2,164 件 ・ 関連度順
概要と使いどころ
Use when working with Aws Gamelift — aWS GameLift fleet management including game server deployments, fleet scaling, matchmaking configuration, game session monitoring, and player session tracking. Covers fleet health, capacity utilization, matchmaking metrics, and cost 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 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 Airflow — apache Airflow workflow orchestration management. Covers DAG management, task instance monitoring, executor health, pool status, variable management, connection audit, and scheduler diagnostics. Use when checking DAG run status, investigating task failures, managing Airflow resources, or auditing 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 Appium — appium mobile testing management and server monitoring. Covers Appium server health, session management, desired capabilities review, device farm integration, test result analysis, and driver configuration. Use when managing mobile test automation, investigating Appium server issues, or reviewing mobile testing 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 Gcp Bigquery Deep — google BigQuery job analysis, slot utilization, storage optimization, materialized views, BI Engine capacity, and query performance diagnostics via bq CLI and gcloud.
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 Dynamodb — amazon DynamoDB table analysis, capacity mode evaluation, GSI/LSI usage, item access patterns, and cost 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 Aws Ecs — aWS ECS cluster health, service status, task analysis, capacity provider management, and deployment tracking. Covers service stability, task failure investigation, container insights, resource utilization, and scaling 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 Druid — apache Druid datasource analysis, ingestion task management, supervisor status, segment management, and query 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。
Use when working with Cassandra — apache Cassandra keyspace analysis, compaction strategies, repair status, nodetool operations, and cluster health 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 Semgrep — semgrep static application security testing (SAST). Covers code scanning, custom rule creation, CI integration, autofix capabilities, multi-language support, and vulnerability triage. Use when scanning source code for security vulnerabilities, creating custom detection rules, or integrating SAST into CI pipelines.
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。
Drive the user's desktop in the background — clicking, typing, scrolling, dragging — without stealing the cursor, keyboard focus, or switching virtual desktops / Spaces. Cross-platform: macOS, Windows, Linux. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.
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。
PVDetector, a training-free framework that detects PI attacks during LLM inference by measuring hidd GAP: no mappable capability in latest signal (our
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。
Hierarchical, Hybrid, and Omnimodal Models Jun 30 58 m Jun 30, 2026 58 m read Show 10 25 50 100 pe GAP: no mappable capability in latest signal (our t
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。
With this cross-journal Collection, the editors at Nature Communications , Communications Psychology GAP: no mappable capability in latest signal (our
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。
Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models Isabel Papadimitriou and Dan Jurafsky 2020 Empirical Me
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。
Subquadratic Attention Harvard NVIDIA Arya Tschand Yaosheng Fu Vijay Janapa Reddi Splitting quadrati
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。
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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 explicitly asks for a desktop or system screenshot (full screen, specific app or window, or a pixel region), or when tool-specific capture capabilities are unavailable and an OS-level capture is needed.
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。
Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.
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 building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API capabilities.
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。
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.
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。
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
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。
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
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。
Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines
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。