Agent skill

Programmatic SEO

by OpenClaudia in OpenClaudia/openclaudia-skills

Create SEO-optimized pages at scale using programmatic/template-based approaches.

MITAuto-check passedMarketing & SEO

Install Programmatic SEO

skills CLI
$ npx skills add OpenClaudia/openclaudia-skills --skill programmatic-seo -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install OpenClaudia/openclaudia-skills programmatic-seo --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/programmatic-seo .claude/skills/programmatic-seo && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
programmatic-seo
GitHub stars
711
Token cost
~3.3k tokens
SKILL.md length
898 words
Files
1
Skills in repo
74
Repo updated
First seen
Licence
MIT

At a glance

Create SEO-optimized pages at scale using programmatic/template-based approaches.

  • Works in 7 steps: Identify the Page Pattern → Build the Data Source → Create the Page Template → …
  • The user says programmatic SEO
  • SKILL.md covers What is Programmatic SEO?, Process, Output and Common Pitfalls
  • Reaches schema.org and sitemaps.org

What it does

Programmatic SEO is an agent skill from OpenClaudia/openclaudia-skills. Create SEO-optimized pages at scale using programmatic/template-based approaches. Use when the user says "programmatic SEO", "pSEO", "pages at scale", "template pages", "dynamic SEO pages", "auto-generate pages", "landing pages at scale", "city pages", "comparison pages", or wants to create many similar pages targeting different keywords.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Programmatic SEO. The repository describes itself as: 77 open-source marketing skills for Claude Code, Codex, and other AI coding agents. SEO, content, email, ads, analytics, and growth. The licence is MIT.

When your agent uses it

  • The user says programmatic SEO
  • Dynamic SEO pages
  • Auto-generate pages
  • Landing pages at scale

Example prompts

  • “programmatic SEO”
  • “pages at scale”
  • “template pages”
  • “/programmatic-seo”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Identify the Page Pattern
  2. Build the Data Source
  3. Create the Page Template
  4. Next.js Implementation
  5. Internal Linking Strategy
  6. Indexation Management
  7. Meta Tag Formulas

What it can do on your machine

Read from SKILL.md and the folder at commit 28bf209. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • schema.org
    • sitemaps.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Programmatic SEO loads about 3.3k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 898 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from OpenClaudia/openclaudia-skills at commit 28bf209, republished under its MIT licence (© OpenClaudia). 898 words, ~3,337 tokens.

Download SKILL.mdSave it as .claude/skills/programmatic-seo/SKILL.md (or your agent's skills folder).
name
programmatic-seo
description
Create SEO-optimized pages at scale using programmatic/template-based approaches. Use when the user says "programmatic SEO", "pSEO", "pages at scale", "template pages", "dynamic SEO pages", "auto-generate pages", "landing pages at scale", "city pages", "comparison pages", or wants to create many similar pages targeting different keywords.

Programmatic SEO Skill

You are an expert in programmatic SEO (pSEO) -- the strategy of creating large numbers of targeted pages using templates and data. Help users identify page patterns, build templates, and deploy at scale while avoiding thin content penalties.

What is Programmatic SEO?

Programmatic SEO creates pages at scale by combining:

  • A page template (layout + structure)
  • A data source (database, API, CSV)
  • Dynamic content (unique per page, not just variable substitution)
  • SEO optimization (meta tags, internal links, schema)

Examples of successful pSEO:

  • Zapier: "How to connect {App A} to {App B}" (150K+ pages)
  • Nomad List: "{City} for digital nomads" (1000+ city pages)
  • Wise: "{Currency A} to {Currency B} exchange rate" (10K+ pages)
  • G2: "{Software} reviews" (100K+ product pages)
  • Tripadvisor: "Best {type} in {city}" (millions of pages)

Process

Step 1: Identify the Page Pattern

Help the user find their pSEO opportunity. Look for patterns where:

Pattern formula: {Modifier} + {Head Term} + {Qualifier}

Pattern TypeFormulaExampleVolume Potential
Location + Service"{service} in {city}""plumber in Austin"Cities x Services
Comparison"{product A} vs {product B}""Notion vs Asana"nC2 combinations
Integration"{tool A} + {tool B} integration""Slack Salesforce integration"Tools x Tools
Template/Example"{type} template""invoice template"Types count
Stats/Data"{topic} statistics {year}""remote work statistics 2025"Topics x Years
Glossary"What is {term}""What is APR"Terms count
Best/Top"Best {product} for {use case}""Best CRM for startups"Products x Uses
Review"{product} review""Airtable review"Products count
Alternative"{product} alternatives""Slack alternatives"Products count
Cost/Pricing"How much does {service} cost""How much does a website cost"Services count

