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github/nuget-manager

.NET 프로젝트/솔루션의 NuGet 패키지를 관리합니다. NuGet 패키지를 추가, 제거 또는 업데이트할 때 이 스킬을 사용합니다. 패키지 관리를 위해 `dotnet` CLI 사용을 강제하며, 버전 업데이트 시에만 직접 파일 편집을 허용하는 엄격한 절차를 제공합니다.

nuget-manager란 무엇인가요?

이 스킬은 .NET 프로젝트 또는 솔루션에서 NuGet 패키지를 관리하는 방법을 정의합니다. `dotnet` CLI를 사용한 패키지 추가, 제거, 업데이트 작업을 지원하며, 버전 업데이트 시에만 제한적으로 `.csproj` 파일을 직접 편집하는 절차를 제공합니다. 이는 패키지 관리 작업의 일관성과 안정성을 보장하는 데 유용하며, 주로 Claude Code와 같은 AI 에이전트 환경에서 사용됩니다. 스킬은 크게 `add`, `remove`, `update` 세 가지 주요 명령어로 구성되어 있으며, 각 명령어에 대해 `dotnet` CLI를 우선 사용하고, `dotnet` CLI로 업데이트가 불가능한 경우에만 직접 파일 편집을 허용합니다. 모든 작업은 현재 솔루션 디렉토리 안에서 수행되어야 하며, 작업 전후에 솔루션의 빌드 상태를 확인하는 것이 권장됩니다.

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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

nuget-manager은(는) 무엇을 하나요?

.NET 프로젝트/솔루션의 NuGet 패키지를 관리합니다. NuGet 패키지를 추가, 제거 또는 업데이트할 때 이 스킬을 사용합니다. 패키지 관리를 위해 dotnet CLI 사용을 강제하며, 버전 업데이트 시에만 직접 파일 편집을 허용하는 엄격한 절차를 제공합니다.

사용법

패키지 추가

dotnet add <PROJECT> package <PACKAGE_NAME>

특정 버전을 추가하려면:

dotnet add <PROJECT> package <PACKAGE_NAME> --version <VERSION>

패키지 제거

dotnet remove <PROJECT> package <PACKAGE_NAME>

패키지 업데이트

dotnet list <PROJECT> package --outdated
dotnet add <PROJECT> package <PACKAGE_NAME> --version <VERSION>

dotnet CLI를 사용할 수 없거나 실패하는 경우에만 .csproj 파일의 PackageReference를 직접 편집하십시오.

참고: 직접 파일 편집은 패키지 버전 업데이트에만 허용됩니다. 패키지 추가 또는 제거는 항상 dotnet CLI를 사용해야 합니다.

요구 사항

  • dotnet CLI가 설치되어 있어야 합니다.
  • 솔루션 디렉토리 내에 있는 프로젝트에서만 작업하십시오.
  • NuGet 패키지 변경 후에는 dotnet build로 솔루션이 빌드되는지 확인하십시오.

제한 사항

  • 이 스킬은 .NET 프로젝트의 NuGet 패키지 관리에만 적용됩니다.
  • 솔루션의 Directory.Build.props와 같은 전역 패키지 구성을 수정하지 마십시오.
  • 패키지 소스는 변경하지 마십시오.

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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