「dr」の検索結果

Click, hover, drag, and interact with located UI elements

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。

Configure TestDriver sandbox options and environment settings

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。

How the TestDriver agent behaves on GitHub issues, pull requests, and @mentions

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。

The fastest way to get started with TestDriver. Just set your API key and start testing.

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。

Deploy TestDriver on your AWS infrastructure using CloudFormation

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。

Scaffold a project, connect TestDriver to your AI client, and run your first test.

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 TestDriver to an existing project by hand.

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。

Launch browsers, desktop apps, and extensions in your TestDriver sandbox

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。

How TestDriver learns your app and caches what it discovers for instant, deterministic replays

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 TestDriver tests iteratively using MCP tools with visual feedback

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 option you can pass to the TestDriver client, with defaults and examples

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。

Run TestDriver tests in CI/CD with parallel execution and cross-platform support

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。

Create the required PR-ready summary block, branch suggestion, title, and draft description for openai-guardrails-python after runtime, tests, examples, build/test configuration, or behavior-impacting docs 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。

Start and carry an explicitly invoked openai-guardrails-python implementation through a fresh isolated worktree and a local PR-ready handoff. Fetch the latest origin/main, keep task changes uncommitted, replay them onto the latest main before final review, run applicable verification and $implementation-final-review, use $pr-draft-summary to generate the complete PR draft and branch name, then create one clean local commit with takeover provenance when applicable. Use only when the user explicitly invokes this skill; never push, open a PR, or mutate GitHub.

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。

Creates Excalidraw diagrams through guided or autonomous workflows. Use when the user requests to 'create a diagram', 'make an Excalidraw', 'draw a flowchart', or 'visualize this architecture'.

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。

Think and work like an expert MEMS Engineer. Use when a task calls for MEMS Engineer judgment. Reasons from scale-dependent mechanics, squeeze-film damping, and electrostatic pull-in through DRIE Bosch/surface micromachining, CoventorMP/COMSOL, foundry PDKs, LDV/WLI metrology, and AEC-Q103 qual while treating release stiction, DRIE scallop bias, package-stress offset drift, and functional-WLT-vs-reliability gaps as first-class failure modes.

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。

Think and work like an expert Hydrologist. Use when a task calls for Hydrologist judgment. Reasons from hydrologic-cycle closure (P = ET + Q + ΔS + I), runoff-generation mechanisms, and channel routing through HEC-HMS, HEC-RAS, the National Water Model, and split-sample KGE/NSE/PBIAS calibration, while treating post-flood rating-curve shifts, radar QPE bias, snow/rain misclassification, and non-stationarity in extremes as first-class failure modes.

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。

Think and work like an expert Agricultural Engineer. Use when a task calls for Agricultural Engineer judgment. Reasons from mass, energy, and momentum balances on variable biological media through ET-based scheduling, psychrometric grain-drying balances, ASABE D497 draft estimates, EPANET pipe networks, and RTK-GNSS/ISOBUS precision-ag validation while treating soil compaction, clogged drip emitters, grain-bin hotspots, GPS drift, and uncalibrated yield monitors as first-class failure modes.

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。

Safe-release techniques DURING rollout: feature flags, progressive rollouts, canary analysis, guardrail metrics, production smoke tests, and synthetic users. Bridges QA and SRE practices. Use when: "feature flag testing," "canary deploy," "progressive rollout," "guardrail metrics," "dark launch," "safe rollout." Not for: scheduled probes that run continuously after release — use `synthetic-monitoring`. Not for: designing tests from prod telemetry — use `observability-driven-testing`. Related: release-readiness, synthetic-monitoring, observability-driven-testing, qa-metrics.

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。

Detect template version drift and guide migration to the latest template version. Compares current repo against the template, identifies what's outdated, and automates safe updates while protecting product-specific content. Triggers on requests to sync with template, update template version, check for template drift, or when user asks "sync template", "update to v3", "template drift", "check template version".

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。

Explore and manage databases with Drizzle Studio. Use when a user asks to browse database contents visually, inspect tables and data, run ad-hoc queries, manage database records through a GUI, debug database issues, or use a lightweight alternative to pgAdmin or DBeaver. Covers setup with Drizzle ORM, standalone usage, data browsing, filtering, and inline editing.

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。

Expert guidance for Excalidraw, the open-source virtual whiteboard library for creating hand-drawn style diagrams and sketches. Helps developers embed Excalidraw in React applications, build custom integrations, and leverage the API for programmatic diagram creation.

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。

Appium is an open-source framework that automates native, hybrid and mobile-web apps on iOS and Android through the WebDriver protocol. Use when the user wants to set up Appium 3, write mobile UI tests in JavaScript (WebdriverIO) or Python, pick capabilities, run gestures, or fix "appium", "XCUITest", "UiAutomator2", "mobile automation" problems. For React Native-only testing, see detox.

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。

AWS Bedrock — fully managed foundation models on AWS infrastructure. Use when deploying AI in AWS-native environments, needing enterprise compliance (SOC2, HIPAA), running Claude, Llama, Titan, or Mistral on AWS, leveraging Knowledge Bases for RAG, or applying Guardrails for content safety.

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。