Qualification criteria for a good pSEO opportunity:

  • Pattern has 100+ possible pages minimum
  • Each combination has measurable search volume (even 10-50/mo is fine at scale)
  • You can generate genuinely useful, unique content for each page
  • The data is available (API, database, web scraping)
  • Competitors aren't already dominating with better data
  • Pages serve real user intent (not just keyword stuffing)
Step 2: Build the Data Source

Define the data model that powers the pages:

typescript
// Example: City + Service pages
interface PageData {
  // URL parameters
  slug: string;           // "plumber-in-austin-tx"
  city: string;           // "Austin"
  state: string;          // "TX"
  service: string;        // "plumber"

  // SEO fields
  title: string;          // "Best Plumber in Austin, TX | Top 10 for 2025"
  metaDescription: string; // "Find the best plumber in Austin, TX..."
  h1: string;             // "Best Plumber in Austin, TX"

  // Dynamic content
  providers: Provider[];  // Local providers data
  avgCost: number;        // Average cost in this market
  reviewCount: number;    // Total reviews aggregated
  faqs: FAQ[];            // Location-specific FAQs

  // Related pages (internal linking)
  nearbyPages: string[];  // Nearby cities
  relatedServices: string[]; // Related services
}

Data sources to consider:

  • Public APIs (government data, Wikipedia, industry databases)
  • Web scraping (with permission/robots.txt compliance)
  • User-generated content (reviews, contributions)
  • AI-generated unique analysis per entity
  • Licensed data (paid data providers)
  • Internal product data (for SaaS/e-commerce)
Step 3: Create the Page Template

Build a template that generates high-quality, unique pages. Critical principle: each page must provide standalone value.

Template Structure
markdown
## Page Template: {Pattern Name}

### Above the Fold
- H1: {dynamic title}
- Key stat or hook: {dynamic data point}
- Quick summary: 2-3 sentences with unique data
- CTA (if commercial intent)

### Main Content Section 1: Overview
- {Entity}-specific introduction (NOT generic)
- Unique data point 1: {dynamic}
- Unique data point 2: {dynamic}
- Contextual explanation

### Main Content Section 2: Detailed Analysis
- Comparison table or detailed breakdown
- {Entity}-specific insights
- Data visualizations if applicable

### Main Content Section 3: Practical Information
- How-to or action steps
- Costs, timing, or specifications
- Location-specific or entity-specific details

### Related Content
- Internal links to related pages (same cluster)
- Links to parent/pillar page
- Links to sibling pages

### FAQ Section
- 3-5 questions specific to this entity
- Answers with unique data points

### Schema Markup
- Appropriate structured data for page type
Avoiding Thin Content

The #1 risk in pSEO is thin content. Every page must pass this checklist:

CheckRequirementHow
Unique content> 60% of visible text is unique to this pageDynamic data, unique analysis, UGC
Sufficient depth> 500 words of substantive content per pageTemplate sections + dynamic content
Unique dataAt least 2 data points unique to this entityAPI data, calculations, aggregations
Useful to visitorPage answers the searcher's query fullyMatch search intent
Not auto-generated feelReads naturally, not like a templateVaried sentence structures, context
Internal valueLinks to and from other relevant pagesRelated pages section, breadcrumbs
Visual contentAt least 1 unique or relevant imageMaps, charts, entity images
Step 4: Next.js Implementation
Dynamic Routes (App Router)
typescript
// app/[service]/[city]/page.tsx

import { Metadata } from 'next';
import { notFound } from 'next/navigation';
import { getPageData, getAllPages } from '@/lib/pseo-data';

interface Props {
  params: { service: string; city: string };
}

// Generate static paths at build time
export async function generateStaticParams() {
  const pages = await getAllPages();
  return pages.map((page) => ({
    service: page.serviceSlug,
    city: page.citySlug,
  }));
}

// Dynamic meta tags
export async function generateMetadata({ params }: Props): Promise<Metadata> {
  const data = await getPageData(params.service, params.city);
  if (!data) return {};

  return {
    title: data.title,
    description: data.metaDescription,
    alternates: {
      canonical: `https://example.com/${params.service}/${params.city}`,
    },
    openGraph: {
      title: data.ogTitle,
      description: data.ogDescription,
      url: `https://example.com/${params.service}/${params.city}`,
      type: 'website',
    },
  };
}

export default async function Page({ params }: Props) {
  const data = await getPageData(params.service, params.city);
  if (!data) notFound();

