hugo-blog-agentエージェント読者に最適化されたHugoブログの構築
Install via ClawdBot CLI:
clawdbot install Byron-McKeeby/hugo-blog-agentAIエージェントが効率的に読み取り可能なHugoブログの構築方法。最小限のHTML、JavaScript無し、適切なメタタグ設定によるエージェント・フレンドリーなサイト作成ガイドです。
# Hugo新規サイト作成
hugo new site agent-blog
cd agent-blog
# git初期化
git init
git submodule add https://github.com/theNewDynamic/gohugo-theme-ananke themes/ananke
# 基本設定
cat > hugo.toml << 'EOF'
baseURL = 'https://yourdomain.com'
languageCode = 'ja'
title = 'エージェント対応ブログ'
theme = 'ananke'
[params]
# エージェント最適化パラメータ
show_reading_time = false
show_sharing_links = false
show_comments = false
minimal_layout = true
[markup]
[markup.goldmark]
[markup.goldmark.renderer]
unsafe = true
hardWraps = false
[markup.highlight]
style = "github"
lineNos = false
# RSS設定
[outputFormats]
[outputFormats.RSS]
mediatype = "application/rss+xml"
baseName = "feed"
[outputs]
home = ["HTML", "RSS", "JSON"]
page = ["HTML"]
section = ["HTML", "RSS"]
EOF
# 最小テーマ作成
mkdir -p themes/agent-minimal/layouts/{_default,partials}
# ベーステンプレート
cat > themes/agent-minimal/layouts/_default/baseof.html << 'EOF'
<!DOCTYPE html>
<html lang="{{ .Site.LanguageCode }}">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>{{ if .IsHome }}{{ .Site.Title }}{{ else }}{{ .Title }} | {{ .Site.Title }}{{ end }}</title>
<!-- エージェント識別メタタグ -->
<meta name="author-type" content="agent">
<meta name="content-type" content="agent-readable">
<meta name="ai-friendly" content="true">
<!-- 構造化データ -->
<meta name="description" content="{{ with .Description }}{{ . }}{{ else }}{{ .Site.Params.description }}{{ end }}">
<meta name="robots" content="index, follow">
<!-- RSS -->
<link rel="alternate" type="application/rss+xml" title="{{ .Site.Title }}" href="{{ .Site.BaseURL }}/feed.xml">
<!-- 最小CSS -->
<style>
body { font-family: monospace; line-height: 1.6; max-width: 800px; margin: auto; padding: 20px; }
h1, h2, h3 { border-bottom: 1px solid #ccc; }
pre { background: #f5f5f5; padding: 10px; overflow-x: auto; }
code { background: #f5f5f5; padding: 2px 4px; }
.date { color: #666; font-size: 0.9em; }
.nav { margin-bottom: 20px; }
.nav a { margin-right: 10px; }
</style>
</head>
<body>
<nav class="nav">
<a href="{{ .Site.BaseURL }}">ホーム</a>
<a href="{{ .Site.BaseURL }}/posts">記事一覧</a>
<a href="{{ .Site.BaseURL }}/feed.xml">RSS</a>
</nav>
<main>
{{ block "main" . }}{{ end }}
</main>
<footer>
<hr>
<p>© {{ now.Format "2006" }} {{ .Site.Title }} | <a href="{{ .Site.BaseURL }}/feed.xml">RSS</a></p>
</footer>
</body>
</html>
EOF
# 記事一覧テンプレート
cat > themes/agent-minimal/layouts/_default/list.html << 'EOF'
{{ define "main" }}
<h1>{{ .Title }}</h1>
{{ range .Pages }}
<article>
<h2><a href="{{ .Permalink }}">{{ .Title }}</a></h2>
<div class="date">{{ .Date.Format "2006-01-02" }}</div>
<p>{{ .Summary }}</p>
<div>
{{ range .Params.tags }}
<span style="background: #eee; padding: 2px 6px; margin-right: 5px; font-size: 0.8em;">#{{ . }}</span>
{{ end }}
</div>
</article>
<hr>
{{ end }}
{{ end }}
EOF
# 個別記事テンプレート
cat > themes/agent-minimal/layouts/_default/single.html << 'EOF'
{{ define "main" }}
<article>
<h1>{{ .Title }}</h1>
<div class="date">
投稿日: {{ .Date.Format "2006-01-02 15:04" }}
{{ if .Params.tags }}
| タグ: {{ range .Params.tags }}<span style="background: #eee; padding: 2px 6px; margin-right: 5px;">#{{ . }}</span>{{ end }}
{{ end }}
</div>
<div class="content">
{{ .Content }}
</div>
<!-- 関連記事 -->
{{ if .Site.Params.show_related }}
<hr>
<h3>関連記事</h3>
{{ range first 3 (where .Site.RegularPages "Section" .Section) }}
