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github/vscode-ext-localization

VS Code 확장 프로그램의 적절한 지역화를 위한 가이드라인으로, VS Code 확장 개발 가이드라인, 라이브러리 및 모범 사례를 따릅니다.

vscode-ext-localization란 무엇인가요?

이 스킬은 VS Code 확장 프로그램의 지역화(localization)를 위한 포괄적인 가이드라인을 제공합니다. 개발자들이 VS Code 확장을 여러 언어로 제공할 수 있도록 지원하며, Microsoft의 공식 VS Code 확장 개발 지침과 일관된 방식으로 작동합니다. 주요 기능으로는 문자열 외부화(string externalization), 번역 파일 관리, 현지화된 패키지 게시 방법 등이 포함됩니다. 이 가이드는 VS Code API의 `vscode.l10n` 모듈과 `@vscode/l10n-dev` 라이브러리를 활용하여 효율적인 지역화 워크플로우를 구축하는 방법을 설명합니다. 또한 확장 프로그램의 `package.json`에서 현지화된 문자열을 선언하고, 번역을 위한 XLIFF 파일을 생성하며, VS Code 마켓플레이스에 다국어 지원 확장을 게시하는 모범 사례를 다룹니다. 이 스킬은 VS Code 확장을 개발하는 모든 개발자, 특히 글로벌 사용자를 대상으로 하는 확장을 만드는 팀에게 유용합니다.

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문서

vscode-ext-localization은(는) 무엇을 하나요?

VS Code 확장 프로그램의 적절한 지역화를 위한 가이드라인으로, VS Code 확장 개발 가이드라인, 라이브러리 및 모범 사례를 따릅니다.

개요

이 스킬은 VS Code 확장 프로그램에 지역화를 적용하는 방법에 대한 지침을 제공합니다. 확장 프로그램이 여러 언어를 지원할 수 있도록 하여 전 세계 사용자에게 더 나은 경험을 제공합니다.

주요 기능

  • 문자열 외부화: 코드에서 직접 문자열을 사용하지 않고 외부 파일로 분리합니다.
  • 번역 파일 관리: 다양한 언어에 대한 번역 파일을 생성하고 관리합니다.
  • 현지화된 패키지 게시: 번역된 확장 프로그램을 VS Code 마켓플레이스에 게시합니다.

사용 방법

  1. VS Code 확장 프로젝트에서 vscode.l10n API를 사용하여 문자열을 외부화합니다.
  2. @vscode/l10n-dev 라이브러리를 사용하여 번역 가능한 문자열을 추출합니다.
  3. 추출된 문자열을 XLIFF 형식으로 변환하여 번역가에게 전달합니다.
  4. 번역된 문자열을 확장 프로그램에 포함시키고 테스트합니다.
  5. 다국어 지원 확장 프로그램을 VS Code 마켓플레이스에 게시합니다.

모범 사례

  • 모든 사용자 인터페이스 문자열을 외부화하여 번역 가능하게 만듭니다.
  • package.json에서 contributes 섹션의 지역화된 문자열을 적절히 선언합니다.
  • 번역 파일은 .vscode/l10n 디렉토리에 저장합니다.
  • 현지화 테스트를 위해 VS Code의 --locale 플래그를 사용합니다.

자세한 내용

자세한 내용은 VS Code 확장 지역화 문서를 참조하십시오.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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