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github/copilot-instructions-blueprint-generator

기술에 종속되지 않는 블루프린트 생성기로, 기존 코드베이스 패턴을 분석하고 가정을 피하여 프로젝트 표준, 아키텍처 패턴, 정확한 기술 버전에 일관된 코드를 생성하도록 GitHub Copilot을 안내하는 포괄적인 copilot-instructions.md 파일을 만듭니다.

copilot-instructions-blueprint-generator란 무엇인가요?

이 스킬은 현재 코드베이스를 분석하여 프로젝트별 코딩 표준, 아키텍처 패턴, 사용된 기술 스택 및 버전을 반영한 맞춤형 `copilot-instructions.md` 파일을 생성합니다. 개발자가 Copilot이 특정 라이브러리, 프레임워크, 코딩 규칙을 이해하고 일관되게 적용하도록 지시할 수 있게 해줍니다. 기술에 독립적이므로 모든 프로그래밍 언어나 스택에 사용할 수 있으며, 파일 생성 시 기존 코드에서 패턴을 추론하여 가정을 피합니다. GitHub Copilot과 함께 사용하도록 설계되었으며, `.github/copilot-instructions.md` 또는 `.github/instructions.md` 경로를 통해 사용자 지정 지침을 지원하는 모든 에디터에서 작동합니다. 주요 이점은 기계적으로 학습된 일반적인 제안 대신 프로젝트의 실제 요구 사항에 맞춰진 더 정확하고 일관된 코드 제안을 제공한다는 점입니다.

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

Installed? Explore more 코딩 & 개발 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

copilot-instructions-blueprint-generator은(는) 무엇을 하나요?

기술에 종속되지 않는 블루프린트 생성기로, 기존 코드베이스 패턴을 분석하고 가정을 피하여 프로젝트 표준, 아키텍처 패턴, 정확한 기술 버전에 일관된 코드를 생성하도록 GitHub Copilot을 안내하는 포괄적인 copilot-instructions.md 파일을 만듭니다.

작동 방식

이 스킬은 현재 작업 디렉터리를 스캔하여 사용된 프로그래밍 언어, 빌드 도구, 테스트 프레임워크, 의존성 관리자 및 기타 주요 기술을 식별합니다. 그런 다음 다음과 같은 섹션으로 구성된 구조화된 copilot-instructions.md 파일을 생성합니다:

  • 프로젝트 개요: 프로젝트 이름, 목적, 기술 스택 요약.
  • 코드 스타일 가이드: 들여쓰기, 명명 규칙, 파일 구조 등.
  • 아키텍처 패턴: 일반적인 패턴(예: MVC, 레이어드 아키텍처, 마이크로서비스).
  • 기술 버전: 정확한 버전 정보(예: Python 3.11, React 18.2.0).
  • 모범 사례: 프로젝트 유형(예: 비동기 처리, 오류 처리)에 맞는 특정 권장 사항.

구성

이 스킬은 구성이 필요하지 않습니다. 현재 작업 공간의 내용을 기반으로 지능적으로 분석합니다.

사용 예시

  1. 스킬을 활성화합니다: github/copilot-instructions-blueprint-generator
  2. 루트 디렉터리에서 copilot-instructions.md 파일을 생성하라는 요청을 보냅니다.
  3. 생성된 파일을 검토하고 필요에 따라 사용자 지정합니다.

지원되는 플랫폼

  • GitHub Copilot (VS Code, JetBrains, Neovim 등)
  • copilot-instructions.md 파일을 지원하는 모든 에디터

라이선스

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다.

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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