Agencies ke liye llms.txt aur AI crawler review: launch se pehle Claude Code workflow

web agencies aur client site teams wali team jab llms.txt aur AI crawler launch review sunti hai, to aksar seedha file, schema ya acronym par chali jati hai. Visitor ko pehle jawab, price, owner, proof aur next step chahiye.

Source base llms.txt proposal, Google robots.txt, Google crawler docs, OpenAI crawler docs aur Cloudflare AI Crawl Control hai. Related internal reading: professional-services inquiry flow and agency checklist.

मुख्य बातें

• Consultation clicks, form completion, repeated questions, review time aur publish se pehle mile risky items ko measure karein.
• 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.

गलती कहाँ शुरू होती है

Galti tab shuru hoti hai jab team AI system ko satisfy karne ki koshish karti hai, reader ko nahi. Thin FAQ ya new file page ke missing scope, proof aur CTA ko fix nahi karti.

web agencies aur client site teams wali team jab llms.txt aur AI crawler launch review sunti hai, to aksar seedha file, schema ya acronym par chali jati hai. Visitor ko pehle jawab, price, owner, proof aur next step chahiye.

Claude Code ka scope aur human approval

Claude Code page, FAQ notes, metadata, URLs aur approval table ko organize kar sakta hai. Human owner price, legal wording, privacy, client policy aur final publish approve karta hai.

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.

Copy-paste prompt

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

Working check code

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: trend ko review se bada banana

The risky pattern is selling llms.txt aur AI crawler launch review 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.

अक्सर पूछे जाने वाले सवाल

Does this guarantee more search traffic?

Traffic guarantee nahi hota. Page clear aur reviewable banta hai.

What should be measured first?

Consultation clicks, form completion, repeated questions, review time aur publish se pehle mile risky items ko measure karein.

Who approves the final page?

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

इस लेख के लिए क्या verify किया

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