docs-product-alignment

name	docs-product-alignment
description	Audit and update docs/copilot/ documentation to accurately reflect current VS Code AI capabilities. Use when: competitive analysis reveals gaps, product launches new features, docs use outdated framing, or keyword coverage needs strengthening for discoverability by users and AI agents. Produces a gap analysis plus targeted edits across affected files.

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 Change only what is inaccurate, outdated, or missing.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 List what is inaccurate, outdated, or missing.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:

• docs/copilot/overview.md, docs/copilot/concepts/overview.md, docs/agents/reference/ai-features-cheat-sheet.md
• docs/copilot/getting-started.md, docs/agents/overview.md
• docs/agents/reference/workspace-context.md, docs/agents/agent-troubleshooting/faq.md

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