docs-product-alignment

Audit and update docs/copilot/ documentation to accurately reflect current VS Code AI capabilities. Use when: competitive analysis reveals gaps, product…

npx skills add https://github.com/microsoft/vscode-docs --skill docs-product-alignment

Documentation Product Alignment

Audit VS Code Copilot documentation against current product capabilities and produce targeted, style-compliant edits. Follow the docs-writing style guide for all writing rules.

Guardrails

  • Factual only. Every claim must map to something the product does today. No superlatives, no competitive comparisons, no invented terminology.
  • Content framing. Establish each page's primary persona, reader intent, and article purpose by following the content-framing guidance. Ask the writer before editing when the framing is ambiguous and would change the audit or recommendations.
  • Two consumers. The primary persona reads the prose; AI agents and search crawlers index Keywords, MetaDescriptions, and opening paragraphs. Both matter.
  • Minimal edits. Change only what is inaccurate, outdated, or missing. One precise sentence beats a rewritten section.
  • Verifiable. If you cannot point to a UI element, setting, or documented behavior, do not write it.

Workflow

  1. Gather context and frame each page. Read the latest release notes, check github.com/features/copilot, and review any competitive claims or feature matrices the user provides. For each page, record its primary persona, reader intent, and article purpose before proposing edits.
  2. Audit high-traffic pages. Read each page and compare against current product truth. Focus on MetaDescriptions, Keywords, opening paragraphs, and terminology.
  3. Gap analysis. List what is inaccurate, outdated, or missing. Map each gap to a file and location. Include the page framing in the analysis, and prioritize by page traffic and impact on the primary persona's intent.
  4. Edit. Apply targeted changes that support the recorded framing. Vary phrasing across pages to avoid repetition.
  5. Verify. Search changed files for banned words, em-dashes, and MetaDescription length violations. Confirm that each changed page still serves its recorded primary persona and reader intent.

High-traffic pages to always check:

Terminology

Use these terms consistently. The "Avoid" column lists terms that creep in but should not.

ConceptUseAvoid
Autonomous coding sessionsagentsagent mode, agentic workflows
Running without user interactionbackground agentsbackground agent mode
Code suggestions as you typeinline suggestionscode completions, autocomplete
Predicted next edit locationnext edit suggestions (NES)predictive edits
Understanding code across filesworkspace context, cross-file reasoningdeep semantic understanding (overuse)
GitHub's search for code contextGitHub's code searchremote search
VS Code's type/symbol analysislanguage intelligence (IntelliSense, LSP)code intelligence
Multiple AI model optionsmultiple AI modelsleading AI models
Plan then implement workflowPlan agent, implementation agentplanning mode

Capability areas

When auditing, ensure docs accurately cover these areas:

  • Agents: plan, implement, verify, parallel sessions, local/background/cloud, third-party support
  • Context: semantic search, language intelligence (LSP), GitHub code search, cross-repo awareness
  • Multi-file editing: coordinated changes, architecture-level refactoring, framework migrations
  • Enterprise: organization policies, model access controls, content exclusions, trust boundaries
  • SDLC workflow: Plan agent, implementation agent, Copilot code review, background/cloud handoff
  • Scale: large codebases, monorepos, multi-root workspaces, remote indexing

More skills from microsoft

oss-growth
microsoft
OSS growth hacker persona
agent-framework-azure-ai-py
microsoft
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
devops
azure-ai-anomalydetector-java
microsoft
Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
development
azure-ai-language-conversations-py
microsoft
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development