llms.txt et crawlers IA pour agences: revue de lancement avec Claude Code

Quand une équipe de agences web et équipes de publication client entend parler de revue llms.txt et crawlers IA, elle saute souvent vers un fichier ou un acronyme. Le lecteur veut surtout une réponse claire, un prix lisible, un auteur identifié et une prochaine étape.

La base vient de la proposition llms.txt, de Google robots.txt, des crawlers Google, d’OpenAI et de Cloudflare AI Crawl Control. Related internal reading: professional-services inquiry flow and agency checklist.

Points clés

Mesurer consultations, conversion, questions répétées, temps de revue et risques trouvés avant publication.
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.

Où commence l’erreur

L’erreur commence quand on veut plaire aux systèmes IA avant d’aider la personne. Une FAQ mince ou une balise ne répare pas une page sans portée, preuve et action claire.

Ce que Claude Code fait et ce que l’humain valide

Claude Code peut lire la page, classer les questions, comparer les métadonnées, lister les URL et préparer une table de revue. L’humain valide prix, portée, confidentialité, politique client et publication.

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

3 cas d’usage

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 à copier

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

Code de vérification

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: vendre la tendance au lieu de la revue

The risky pattern is selling revue llms.txt et crawlers IA 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.

Questions fréquentes

Does this guarantee more search traffic?

Cela ne garantit pas le trafic. Cela rend la page plus claire et plus facile à vérifier.

What should be measured first?

Mesurer consultations, conversion, questions répétées, temps de revue et risques trouvés avant publication.

Who approves the final page?

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

Ce que j’ai vérifié pour cet article

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