<p><a href="{{ .Permalink }}">{{ .Title }}</a> ({{ .Date.Format "2006-01-02" }})</p>
{{ end }}
{{ end }}
</article>
{{ end }}
EOF
#!/bin/bash
# create-post.sh - エージェント最適化記事作成
create_agent_post() {
local title="$1"
local filename="$(echo "$title" | iconv -t ascii//TRANSLIT | sed 's/[^a-zA-Z0-9]/-/g' | tr '[:upper:]' '[:lower:]')"
local date="$(date -I)"
hugo new "posts/${date}-${filename}.md"
# フロントマター最適化
cat > "content/posts/${date}-${filename}.md" << EOF
---
title: "${title}"
date: $(date -Iseconds)
draft: false
tags: ["AI", "エージェント"]
description: "${title}の解説記事"
author-type: "agent"
content-structure: "linear"
---
# ${title}
この記事では${title}について説明します。
## 概要
## 詳細
## まとめ
EOF
echo "記事作成完了: content/posts/${date}-${filename}.md"
}
# 使用例
create_agent_post "エージェントのための情報アーキテクチャ"
# layouts/_default/rss.xml
{{- $pctx := . -}}
{{- if .IsHome -}}{{ $pctx = .Site }}{{- end -}}
{{- $pages := slice -}}
{{- if or $.IsHome $.IsSection -}}
{{- $pages = $pctx.RegularPages -}}
{{- else -}}
{{- $pages = $pctx.Pages -}}
{{- end -}}
{{- $limit := .Site.Config.Services.RSS.Limit -}}
{{- if ge $limit 1 -}}
{{- $pages = $pages | first $limit -}}
{{- end -}}
{{- printf "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"yes\"?>" | safeHTML }}
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>{{ if eq .Title .Site.Title }}{{ .Site.Title }}{{ else }}{{ with .Title }}{{.}} on {{ end }}{{ .Site.Title }}{{ end }}</title>
<link>{{ .Permalink }}</link>
<description>エージェント向け最新情報</description>
<generator>Hugo</generator>
<language>{{ .Site.LanguageCode }}</language>
<managingEditor>{{ .Site.Author.email }}{{ with .Site.Author.name }} ({{ . }}){{ end }}</managingEditor>
<webMaster>{{ .Site.Author.email }}{{ with .Site.Author.name }} ({{ . }}){{ end }}</webMaster>
<copyright>{{ .Site.Copyright }}</copyright>
<lastBuildDate>{{ .Date.Format "Mon, 02 Jan 2006 15:04:05 -0700" | safeHTML }}</lastBuildDate>
{{ with .OutputFormats.Get "RSS" }}
{{ printf "<atom:link href=%q rel=\"self\" type=%q />" .Permalink .MediaType | safeHTML }}
{{ end }}
{{- range $pages -}}
<item>
<title>{{ .Title }}</title>
<link>{{ .Permalink }}</link>
<pubDate>{{ .Date.Format "Mon, 02 Jan 2006 15:04:05 -0700" | safeHTML }}</pubDate>
<guid>{{ .Permalink }}</guid>
<description>{{ .Summary | html }}</description>
<!-- エージェント用メタデータ -->
<category>{{ range .Params.tags }}{{ . }}, {{ end }}</category>
<author>{{ .Params.author }}</author>
</item>
{{- end }}
</channel>
</rss>
# /etc/nginx/sites-available/agent-blog
server {
listen 80;
server_name yourdomain.com;
root /var/www/agent-blog/public;
index index.html;
# エージェント識別
add_header X-Content-Type "agent-optimized";
add_header X-AI-Friendly "true";
# 圧縮最適化
gzip on;
gzip_types text/plain text/css text/xml application/xml application/rss+xml text/javascript;
gzip_min_length 1000;
# キャッシュ設定
location ~* \.(css|js|png|jpg|jpeg|gif|svg)$ {
expires 1y;
add_header Cache-Control "public, immutable";
}
# HTML最適化
location / {
try_files $uri $uri/ =404;
add_header X-Content-Structure "linear";
add_header X-Navigation "simple";
}
# RSS専用設定
location /feed.xml {
add_header Content-Type "application/rss+xml; charset=utf-8";
add_header X-Update-Frequency "daily";
}
# ログ設定(エージェントアクセス分析用)
access_log /var/log/nginx/agent-blog-access.log combined;
error_log /var/log/nginx/agent-blog-error.log;
}
#!/bin/bash
# daily-blog-update.sh
cd /var/www/agent-blog
# git pull最新情報
git pull origin main
# Hugoビルド
hugo --minify
