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Frontend · Version 1.4.0 · Reviewed 2026-08-02

Heading Level Auditor

Improve document outline extraction and section hierarchy review with evidence, explicit trade-offs, and a verification plan.

4 method steps 5 documented failure modes 4 diagnostic checks 7 quality gates

Audits headings as a semantic document outline, distinguishing visual typography from section ownership and identifying skipped or empty levels that impair navigation.

₹199 one-time

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What this skill helps you do

  • Document outline extraction
  • Section hierarchy review
  • Visual-semantic mismatch detection

How Heading Level Auditor works

You provide

Component code, field metrics, and the failing interaction

It inspects

Semantics, focus, and render behavior for document outline extraction

It decides

A section hierarchy review fix using native elements where possible

You verify

Keyboard traversal and assistive output re-checked

What it checks first

Heading Level Auditor audits headings as a semantic document outline, distinguishing visual typography from section ownership and identifying skipped or empty levels that impair navigation. Use it when the work involves Document outline extraction, Section hierarchy review, Visual-semantic mismatch detection.

  1. Whether the control uses a native element with real semantics or a div reconstructed with ARIA.
  2. Focus behavior across route changes, dialogs, and dynamic content insertion.
  3. Which Core Web Vital is failing, since LCP, INP, and CLS have unrelated causes.
  4. Whether state lives at the level that actually needs it, since misplaced state re-renders unrelated subtrees.

Failure modes it recognizes

  • A dialog that never moves focus into itself, leaving keyboard users stranded on the page behind it.
  • Layout shift from media without reserved dimensions, damaging CLS after content loads.
  • A long task blocking the main thread so interactions queue and INP degrades.
  • An inline object or function in props defeating memoization on every render.
  • Hydration mismatch from rendering time, randomness, or browser APIs during server render.

Answers it will reject

  • Patching a non-semantic control with ARIA instead of using the native element that behaves correctly.
  • Measuring performance in development mode, where the framework runs work that never ships.
  • Wrapping everything in memo, which adds comparison cost without removing identity churn.

Decision rules it applies

  • Prefer native semantic elements; ARIA is a correction layer rather than a foundation.
  • Reserve space for anything that loads asynchronously so layout stays stable.
  • Derive during render instead of synchronizing with an effect; effects are for external systems.

Evidence it asks for

  • Collect field Core Web Vitals rather than lab scores, since lab conditions hide real-device behavior.
  • Profile renders to attribute them to a specific trigger before optimizing.
  • Test keyboard-only traversal and screen-reader output for any interactive change.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to document outline extraction.
  2. Trace concrete failure or abuse paths for section hierarchy review; do not report checklist items without a mechanism.
  3. Prioritize visual-semantic mismatch detection findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Document outline extraction assessment
  • Section hierarchy review decision and action plan
  • Visual-semantic mismatch detection verification checklist

Evidence requirements

  • Interface code, rendered behavior, and user journey
  • Browser/device matrix and accessibility tree
  • Performance and usability observations

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Review heading levels across the marketplace and product pages, including reusable cards that render at different nesting depths.

Expected output

Extract the accessibility-tree outline per route, identify the single page topic, and check whether each heading introduces a real section at the correct depth. Reusable component typography is decoupled from heading rank, empty headings are removed, and skipped levels are assessed by structure rather than appearance...

Boundaries and compatibility

Ideal for

  • Document outline extraction: produce a decision or artifact grounded in supplied evidence.
  • Section hierarchy review: produce a decision or artifact grounded in supplied evidence.
  • Visual-semantic mismatch detection: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Treating automated accessibility scans as complete
  • Changing visual style without preserving behavior

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.