
Programmatic Seo
- 540 installs
- 23.5k repo stars
- Updated July 17, 2026
- alirezarezvani/claude-skills
programmatic-seo is a Claude Code skill that plans large-scale comparison and variable URL sets from JSON templates so developers who build SEO landing pages at scale can map page patterns before generation.
About
Programmatic SEO is a generator skill centered on a small Python tool that turns a JSON template and variable lists into every planned URL slug for template-driven organic pages—think tool-vs-competitor comparison paths at scale. developers use it after they know their keyword matrix but before bulk page generation or CMS import, so agents and scripts share one canonical URL plan instead of guessing paths in chat. It supports demo mode, file-based configs, and JSON stdout for downstream content or static-site generators. Pair the output with your content templates, internal linking rules, and sitemap generation during Ship perf checks if crawl budget matters. It does not write copy or guarantee rankings; it structures URL inventory so programmatic pages stay consistent and deduplicated.
- Python url_pattern_generator.py expands template variables via Cartesian product
- JSON config: template string, variables map, optional base_url
- Skips self-comparison combos when variable values would duplicate in one URL
- CLI supports demo mode, data file input, and --json output for agent pipelines
Programmatic Seo by the numbers
- 540 all-time installs (skills.sh)
- Ranked #719 of 1,879 Marketing & SEO skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Jul 31, 2026 (Skillselion catalog sync)
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| Installs | 540 |
|---|---|
| repo stars | ★ 23.5k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 17, 2026 |
| Repository | alirezarezvani/claude-skills ↗ |
How do you plan programmatic SEO URL patterns at scale?
Plan large-scale comparison and variable URL sets from JSON templates before generating SEO landing pages.
Who is it for?
Developers planning template-driven comparison or variable landing pages where URL cardinality must be computed before content generation.
Skip if: Single static landing pages, sites without template-based URL expansion, or teams with no JSON data source defining page variables.
When should I use this skill?
A developer needs to plan large-scale comparison URLs, variable SEO landing pages, or cartesian-product URL sets from JSON templates.
What you get
URL pattern list, page template plan, and JSON output mapping variable combinations to SEO landing routes.
- URL pattern list
- Page template plan
- JSON output of planned routes
By the numbers
- Bundles Python script url_pattern_generator.py
- Supports 3 run modes: demo, data file input, and --json output
Files
Programmatic SEO
You are an expert in programmatic SEO—building SEO-optimized pages at scale using templates and data. Your goal is to create pages that rank, provide value, and avoid thin content penalties.
Initial Assessment
Check for product marketing context first: If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Before designing a programmatic SEO strategy, understand:
1. Business Context
- What's the product/service?
- Who is the target audience?
- What's the conversion goal for these pages?
2. Opportunity Assessment
- What search patterns exist?
- How many potential pages?
- What's the search volume distribution?
3. Competitive Landscape
- Who ranks for these terms now?
- What do their pages look like?
- Can you realistically compete?
---
Core Principles
1. Unique Value Per Page
- Every page must provide value specific to that page
- Not just swapped variables in a template
- Maximize unique content—the more differentiated, the better
2. Proprietary Data Wins
Hierarchy of data defensibility: 1. Proprietary (you created it) 2. Product-derived (from your users) 3. User-generated (your community) 4. Licensed (exclusive access) 5. Public (anyone can use—weakest)
3. Clean URL Structure
Always use subfolders, not subdomains:
- Good:
yoursite.com/templates/resume/ - Bad:
templates.yoursite.com/resume/
4. Genuine Search Intent Match
Pages must actually answer what people are searching for.
5. Quality Over Quantity
Better to have 100 great pages than 10,000 thin ones.
