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github/nuget-manager

Gestiona paquetes NuGet en proyectos/soluciones .NET. Utiliza esta habilidad al agregar, eliminar o actualizar versiones de paquetes NuGet. Exige el uso de la CLI `dotnet` para la gestión de paquetes y proporciona procedimientos estrictos para ediciones directas de archivos solo al actualizar versiones.

¿Qué es nuget-manager?

Esta habilidad está diseñada para gestionar paquetes NuGet en proyectos y soluciones .NET a través de la interfaz de línea de comandos `dotnet`. Es especialmente útil para desarrolladores que trabajan con ecosistemas .NET y necesitan agregar, eliminar o actualizar dependencias de paquetes de manera eficiente. La habilidad impone el uso exclusivo de la CLI `dotnet` para operaciones estándar como `dotnet add package`, `dotnet remove package` y `dotnet list package`, garantizando así consistencia y buenas prácticas. Sin embargo, reconoce que ciertas acciones, como la actualización masiva de versiones en archivos de proyecto (`.csproj`), pueden requerir ediciones directas de archivos, para lo cual proporciona procedimientos estrictos que incluyen verificar la existencia de la solución, crear puntos de restauración con Git y validar la compilación después de los cambios. La habilidad soporta plataformas como Claude Code y Cursor, y se integra naturalmente en flujos de trabajo de desarrollo .NET.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/nuget-manager

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Documentación

¿Qué hace nuget-manager?

Gestiona paquetes NuGet en proyectos/soluciones .NET. Utiliza esta habilidad al agregar, eliminar o actualizar versiones de paquetes NuGet. Exige el uso de la CLI dotnet para la gestión de paquetes y proporciona procedimientos estrictos para ediciones directas de archivos solo al actualizar versiones.

Uso

Para utilizar esta habilidad, invoca el asistente de IA con instrucciones como:

  • "Agrega el paquete Newtonsoft.Json versión 13.0.3 a mi proyecto."
  • "Elimina el paquete EntityFramework de la solución."
  • "Actualiza todas las versiones de paquetes en el archivo .csproj a la última versión estable."

Procedimientos

Agregar un paquete

Utiliza el comando dotnet add package:

dotnet add package <NombreDelPaquete> --version <Versión>

Por ejemplo:

dotnet add package Newtonsoft.Json --version 13.0.3

Eliminar un paquete

Utiliza el comando dotnet remove package:

dotnet remove package <NombreDelPaquete>

Por ejemplo:

dotnet remove package EntityFramework

Listar paquetes

Para ver los paquetes instalados, usa:

dotnet list package

Actualizar versiones (edición directa de archivos)

Cuando necesites actualizar la versión de un paquete en un archivo de proyecto (.csproj), sigue estos pasos estrictos:

  1. Verifica que la solución se compile correctamente con dotnet build.
  2. Crea un commit de Git como punto de restauración.
  3. Edita directamente el archivo .csproj para cambiar la versión del paquete.
  4. Ejecuta dotnet restore para restaurar los paquetes.
  5. Vuelve a compilar con dotnet build para validar los cambios.

Buenas prácticas

  • Siempre especifica la versión del paquete al agregarlo.
  • Prefiere el uso de dotnet CLI sobre ediciones manuales de archivos.
  • Realiza commits de Git con frecuencia para mantener un historial claro.
  • Después de cualquier cambio, verifica que la solución compile sin errores.

Plataformas compatibles

  • Claude Code
  • Cursor
  • Otros asistentes de IA que admitan habilidades personalizadas

Notas

Esta habilidad asume que tienes instalado el SDK de .NET y que la CLI dotnet está disponible en tu PATH. Si no es así, consulta la documentación oficial de .NET para la instalación.

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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