# RSS検証
xmllint --noout public/feed.xml
if [ $? -eq 0 ]; then
echo "RSS feed valid"
else
echo "RSS feed validation failed"
exit 1
fi
# nginx設定リロード
sudo nginx -t && sudo systemctl reload nginx
echo "ブログ更新完了: $(date)"
#!/bin/bash
# seo-check.sh
check_agent_meta() {
local url="$1"
echo "エージェント最適化チェック: $url"
# 必須メタタグ確認
curl -s "$url" | grep -q 'name="author-type"' && echo "✓ author-type メタタグ" || echo "✗ author-type メタタグ"
curl -s "$url" | grep -q 'name="ai-friendly"' && echo "✓ ai-friendly メタタグ" || echo "✗ ai-friendly メタタグ"
# RSS確認
curl -s "${url}/feed.xml" | xmllint --noout - && echo "✓ RSS valid" || echo "✗ RSS invalid"
# JavaScript無し確認
curl -s "$url" | grep -q '<script' && echo "✗ JavaScript detected" || echo "✓ No JavaScript"
}
check_agent_meta "https://yourdomain.com"
author-type=agent メタタグの詳細な実装については kairyuu.net を参照。
Generated Mar 1, 2026
Research institutions can use this skill to create AI-readable documentation portals for sharing research papers, technical reports, and experimental data. The minimal HTML structure and agent-specific meta tags ensure AI systems can efficiently crawl and analyze scientific content for literature reviews and knowledge extraction.
Companies can deploy internal knowledge bases optimized for AI agents to help employee assistants access company policies, technical documentation, and procedural guides. The RSS feed optimization allows real-time content updates to be consumed by AI systems monitoring organizational changes.
Media organizations can create AI-friendly news blogs that allow monitoring agents to efficiently track and summarize news content. The structured content format with clear date stamps and tags enables AI systems to perform trend analysis and real-time news monitoring without JavaScript interference.
Online education providers can build course material blogs that AI tutoring systems can easily parse and reference. The linear content structure and agent-specific metadata help AI educational assistants retrieve and present learning materials to students in optimized formats.
Software companies can create AI-optimized documentation for their APIs and developer tools, enabling AI coding assistants to quickly reference and incorporate documentation into code suggestions. The minimal theme ensures reliable parsing by development-focused AI agents.
Offer a managed service where businesses can host their AI-friendly blogs with automated content optimization. Revenue comes from monthly subscriptions based on content volume and AI agent access frequency, with premium tiers offering advanced analytics on agent interactions.
Provide professional services to help organizations implement and customize the Hugo Blog Agent setup for their specific needs. Revenue is generated through project-based fees for initial setup, training, and ongoing maintenance contracts for larger enterprise deployments.
Develop and sell specialized tools that automatically optimize existing content for AI agent consumption, integrating with the Hugo framework. Revenue comes from software licensing fees and enterprise deployment packages for content teams needing to convert legacy materials.
💬 Integration Tip
Start by implementing the minimal theme with agent-specific meta tags, then gradually add RSS optimization for real-time content updates. Test with actual AI agents to validate parsing effectiveness before full deployment.
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