architecture-blueprint-generator은(는) 무엇을 하나요?
포괄적인 프로젝트 아키텍처 청사진 생성기로, 코드베이스를 분석하여 상세한 아키텍처 문서를 자동 생성합니다. 기술 스택과 아키텍처 패턴을 자동 탐지하고, 시각적 다이어그램을 생성하며, 구현 패턴을 문서화하고, 아키텍처 일관성 유지 및 새로운 개발 방향을 제시하는 확장 가능한 청사진을 제공합니다.
프로젝트 아키텍처 청사진 생성기로, 코드베이스를 분석하여 상세한 아키텍처 문서를 자동 생성합니다. 기술 스택과 아키텍처 패턴을 자동 탐지하고, 시각적 다이어그램을 생성하며, 구현 패턴을 문서화하고, 아키텍처 일관성 유지 및 새로운 개발 방향을 제시하는 확장 가능한 청사진을 제공합니다.
이 스킬은 코드베이스를 분석하여 포괄적인 프로젝트 아키텍처 청사진을 자동으로 생성하는 도구입니다. 개발자는 이를 통해 프로젝트의 기술 스택, 아키텍처 패턴, 의존성 구조를 자동으로 파악하고, 시각적 다이어그램과 상세한 구현 패턴 문서를 얻을 수 있습니다. 주로 GitHub 및 유사한 플랫폼에서 동작하며, CLI 환경과 연동됩니다. SKILL.md 문서에 따라 소스 코드를 분석하여 컴포넌트 구조, 데이터 흐름, 인터페이스 설계를 추출하고, 마크다운 및 Mermaid 형식의 다이어그램을 생성합니다. 또한, 아키텍처 일관성을 유지하고 새로운 기능 개발 시 가이드라인을 제공하는 청사진 템플릿을 생성하여, 대규모 프로젝트의 진화를 효율적으로 관리할 수 있도록 돕습니다. 이 스킬은 아키텍처 문서화 자동화를 통해 개발자의 생산성을 높이고, 팀 간 지식 공유를 원활하게 합니다.
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/architecture-blueprint-generatorInstalled? Explore more 이미지 skills: steipete/songsee, affaan-m/frontend-design, mattpocock/improve-codebase-architecture · View all 6 →
포괄적인 프로젝트 아키텍처 청사진 생성기로, 코드베이스를 분석하여 상세한 아키텍처 문서를 자동 생성합니다. 기술 스택과 아키텍처 패턴을 자동 탐지하고, 시각적 다이어그램을 생성하며, 구현 패턴을 문서화하고, 아키텍처 일관성 유지 및 새로운 개발 방향을 제시하는 확장 가능한 청사진을 제공합니다.
This repo contains 20 individual skills — each has its own dedicated page.
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.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
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)
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
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"
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.
Instrument a webapp to send useful telemetry data to Azure App Insights
Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.
Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Create ASP.NET Minimal API endpoints with proper OpenAPI documentation
Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.
Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
Add documentation for a new AI provider — usage docs, env vars, Docker config, image resources.
Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.