  const jsonLd = {
    '@context': 'https://schema.org',
    '@type': 'WebPage',
    name: data.title,
    description: data.metaDescription,
    // ... additional schema
  };

  return (
    <>
      <script
        type="application/ld+json"
        dangerouslySetInnerHTML={{ __html: JSON.stringify(jsonLd) }}
      />
      {/* Page component tree */}
    </>
  );
}
Sitemap Generation
typescript
// app/sitemap.ts
import { MetadataRoute } from 'next';
import { getAllPages } from '@/lib/pseo-data';

export default async function sitemap(): Promise<MetadataRoute.Sitemap> {
  const pages = await getAllPages();

  return pages.map((page) => ({
    url: `https://example.com/${page.serviceSlug}/${page.citySlug}`,
    lastModified: page.updatedAt,
    changeFrequency: 'monthly',
    priority: 0.7,
  }));
}

For large sitemaps (50,000+ URLs), use sitemap index:

typescript
// app/sitemap.xml/route.ts
import { getAllPageCount } from '@/lib/pseo-data';

export async function GET() {
  const totalPages = await getAllPageCount();
  const sitemapCount = Math.ceil(totalPages / 50000);

  const sitemaps = Array.from({ length: sitemapCount }, (_, i) =>
    `<sitemap><loc>https://example.com/sitemap-${i}.xml</loc></sitemap>`
  ).join('');

  return new Response(
    `<?xml version="1.0" encoding="UTF-8"?>
    <sitemapindex xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
      ${sitemaps}
    </sitemapindex>`,
    { headers: { 'Content-Type': 'application/xml' } }
  );
}
Robots.txt Management
typescript
// app/robots.ts
import { MetadataRoute } from 'next';

export default function robots(): MetadataRoute.Robots {
  return {
    rules: [
      {
        userAgent: '*',
        allow: '/',
        disallow: ['/api/', '/admin/'],
      },
    ],
    sitemap: 'https://example.com/sitemap.xml',
  };
}
Show full SKILL.md (372 more words)Show less
Step 5: Internal Linking Strategy

Internal linking is critical for pSEO. Implement these patterns:

1. Hub-and-Spoke:

Hub: /services/plumber/
  |- /plumber/austin-tx
  |- /plumber/dallas-tx
  |- /plumber/houston-tx

2. Sibling Links: Each city page links to 5-10 nearby city pages for the same service.

3. Cross-Category Links: Each city page links to other services in the same city.

4. Breadcrumb Navigation:

Home > Services > Plumber > Austin, TX

5. Footer/Sidebar Links: Popular pages and category indexes.

Implementation pattern:

typescript
// Internal linking component
function RelatedPages({ currentSlug, relatedPages }) {
  return (
    <section>
      <h2>Related Pages</h2>
      <div className="grid grid-cols-2 md:grid-cols-3 gap-4">
        {relatedPages.map((page) => (
          <Link
            key={page.slug}
            href={`/${page.slug}`}
            className="text-blue-600 hover:underline"
          >
            {page.title}
          </Link>
        ))}
      </div>
    </section>
  );
}
Step 6: Indexation Management

When deploying thousands of pages, manage indexation carefully:

Gradual rollout:

  1. Deploy 50-100 pages first
  2. Monitor indexation in Google Search Console
  3. Check for "Discovered - currently not indexed" and "Crawled - currently not indexed"
  4. If indexation rate > 80%, continue deploying in batches of 500-1000
  5. If indexation rate < 50%, improve page quality before deploying more

Indexation signals:

  • Submit sitemap to Google Search Console
  • Use IndexNow API (Bing, Yandex) for faster discovery
  • Internal link from high-authority existing pages
  • Share initial pages on social media for crawl signals

Quality thresholds:

  • If Google indexes < 30% of pages, your template likely produces thin content
  • If pages get indexed then dropped, content quality is borderline
  • Monitor "Page experience" and "Core Web Vitals" in GSC for pSEO pages
Step 7: Meta Tag Formulas

Use these formulas for generating meta tags at scale:

Title tag formulas (50-60 chars):

"{Primary Keyword} in {Location} | {Brand}"
"Best {Service} in {City}, {State} ({Year})"
"{Product A} vs {Product B}: {Differentiator}"
"{Number} Best {Category} for {Use Case} ({Year})"
"How Much Does {Service} Cost in {City}? ({Year} Pricing)"

Meta description formulas (150-160 chars):

"Find the best {service} in {city}. Compare {X} local providers, read {Y} reviews, and get free quotes. Average cost: ${Z}."
"Detailed comparison of {A} vs {B}. See features, pricing, pros/cons, and which is better for {use case}."
"{X} best {category} for {audience}. We analyzed {Y} options based on {criteria}. Updated for {year}."

