it saves new versions of itself), keeping state shared across viewers, knowing who is viewing, asking Claude a question of its own, storing files people add, or handing the viewer a file to save. Serves this user's live capability roster and the typed call definitions. Load it whenever any such runtime behavior would make an artifact more useful, before writing the page.

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。

Detects and prevents privilege escalation inside Kubernetes pods by combining admission control (OPA policies), runtime monitoring (Falco), and audit log analysis of security contexts, Linux capabilities, and syscall patterns. Use when investigating a pod running as root or privileged, hardening workloads against in-pod escalation, or hunting for containers exceeding their intended scope. Keywords: allowPrivilegeEscalation, runAsRoot, capabilities, securityContext, OPA, Falco, audit log. Do not use for escalation through RBAC and service-account permissions - use auditing-kubernetes-rbac-privilege-escalation.

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。

Used to analyze Apache ShardingSphere community issues. Emphasizes root-cause-first and evidence-first classification before conclusions, and produces copy-ready GitHub issue replies in the voice of an Apache ShardingSphere community maintainer.

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。

Guide for modifying an already-installed workspace capability — adding or editing its MCP servers, skills, integrations, or agent guidance. Use when the user wants to customize, change, or extend an existing capability.

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。

Guide for assembling a workspace capability (skills + integrations). Use when the user wants to create a capability.

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 a capability to the CURRENT Convex app — consults the served Convex capability catalog for always-current procedures (billing, crons, auth, agent, search, …); falls back to built-in hosting or @convex-dev component search. TRIGGER when the user runs /add, or asks to add hosting/publishing or any backend capability to an existing Convex app.

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。

Guides YAML SDK development in Apache Beam, including environment setup, testing, and key concepts. Use when working with Beam YAML code in sdks/python/apache_beam/yaml/.

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。

Ensures all new files include proper Apache 2.0 license headers. Use when creating any new file in the Apache Beam 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。

[omh] Parallel tool-call capability in doubt: check version currency and parallel-tool capability status, then apply an update only after diff approval. Use when the user says: parallel-tools, parallel tools, hermes parallel tools setup, update hermes for parallel tools, check parallel tool support, enable parallel tool calls, verify parallel tools capability, check hermes version for parallel tools.

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。

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

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。

Azure Fabric Management SDK for Python. Use for managing Microsoft Fabric capacities and resources. Triggers: "azure-mgmt-fabric", "FabricMgmtClient", "Fabric capacity", "Microsoft Fabric", "Power BI capacity".

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。

Guides general use of AWS messaging and streaming services. Covers Amazon SQS, Amazon SNS, Amazon EventBridge, Amazon MQ, Amazon Kinesis Data Streams, Amazon Data Firehose, Amazon Managed Service for Apache Flink, and Amazon Managed Streaming for Apache Kafka (MSK). Use when reasoning about messaging and streaming patterns. Also identifies which AWS service owns each customer communication channel: email (Amazon SES), and WhatsApp, SMS, MMS, RCS, voice and mobile push (the AWS End User Messaging family of services). Routes the request to the specialized skill for that channel. Defers to the channel's specialized skill when the user already named a specific channel. In general, use specific skills or documentation searches for detailed service-specific questions. Do NOT use for MSK or Managed Service for Apache Flink questions, prefer specific skills. Does not configure customer communication channels; defers to specific 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。

Analyze and control capacity, queueing, and backpressure for a tenant-specific Abridge integration without inventing public limits or headers. Use when handling concurrency or throttling requirements. Trigger with "plan Abridge capacity".

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。

Derive a safe Adobe workload envelope from synthetic load, queue/backpressure behavior, current service constraints, spend, and output correctness. Use when the task requires adobe capacity and load envelope. Trigger with "load test Adobe", "scale Firefly jobs", or "Adobe capacity plan".

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。

Owns execution: how work actually gets done across the organization, including process, program management, capacity, vendors, supply chain, and service delivery. Use this when execution is the problem rather than strategy, to design or fix a process, to resolve cross-functional handoff failures, to plan capacity, to assess delivery risk, or when the same failure keeps recurring. Also use to decide whether to build, hire, or outsource a capability.

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。

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

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。

Evaluate, configure, and migrate workloads to AWS Lambda Managed Instances (LMI). Triggers on: Lambda Managed Instances, LMI, capacity provider, multi-concurrency Lambda, dedicated instance Lambda, EC2-backed Lambda, cold start elimination, Graviton Lambda, instance type for Lambda, scheduled scaling for LMI, Lambda cost optimization with Reserved Instances or Savings Plans. Also trigger when users describe high-volume predictable workloads seeking cost savings, want to scale LMI capacity on a schedule, or compare Lambda vs EC2 for steady-state traffic. For standard Lambda without LMI, use the aws-lambda skill instead.

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。