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github/github-copilot-starter

새 프로젝트의 기술 스택에 기반하여 완전한 GitHub Copilot 구성을 설정합니다.

github-copilot-starter란 무엇인가요?

이 스킬은 개발자가 새 프로젝트를 시작할 때 기술 스택에 맞춰 GitHub Copilot 설정을 자동으로 구성해 줍니다. 프로젝트의 언어와 프레임워크를 인식하여 적절한 지침(instructions)과 제외 규칙(exclusions)을 포함한 `.github/copilot-instructions.md` 파일을 생성하며, Copilot이 더 정확하고 상황에 맞는 코드 제안을 할 수 있도록 돕습니다. 이 스킬은 GitHub Copilot이 활성화된 모든 저장소에서 사용할 수 있으며, CLI 도구 또는 에이전트가 프로젝트 초기화 시점에 실행하기 적합합니다. 또한 불필요한 파일을 자동 생성하지 않고 기존 프로젝트 구조를 존중하며, 사용자에게 명확한 피드백을 제공합니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/github-copilot-starter

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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

github-copilot-starter은(는) 무엇을 하나요?

새 프로젝트의 기술 스택에 기반하여 완전한 GitHub Copilot 구성을 설정합니다.

사용 방법

  1. 프로젝트 디렉토리에서 이 스킬을 호출합니다.

    • 터미널에서 에이전트에게 "이 프로젝트를 위해 Copilot을 설정해 줘"라고 요청합니다.
    • 또는 명시적인 명령어를 사용합니다: github/github-copilot-starter
  2. 기술 스택 분석

    • 스킬은 package.json, pyproject.toml, Gemfile, go.mod 등과 같은 프로젝트 구성 파일을 자동으로 감지합니다.
    • 감지된 언어, 프레임워크, 주요 라이브러리를 기반으로 적절한 Copilot 지침을 생성합니다.
  3. Copilot 구성 파일 생성

    • 생성된 구성은 다음 파일로 저장됩니다:
      • .github/copilot-instructions.md
    • 이 파일에는 Copilot이 프로젝트 컨텍스트를 이해하는 데 도움이 되는 지침이 포함됩니다.
  4. 확인 및 피드백

    • 설정이 완료되면 생성된 파일과 주요 내용에 대한 요약이 제공됩니다.

지원되는 기술

  • 언어: JavaScript, TypeScript, Python, Go, Ruby, Rust, Java, C#, PHP
  • 프레임워크: React, Next.js, Express, Django, Flask, Rails, Spring Boot, ASP.NET Core, Laravel
  • 패키지 매니저: npm, yarn, pnpm, pip, bundler, cargo, gradle, maven, composer

예시

Next.js 프로젝트 설정

# 프로젝트 디렉토리에서
copilot setup

이 명령어는 .github/copilot-instructions.md를 생성하며, 다음과 같은 내용을 포함합니다:

# Copilot 지침

이 프로젝트는 Next.js 14 (App Router)를 사용합니다.
- 서버 컴포넌트와 클라이언트 컴포넌트를 올바르게 구분하세요.
- `use client` 지시어는 필요한 경우에만 사용하세요.
- 파일 기반 라우팅을 사용하세요.
- 모든 API 경로는 `/api/*` 아래에 있어야 합니다.

참고 사항

  • 이미 Copilot 구성이 있는 경우 덮어쓰지 않고 병합합니다.
  • 설정은 저장소별로 적용되며 팀원 모두에게 영향을 줍니다.
  • 구성 파일은 버전 관리에 포함시키는 것이 좋습니다.

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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