seo-agentic

Use when auditing whether an AI agent can safely understand and complete a web task through the rendered interface, DOM, accessibility tree, forms, authentication, consent, state changes, and document

SKILL.md

GitHub

Agentic Web Readiness

Markdown delivery contract

Read markdown-delivery-contract.md for every task. Write a substantive .md document for every material work step and register its direct destination in the work index.md. For public-site work, apply the owner-required Markdown discovery profile across the full eligible inventory; preserve native standards and immutable evidence formats.

Lane outputs: agent-journey.md; task-verification.md (combine small related steps with stable section links). Test read-only knowledge tasks starting from llms.txt and their direct Markdown destinations. Keep UI state, forms, consent and transaction authorization in their existing surfaces; a knowledge export does not authorize actions.

Audit whether a permitted agent can perceive, understand, and safely complete a bounded task using the same truthful interface and controls available to users.

Read agent-task-protocol.md before a formal audit.

Procedure

  1. Define one user-authorized task, start/end states, account/access assumptions, locale/device, risk class, owner, and success evidence.
  2. Capture screenshot, DOM, accessibility tree, accessible names/roles/states, focus order, form labels/errors, dynamic state, network-independent confirmations, and any approved agent trace.
  3. Verify semantic controls, stable identifiers/state, keyboard operation, loading/error recovery, consent, authentication, rate limits, and human-readable confirmation.
  4. Require explicit confirmation for purchase, publication, deletion, permission, financial, legal, or other consequential actions. Never weaken security, privacy, bot controls, or anti-abuse systems for convenience.
  5. Test task completion with bounded safe inputs in an authorized environment. Record partial completion, blocked access, ambiguity, and irreversible steps; do not simulate success.
  6. Evaluate UCP, ACP, MCP, A2A, or similar capabilities only against current specifications and a documented consumer. Record discovery location, operations, authentication, version, fallback, owner, and policy boundary.
  7. Route crawl/index concerns to seo-technical, commerce data to seo-commerce, accessibility remediation to the implementation owner, and search/AI measurement to seo-performance or ai-visibility-monitor.
  8. Package findings as SEO Findings 1.1.0 with task_flow or protocol_capability targets and agentic, accessibility, policy, technical, or commerce categories.

Guardrails

Do not call semantic HTML a ranking factor, protocol support a visibility requirement, or successful automation evidence of universal agent compatibility. Do not bypass authentication, CAPTCHA, consent, rate limits, paywalls, robots, or destructive confirmations.

Formal evidence handoff

Package only supported observations as SEO Findings 1.1.0. Let <suite-root> mean ${CLAUDE_PLUGIN_ROOT} in Claude Code. In Codex, read .seo-suite-runtime.json beside this SKILL.md when present and use its suite_root value; otherwise use the absolute repository checkout, then run python "<suite-root>/scripts/validate_seo_findings.py" validate-findings <bundle>/seo-findings.json --bundle <bundle>. Keep the handoff provisional until this command passes.

Output

Return the task contract, perception/state evidence, completion trace, blockers, safety controls, optional protocol assessment, owners, verification, rollback, and limitations—never an agentic SEO score.