7,362 件 ・ 関連度順
概要と使いどころ
Use when working with Crucible — atlassian Crucible code review management including review creation, commenting workflows, approval processes, and Jira integration. Use when managing Crucible review workflows, configuring review templates, and enforcing review completion policies.
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 Confluence Deep — deep Confluence management covering spaces, pages, content analytics, permissions, and workspace health. Use when performing deep audits of Confluence usage, analyzing content freshness, reviewing space permissions, or assessing documentation coverage across an Atlassian Confluence instance.
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 Crowdstrike — crowdStrike Falcon endpoint detection and response, threat intelligence, host management, and incident investigation. Covers detection queries, host inventory, IOC searches, real-time response sessions, and vulnerability assessment. Use when investigating security incidents, reviewing endpoint health, analyzing threat detections, or managing CrowdStrike Falcon sensors.
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 Contentful — contentful headless CMS management covering space and environment inventory, content type analysis, entry and asset monitoring, locale configuration, webhook tracking, API key auditing, and usage quota monitoring. Use when reviewing content models, investigating publishing issues, monitoring API usage, or auditing space access.
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 Black Duck — synopsys Black Duck software composition analysis for open-source risk management, vulnerability detection, and license compliance. Covers project scanning, BOM component analysis, vulnerability tracking, license risk assessment, and policy management. Use when reviewing SCA scan results, analyzing open-source component risks, tracking license compliance, or managing Black Duck project configurations.
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 Banana Dev — banana.dev ML inference platform management covering model inventory, deployment status, API call history, GPU allocation, scaling configuration, build logs, and latency metrics. Use for comprehensive Banana.dev model deployment assessment and inference performance 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。
Use when working with Baseten — baseten ML deployment platform management covering model inventory, deployment status, autoscaling configuration, inference call history, GPU allocation, environment management, and performance metrics. Use for comprehensive Baseten workspace assessment and ML serving optimization.
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 Azure Boards Deep — deep Azure Boards management covering work items, sprints, backlogs, queries, and team velocity analytics. Use when performing deep audits of Azure DevOps Boards, analyzing sprint health, reviewing backlog grooming, or assessing team capacity and velocity across Azure DevOps projects.
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 Azure Dns — azure DNS management covering public and private DNS zones, record sets, virtual network links, DNSSEC, and alias records. Use when managing Azure DNS zones, configuring DNS records, setting up private DNS for VNets, managing alias records for Azure resources, or troubleshooting DNS 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。
Use when working with Appwrite — appwrite backend platform management covering project inventory, database collections, user authentication stats, storage bucket usage, function deployments, webhook configuration, and platform health. Use for comprehensive Appwrite project assessment and resource monitoring.
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 Aws Cdk Deep — aWS CDK application and stack management. Covers stack synthesis, CloudFormation template inspection, diff analysis, deployment orchestration, context values, asset management, and construct library usage. Use when managing CDK apps, debugging synthesis errors, reviewing stack diffs, or analyzing deployed CloudFormation resources.
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 Azure Policy — azure Policy compliance assessment, policy and initiative assignment management, remediation task tracking, and definition analysis via Azure CLI.
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 Aws Organizations — aWS Organizations account management, OU structure analysis, SCP policy review, and service access control. Covers organizational hierarchy, account inventory, policy inheritance analysis, delegated administrator status, and service control boundary assessment.
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。
Live-CPU/RAM/DISK/NET-Dashboard + Top-Prozesse + Alarm.
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。
Prompt Engineering Is Dead in 2025 Introduction Prompt engineering, once hailed as the essential ski
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。
MetaBackdoor trains the model to activate hidden behavior when the total token count crosses a speci
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。
Role subprocesses for Dev/Tester/Reviewer/Architect tasks.
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。
Sifa 1 , Fabian Küch 6 , Andreas Herten 4 , René Jäkel 3 , Georg Rehm 5 , Stefan Kesselheim 4 , Joac
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。
April 29, 2026 Key Takeaways PocketOS, a company that designs software for car rental businesses, ha
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 and maintain Architecture Decision Records (ADRs) following best practices for technical decision documentation. Use when documenting significant technical decisions, reviewing past architectural choices, or establishing decision processes.
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。
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
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。
Vite 8.0.10 build tooling — HMR, fast builds, plugins, and optimized production assets. Use when configuring Vite, setting up React/Vue projects with Vite, or optimizing frontend build performance.
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。
Never test mock behavior. Never add test-only methods to production classes. Understand dependencies before mocking.
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。
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
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。