「ar」の検索結果

Generate or modify run commands for the current session. Use when the user wants to set up or update run commands that appear in the session's Run button.

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Summarize the current state of the git repository

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Set up eval-based QA for Python LLM applications: instrument the app, build golden datasets, write and run eval tests, and iterate on failures. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.

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Use when you need to check feature flag states, compare channels, or debug why a feature behaves differently across release channels.

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Interactive, input-tool powered, task refinement workflow: interrogates scope, deliverables, constraints before carrying out the task; Requires the Joyride extension.

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Guidelines for fixing unhandled errors from the VS Code error telemetry dashboard. Use when investigating error-telemetry issues with stack traces, error messages, and hit/user counts. Covers tracing data flow through call stacks, identifying producers of invalid data vs. consumers that crash, enriching error messages for telemetry diagnosis, and avoiding common anti-patterns like silently swallowing errors.

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Triage and resolve Fedora issues with dnf, systemd, and SELinux-aware guidance.

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Use this skill whenever the user mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyond RFC 8805 compliance. Do NOT use for private or internal IP address management — applies only to publicly routable IP addresses.

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Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.

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Monitor Power Automate flow health, track failure rates, and inventory tenant assets using the FlowStudio MCP cached store. The live API only returns top-level run status. Store tools surface aggregated stats, per-run failure details with remediation hints, maker activity, and Power Apps inventory — all from a fast cache with no rate-limit pressure on the PA API. Load this skill when asked to: check flow health, find failing flows, get failure rates, review error trends, list all flows with monitoring enabled, check who built a flow, find inactive makers, inventory Power Apps, see environment or connection counts, get a flow summary, or any tenant-wide health overview. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app

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Investigate and fix CI failures on a pull request. Use when CI checks fail on a PR branch — covers finding the PR, identifying failed checks, downloading logs and artifacts, extracting the failure cause, and iterating on a fix. Requires the `gh` CLI.

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Get best practices for TUnit unit testing, including data-driven tests

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Get best practices for C# async programming

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Creates integration test cases for .NET data access artifacts during Oracle-to-PostgreSQL database migrations. Generates DB-agnostic xUnit tests with deterministic seed data that validate behavior consistency across both database systems. Use when creating integration tests for a migrated project, generating test coverage for data access layers, or writing Oracle-to-PostgreSQL migration validation tests.

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Create a new implementation plan file for new features, refactoring existing code or upgrading packages, design, architecture or infrastructure.

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Create time-boxed technical spike documents for researching and resolving critical development decisions before implementation.

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Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file

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Generate a complete MCP server project in C# with tools, prompts, and proper configuration

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Get best practices for NUnit unit testing, including data-driven tests

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Generate Python SDK setup + CRUD + bulk + paging snippets using official patterns.

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Discovers all projects in a .NET solution, classifies each for Oracle-to-PostgreSQL migration eligibility, and produces a persistent master migration plan. Use when starting a multi-project Oracle-to-PostgreSQL migration, creating a migration inventory, or assessing which .NET projects contain Oracle dependencies.

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Get best practices for MSTest 3.x/4.x unit testing, including modern assertion APIs and data-driven tests

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Diátaxis Documentation Expert. An expert technical writer specializing in creating high-quality software documentation, guided by the principles and structure of the Diátaxis technical documentation authoring framework.

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Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

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。