llms.txt dan crawler AI untuk agensi: review sebelum launch dengan Claude Code

Ketika tim agensi web dan tim situs klien mendengar review llms.txt dan crawler AI, mereka sering langsung mengejar file atau istilah baru. Pengunjung sebenarnya ingin jawaban, harga, pemilik informasi, bukti, dan langkah berikutnya.

Dasarnya adalah proposal llms.txt, robots.txt Google, crawler Google, dokumentasi OpenAI, dan Cloudflare AI Crawl Control. Related internal reading: professional-services inquiry flow and agency checklist.

Poin utama

Ukur klik konsultasi, penyelesaian form, pertanyaan berulang, waktu review, dan risiko yang ditemukan sebelum publish.
Claude Code ko review table banane दें: missing facts, risky claims, unclear CTA, URL boundary, owner approval.
Human owner price, legal scope, client policy, private URL aur final publication approve kare.
Start with one page, one checklist, and one clear consultation path.

Di mana masalah dimulai

Masalah dimulai saat tim mencoba memuaskan sistem AI sebelum membantu manusia. FAQ tipis atau file baru tidak memperbaiki halaman yang tidak punya cakupan, bukti, dan CTA yang jelas.

Cakupan Claude Code dan cakupan manusia

Claude Code dapat membaca halaman, menyusun pertanyaan, membandingkan metadata, mendaftar URL, dan membuat tabel review. Manusia menyetujui harga, cakupan, privasi, kebijakan klien, dan publikasi.

For monetized work, send team-level workflows to training and consultation.

3 use case

Use case 1: Review the main page

Input: page copy, FAQ notes, price notes, owner notes.
Output: missing facts, risky claims, unclear CTA, approval questions.
Human check: price, scope, credentials, privacy, client policy.

Use case 2: Compare metadata and visible text

Input: title, description, structured data, robots or llms notes, visible page.
Output: hidden claims, old dates, missing source links, unsafe promises.
Human check: each claim is true, current, and safe to publish.

Use case 3: Turn the workflow into a paid review

Input: page URL, sitemap, form fields, client approval sheet.
Output: screenshots, go/no-go list, fixes, next-month review items.
Human check: scope, price, owner, delivery limit, and follow-up plan.

Prompt siap pakai

あなたは制作会社の公開前レビュー担当です。クライアントサイトのrobots.txt、llms.txt、sitemap、重要ページを読み、AIクローラーとAI検索向けの境界を整理してください。

入力:
- robots.txt:
- llms.txt:
- sitemap URL:
- 公開してよいページ:
- 学習や要約に使われたくないページ:
- 問い合わせフォームURL:
- 管理画面や資料URL:

出力:
1. robots.txtで止めるもの
2. llms.txtに載せるもの
3. llms.txtに載せないもの
4. Search向けに残すべきページ
5. クライアント承認が必要な判断
6. 公開前にブラウザで見るURL一覧

禁止:
- llms.txtをSEO順位の魔法として説明しない
- robots.txtだけで機密情報を守れると言わない
- 管理画面、見積書、顧客資料のURLをサンプルに混ぜない

Kode verifikasi

const sitePolicy = {
  robotsTxt: [
    "User-agent: GPTBot",
    "Disallow: /private/",
    "User-agent: Google-Extended",
    "Disallow: /drafts/",
    "User-agent: *",
    "Disallow: /admin/",
    "Sitemap: https://example.com/sitemap-index.xml"
  ].join("\n"),
  llmsTxt: [
    "# Example Clinic Site",
    "",
    "## Services",
    "- https://example.com/services/",
    "- https://example.com/pricing/",
    "",
    "## Support",
    "- https://example.com/faq/",
    "",
    "## Do not summarize",
    "- Private estimates and customer records are not public sources."
  ].join("\n"),
  sensitivePaths: ["/admin/", "/private/", "/drafts/", "/customers/"]
};

function auditCrawlerBoundary(policy) {
  const errors = [];
  for (const path of policy.sensitivePaths) {
    if (!policy.robotsTxt.includes(`Disallow: ${path}`)) {
      errors.push(`robots.txtに ${path} のDisallowがありません`);
    }
  }
  if (!policy.robotsTxt.includes("Sitemap:")) {
    errors.push("Sitemap行がありません");
  }
  if (!policy.llmsTxt.includes("## Services")) {
    errors.push("llms.txtに公開サービスの節がありません");
  }
  if (policy.llmsTxt.includes("/admin/") || policy.llmsTxt.includes("/customers/")) {
    errors.push("llms.txtに非公開URLが混ざっています");
  }
  return { ok: errors.length === 0, errors };
}

console.log(auditCrawlerBoundary(sitePolicy));

Pitfall: menjual tren, bukan review

The risky pattern is selling review llms.txt dan crawler AI as a shortcut. The better offer is a visible review: what was checked, what changed, what needs approval, and what metric will be watched.

Another risk is sending private information into the workflow. Use sanitized examples and public pages. Remove client names, private estimates, customer records, draft URLs, and internal documents unless they are necessary and approved.

Pertanyaan umum

Does this guarantee more search traffic?

Tidak menjamin traffic. Ini membuat halaman lebih jelas dan mudah diverifikasi.

What should be measured first?

Ukur klik konsultasi, penyelesaian form, pertanyaan berulang, waktu review, dan risiko yang ditemukan sebelum publish.

Who approves the final page?

The business owner or client decision maker. Claude Code prepares the table; humans make the decision.

Yang saya verifikasi untuk artikel ini

I checked the primary documentation, narrowed the workflow to reviewable artifacts, and included runnable Node.js code that catches common gaps before publication.