1,869 件 ・ 関連度順
概要と使いどころ
Use when working with Aws Codedeploy — aWS CodeDeploy application and deployment management. Covers applications, deployment groups, deployment history, deployment configurations, instance health, target revisions, and rollback settings. Use when inspecting deployments, debugging failed deployments, reviewing deployment strategies, or auditing deployment group 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 Aws Codecommit — aWS CodeCommit repository management and analysis. Covers repository inventory, branch details, pull request status, commit history, approval rules, and trigger configurations. Use when inspecting repositories, reviewing pull requests, auditing branch policies, or analyzing commit activity.
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 Resource Manager — azure Resource Manager (ARM) template and deployment management. Covers template validation, deployment operations, resource group inspection, what-if analysis, deployment history, and template spec management. Use when deploying ARM templates, reviewing deployment status, analyzing resource groups, or troubleshooting failed deployments.
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 Betteruptime — better Uptime monitoring, status pages, incident management, on-call scheduling, and heartbeat tracking. Covers monitor configuration, response time analysis, incident lifecycle, on-call rotations, and SLA reporting. Use when managing uptime monitors, reviewing incident history, configuring on-call schedules, or analyzing response times in Better Uptime.
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 Aws Codebuild — aWS CodeBuild project management and build analysis. Covers build project inventory, build history, build phase details, environment configurations, source credentials, report groups, and build metrics. Use when inspecting build projects, debugging build failures, reviewing build environments, or analyzing 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。
Use when working with Aws Step Functions — aWS Step Functions execution analysis, state machine management, error tracking, and performance optimization. Covers execution history, failure investigation, state transition metrics, Express vs Standard comparison, and workflow visualization.
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 Cloud Build — google Cloud Build trigger management, build history analysis, worker pool configuration, artifact management, and CI/CD pipeline diagnostics via gcloud 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 Azure Logic Apps — azure Logic Apps workflow run analysis, trigger history, connector management, error diagnostics, and workflow definition inspection 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 Snowflake — snowflake data warehouse analysis, query performance tuning, cost optimization, warehouse management, and schema inspection. Covers QUERY_HISTORY analysis, credit consumption, warehouse utilization, storage analysis, data sharing, and Time Travel. Read this skill before any Snowflake operations — it enforces two-phase execution, anti-hallucination rules, and read-only safety constraints.
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 Cloudtrail — aWS CloudTrail event analysis, trail management, insight event investigation, and organization trail configuration. Covers API activity analysis, security event investigation, resource change tracking, unauthorized access detection, and event history querying.
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 Athena — aWS Athena query execution analysis, workgroup management, Data Catalog integration, and cost-per-query tracking. Covers query history, performance optimization, workgroup quota management, saved queries, and data scanned 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。
Hyperliquid market data, account history, trade 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。
Stock quotes, history, search, compare, crypto via Yahoo.
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。
Query Polymarket: markets, prices, orderbooks, history.
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。
September 16, 2026 New ask Hacker News story: Open-sourced jev architecture last year with model,pap
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。
Session stores conversation history for a specific session, allowing agents to maintain context without requiring explicit manual memory management.
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 git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists or non-security ownership questions.
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 user-facing App Store release notes from git history since the last tag.
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。
Stock quotes, price history, financial news, and 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。
Generate structured changelogs from git history. Use when user asks "what changed recently", "generate a changelog", "summarize commits" or user wants to understand recent development activity.
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。
Alternative Slowbar coffee intelligence — scientific analysis from seed to cup using First Principle + System Thinking. Covers: varieties, terroir, cultivation, disease diagnosis, processing, roasting physics/chemistry, extraction science, brewing, business strategy, storytelling. ALWAYS trigger for coffee-related questions requiring scientific depth. DO NOT trigger for simple recipes or general food questions. Style: science-driven, farmer-first, quality-obsessed.
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。
Authoritative reference for submitting tdmcp to the Anthropic Connectors Directory as a Codex Desktop Extension (MCPB). Use whenever drafting, checking, or fixing the submission — it defines the two submission paths, why tdmcp is a Desktop Extension (never a remote connector), every form field, the rejection-causing gates, and the local-only compliance story. Read this before writing any submission deliverable.
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。