H1 formulas:

"Best {Service} in {City}, {State}"
"{Product A} vs {Product B}: Complete Comparison"
"{Number} Best {Category} for {Use Case}"

Output

When helping a user with programmatic SEO, deliver:

  1. Opportunity Analysis - The pattern, estimated page count, volume potential
  2. Data Model - TypeScript interface for the page data
  3. Page Template - Complete template with all sections
  4. Sample Pages - 2-3 fully rendered example pages
  5. Implementation Plan - Next.js code for routes, sitemap, robots.txt
  6. Internal Linking Strategy - How pages connect
  7. Indexation Plan - Rollout schedule and monitoring metrics
  8. Quality Checklist - Per-page quality criteria

Common Pitfalls

  • Template stuffing: Just swapping city names in identical text = thin content penalty
  • No unique data: Pages without entity-specific data points add no value
  • Over-generation: Creating pages for combinations with zero search volume wastes crawl budget
  • Ignoring cannibalization: Overlapping pages compete with each other. Map one keyword per page.
  • No internal links: Orphan pages won't get crawled or ranked
  • All at once: Deploying 100K pages overnight looks unnatural. Batch them.
  • Ignoring noindex: Some pages may not deserve indexing. Use noindex for low-quality or duplicate combinations.

© OpenClaudia, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/programmatic-seo of OpenClaudia/openclaudia-skills.

Open the folder on GitHubat commit 28bf209

Compare with similar skills

Programmatic SEO next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Programmatic SEO compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Programmatic SEO this skillOpenClaudia/openclaudia-skills711—~3.3kAutomated safety check: PassMIT
SEO Competitor Comparison PagesAgriciDaniel/claude-seo18k5 repos~1.9kAutomated safety check: PassMIT
Universal SEO AnalysisAgriciDaniel/claude-seo18k—~4.9kAutomated safety check: PassMIT
Competitor Alternativesfreekmurze/dotfiles1k24 repos~2kAutomated safety check: PassNone
Programmatic SEOfreekmurze/dotfiles1k22 repos~1.7kAutomated safety check: PassNone
Directory Submissionscoreyhaines31/marketingskills54k2 repos~6.3kAutomated safety check: PassMIT

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Categories

Questions about Programmatic SEO

What does Programmatic SEO do?

Create SEO-optimized pages at scale using programmatic/template-based approaches. Programmatic SEO is an agent skill from OpenClaudia/openclaudia-skills. Create SEO-optimized pages at scale using programmatic/template-based approaches.

When should I use Programmatic SEO?

Programmatic SEO fits situations like: the user says programmatic SEO; dynamic SEO pages; auto-generate pages; landing pages at scale.

How do I install Programmatic SEO in Claude Code?

Run `npx skills add OpenClaudia/openclaudia-skills --skill programmatic-seo -a claude-code`. Or copy the skill folder (skills/programmatic-seo in OpenClaudia/openclaudia-skills) into .claude/skills/programmatic-seo in your project. Claude Code loads it when a task matches its description.

How do I install Programmatic SEO in Codex?

Run `npx skills add OpenClaudia/openclaudia-skills --skill programmatic-seo -a codex`. Or copy the skill folder (skills/programmatic-seo in OpenClaudia/openclaudia-skills) into .agents/skills/programmatic-seo in your project. Codex loads it when a task matches its description.

Can I use Programmatic SEO in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OpenClaudia/openclaudia-skills --skill programmatic-seo -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/programmatic-seo, .gemini/skills/programmatic-seo, .github/skills/programmatic-seo and .opencode/skills/programmatic-seo in your project.

What does Programmatic SEO need to run?

SKILL.md names no scripts, command-line tools or credentials: Programmatic SEO is instructions for the agent only.

Does Programmatic SEO access the network?

SKILL.md names 2 domains. In commands or code: schema.org and sitemaps.org; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Programmatic SEO safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Programmatic SEO use?

Programmatic SEO is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Programmatic SEO use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Programmatic SEO?

Skills that share tags, products or a category with Programmatic SEO: SEO Competitor Comparison Pages (AgriciDaniel/claude-seo, 18k stars), Universal SEO Analysis (AgriciDaniel/claude-seo, 18k stars), Competitor Alternatives (freekmurze/dotfiles, 1k stars) and Programmatic SEO (freekmurze/dotfiles, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Programmatic SEO?

OpenClaudia (a GitHub organization) maintains it in OpenClaudia/openclaudia-skills, which has 711 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on September 18, 2026.

Source: OpenClaudia/openclaudia-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.