docs-product-alignment

por microsoft

Auditar y actualizar la documentación de docs/copilot/ para reflejar con precisión las capacidades actuales de IA de VS Code. Úsalo cuando: el análisis competitivo revele brechas, el producto…

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

Descargar ZIP GitHub

6.6k

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.
Two audiences. Humans read 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 Change only what is inaccurate, outdated, or missing.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

Gather context. Read the latest release notes, check github.com/features/copilot, and review any competitive claims or feature matrices the user provides.
Audit high-traffic pages. Read each page and compare against current product truth. Focus on MetaDescriptions, Keywords, opening paragraphs, and terminology.
Gap analysis. List what is inaccurate, outdated, or missing. Map each gap to a file and location. Prioritize by page traffic.
Edit. Apply targeted changes. Vary phrasing across pages to avoid repetition.
Verify. Search changed files for banned words, em-dashes, and MetaDescription length violations.

High-traffic pages to always check:

Terminology

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

Concept	Use	Avoid
Autonomous coding sessions	agents	agent mode, agentic workflows
Running without user interaction	background agents	background agent mode
Code suggestions as you type	inline suggestions	code completions, autocomplete
Predicted next edit location	next edit suggestions (NES)	predictive edits
Understanding code across files	workspace context, cross-file reasoning	deep semantic understanding (overuse)
GitHub's search for code context	GitHub's code search	remote search
VS Code's type/symbol analysis	language intelligence (IntelliSense, LSP)	code intelligence
Multiple AI model options	multiple AI models	leading AI models
Plan then implement workflow	Plan agent, implementation agent	planning 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