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github/gh-cli

Referência completa do GitHub CLI (gh) para repositórios, issues, pull requests, Actions, projetos, releases, gists, codespaces, organizações, extensões e todas as operações do GitHub a partir da linha de comando.

O que é gh-cli?

Esta skill fornece uma referência abrangente para o GitHub CLI (gh), permitindo que desenvolvedores e agentes de IA executem operações completas do GitHub diretamente da linha de comando. Suporta uma ampla gama de recursos, incluindo gerenciamento de repositórios, issues, pull requests, GitHub Actions, projetos, releases, gists, codespaces, organizações e extensões. É útil para automatizar fluxos de trabalho de desenvolvimento, como criar revisões de código, gerenciar issues e acionar Actions, sem a necessidade de navegar pela interface web do GitHub. Compatível com agentes como Claude Code, Cursor e Codex, esta skill utiliza o comando 'gh' para interagir com a API do GitHub, oferecendo comandos intuitivos e consistentes que aumentam a produtividade em tarefas rotineiras de engenharia de software.

Funciona com~Claude Code~Codex CLI~Cursor
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Documentação

O que gh-cli faz?

Referência completa do GitHub CLI (gh) para repositórios, issues, pull requests, Actions, projetos, releases, gists, codespaces, organizações, extensões e todas as operações do GitHub a partir da linha de comando.

Comandos Principais

Repositórios

  • gh repo create: Cria um novo repositório no GitHub.
  • gh repo clone <repositorio>: Clona um repositório para o diretório local.
  • gh repo view <repositorio>: Visualiza detalhes de um repositório.
  • gh repo fork: Cria um fork de um repositório.

Issues

  • gh issue create: Cria uma nova issue.
  • gh issue list: Lista issues abertas.
  • gh issue view <numero>: Visualiza detalhes de uma issue.
  • gh issue close <numero>: Fecha uma issue.

Pull Requests

  • gh pr create: Cria um novo pull request.
  • gh pr list: Lista pull requests abertos.
  • gh pr view <numero>: Visualiza detalhes de um pull request.
  • gh pr merge <numero>: Mescla um pull request.
  • gh pr review <numero>: Adiciona uma revisão a um pull request.

GitHub Actions

  • gh workflow list: Lista workflows do Actions.
  • gh workflow run <workflow>: Dispara um workflow manualmente.
  • gh run list: Lista execuções de workflows.
  • gh run view <id>: Visualiza detalhes de uma execução.
  • gh run watch <id>: Acompanha logs de uma execução em tempo real.

Projetos

  • gh project list: Lista projetos.
  • gh project view <numero>: Visualiza detalhes de um projeto.
  • gh project item-add <project-number> --body <content>: Adiciona um item a um projeto.

Releases

  • gh release create <tag>: Cria uma nova release.
  • gh release list: Lista releases.
  • gh release view <tag>: Visualiza detalhes de uma release.

Gists

  • gh gist create <arquivo>: Cria um novo gist.
  • gh gist list: Lista gists.
  • gh gist view <id>: Visualiza detalhes de um gist.

Codespaces

  • gh codespace create: Cria um novo codespace.
  • gh codespace list: Lista codespaces.
  • gh codespace code: Abre um codespace no VS Code.
  • gh codespace delete: Exclui um codespace.

Organizações

  • gh org list: Lista organizações do usuário.
  • gh org members: Lista membros de uma organização.

Extensões

  • gh extension install <nome>: Instala uma extensão do gh.
  • gh extension list: Lista extensões instaladas.
  • gh extension upgrade <nome>: Atualiza uma extensão.

Configuração

  • gh auth login: Faz login no GitHub.
  • gh auth status: Verifica o status de autenticação.
  • gh config set <chave> <valor>: Define uma configuração.
  • gh config get <chave>: Obtém o valor de uma configuração.

Utilitários

  • gh help: Exibe ajuda geral.
  • gh <comando> --help: Exibe ajuda para um comando específico.
  • gh version: Mostra a versão do gh instalada.

Exemplos de Uso

# Clonar um repositório
gl repo clone claude-code/super-tool

# Criar uma issue
gl issue create --title "Corrigir bug de login" --body "Descrição detalhada do bug"

# Criar um pull request, revisar e mesclar
gl pr create --title "Adicionar nova feature" --body "Descrição"
gl pr review 42 --approve
gl pr merge 42

# Executar um workflow manualmente
gl workflow run deploy.yml --ref main

# Criar uma release com assets
gl release create v1.0.0 --title "v1.0.0" --notes "Notas da release" ./dist/meu-app.zip

Dicas

  • Utilize --help em qualquer comando para ver opções detalhadas.
  • Configure alias para comandos frequentes: gl alias set prc "pr create".
  • Combine comandos com scripts bash para automação de tarefas repetitivas.
  • Mantenha o gh atualizado: gl upgrade.

Referências

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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