frontmatter-description

Verifique e otimize campos de frontmatter MetaDescription na documentação do VS Code. Use ao auditar, adicionar ou melhorar descrições de página para SEO e…

npx skills add https://github.com/microsoft/vscode-docs --skill frontmatter-description

Frontmatter Description

Check and optimize MetaDescription frontmatter fields in markdown documentation files. Produces actionable fixes for descriptions that violate the rules below.

When to Use

  • Adding or editing a documentation page and need to write a MetaDescription.
  • Auditing existing pages for SEO or discoverability improvements.
  • Asked to check, review, or optimize frontmatter descriptions.
  • Editing content articles and want to ensure the MetaDescription reflects the updated content.

Rules

Every MetaDescription value must satisfy all of the following:

RuleDetail
LengthMaximum 160 characters.
ToneAction-oriented, value-focused, factual, and impersonal.
VoiceNo "you can", "users can", or "this page explains".
SentencesComplete sentences, not fragments or labels.
No colonsDo not include : in the value — it breaks YAML parsing.
UniquenessEach description must be unique across the docs.
Reusable variablesPreserve existing {% data variables.<group>.<name> %} references. Do not replace them with rendered product names.

Descriptions must also reflect the page's primary persona, reader intent, and article purpose. Establish that framing by following the content-framing guidance before suggesting or applying a description. If the existing page and writer's request support multiple materially different interpretations, ask the writer to confirm the framing before editing.

Procedure

1. Identify target files

Determine which files to check:

  • If given specific files, use those.
  • If asked to audit a folder (e.g., docs/copilot/), find all .md files in that folder recursively.
  • Skip files that have no YAML frontmatter (e.g., README.md files without --- delimiters).

2. Extract and validate

For each file, read enough of the page to identify its primary persona, reader intent, and article purpose. Then read the YAML frontmatter and check the MetaDescription field:

  1. Missing — Flag if MetaDescription is absent.
  2. Length — Flag if the value exceeds 160 characters. Report the current length.
  3. Voice — Flag if it contains "you can", "users can", "this page explains", or similar reader-addressing phrases.
  4. Tone — Flag if the description is a fragment, a label, or passive rather than action-oriented.
  5. Context — Flag if the description does not mention the relevant feature or tool.
  6. Content alignment — Flag if the description does not represent the page's purpose or the outcome sought by its primary persona.
  7. Forbidden words — Flag any occurrence of "teaching", "enable", "disable", or condescending terms.
  8. Colons — Flag any : character in the value.
  9. Versions — Flag any version numbers (e.g., "v1.90", "VS Code 1.90").
  10. Formatting — Flag Jinja2 variables ({{...}}), HTML tags, or Markdown formatting. Do not flag supported reusable data variables ({% data variables.<group>.<name> %}).
  11. Reusable variables — When a description contains a reusable data variable, verify that its full path exists in data/variables and resolves to the intended text. Preserve valid variables exactly as written.

3. Report findings

Present results as a table:

| File | Issue | Current value | Suggested fix |
|------|-------|---------------|---------------|
  • Group by file path.
  • For each issue, provide the current MetaDescription and a rewritten suggestion that passes all rules.
  • If a description passes all checks, omit it from the table (or mark it as passing if the user asked for a full report).

4. Apply fixes (when asked)

  • Edit the MetaDescription value in the YAML frontmatter.
  • Do not change any other frontmatter fields.
  • Do not alter the page body content.
  • Preserve existing reusable data variables. Never replace a variable with its rendered product name.
  • Verify the fix passes all rules and reflects the confirmed page framing before applying.

Examples

Good descriptions:

  • Get AI-powered inline suggestions from GitHub Copilot in VS Code, including ghost text completions and next edit suggestions.
  • Configure and manage extensions in Visual Studio Code to customize your development environment.
  • Debug Python applications in Visual Studio Code with breakpoints, variable inspection, and integrated terminal output.
  • Build a web app with an AI agent in {% data variables.product.prodname_vscode_shortname %}, then review and validate the result.

Bad descriptions and why:

DescriptionProblem
This page explains how to use Copilot.Uses "this page explains".
You can debug your code with VS Code.Uses "you can".
CopilotFragment, not a sentence.
Learn how to enable the new feature in VS Code 1.90.Uses "enable" and includes a version number.
Set up debugging: configure launch.json for Python.Contains a colon.

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development