6. Avoid Google Penalties
- No doorway pages
- No keyword stuffing
- No duplicate content
- Genuine utility for users
---
The 12 Playbooks (Overview)
| Playbook | Pattern | Example |
|---|---|---|
| Templates | "[Type] template" | "resume template" |
| Curation | "best [category]" | "best website builders" |
| Conversions | "[X] to [Y]" | "$10 USD to GBP" |
| Comparisons | "[X] vs [Y]" | "webflow vs wordpress" |
| Examples | "[type] examples" | "landing page examples" |
| Locations | "[service] in [location]" | "dentists in austin" |
| Personas | "[product] for [audience]" | "crm for real estate" |
| Integrations | "[product A] [product B] integration" | "slack asana integration" |
| Glossary | "what is [term]" | "what is pSEO" |
| Translations | Content in multiple languages | Localized content |
| Directory | "[category] tools" | "ai copywriting tools" |
| Profiles | "[entity name]" | "stripe ceo" |
---
Choosing Your Playbook
| If you have... | Consider... |
|---|---|
| Proprietary data | Directories, Profiles |
| Product with integrations | Integrations |
| Design/creative product | Templates, Examples |
| Multi-segment audience | Personas |
| Local presence | Locations |
| Tool or utility product | Conversions |
| Content/expertise | Glossary, Curation |
| Competitor landscape | Comparisons |
You can layer multiple playbooks (e.g., "Best coworking spaces in San Diego").
---
Implementation Framework
1. Keyword Pattern Research
Identify the pattern:
- What's the repeating structure?
- What are the variables?
- How many unique combinations exist?
Validate demand:
- Aggregate search volume
- Volume distribution (head vs. long tail)
- Trend direction
2. Data Requirements
Identify data sources:
- What data populates each page?
- Is it first-party, scraped, licensed, public?
- How is it updated?
3. URL Pattern Generation (bundled tool)
Generate and sanity-check the URL space before building templates:
python3 scripts/url_pattern_generator.py pattern.json --json # no arg = embedded demoGive it the template (e.g., {tool}-vs-{competitor}-comparison), base URL, and variable lists; it expands the combinations, reports the page count, and flags slug problems. If the expansion produces more pages than you have unique data for (see step 2), cut variables — don't ship thin pages.
4. Template Design
Page structure:
- Header with target keyword
- Unique intro (not just variables swapped)
- Data-driven sections
- Related pages / internal links
- CTAs appropriate to intent
Ensuring uniqueness:
- Each page needs unique value
- Conditional content based on data
- Original insights/analysis per page
5. Internal Linking Architecture
Hub and spoke model:
- Hub: Main category page
- Spokes: Individual programmatic pages
- Cross-links between related spokes
Avoid orphan pages:
- Every page reachable from main site
- XML sitemap for all pages
- Breadcrumbs with structured data
6. Indexation Strategy
- Prioritize high-volume patterns
- Noindex very thin variations
- Manage crawl budget thoughtfully
- Separate sitemaps by page type
---
Quality Checks
Pre-Launch Checklist
Content quality:
- [ ] Each page provides unique value
- [ ] Answers search intent
- [ ] Readable and useful
Technical SEO:
- [ ] Unique titles and meta descriptions
- [ ] Proper heading structure
- [ ] Schema markup implemented
- [ ] Page speed acceptable
Internal linking:
- [ ] Connected to site architecture
- [ ] Related pages linked
- [ ] No orphan pages
Indexation:
- [ ] In XML sitemap
- [ ] Crawlable
- [ ] No conflicting noindex
Post-Launch Monitoring
Track: Indexation rate, Rankings, Traffic, Engagement, Conversion
Watch for: Thin content warnings, Ranking drops, Manual actions, Crawl errors
---
Common Mistakes
- Thin content: Just swapping city names in identical content
- Keyword cannibalization: Multiple pages targeting same keyword
- Over-generation: Creating pages with no search demand
- Poor data quality: Outdated or incorrect information
- Ignoring UX: Pages exist for Google, not users
---
Output Format
Strategy Document
- Opportunity analysis
- Implementation plan
- Content guidelines
Page Template
- URL structure
- Title/meta templates
- Content outline
- Schema markup
---
Task-Specific Questions
1. What keyword patterns are you targeting? 2. What data do you have (or can acquire)? 3. How many pages are you planning? 4. What does your site authority look like? 5. Who currently ranks for these terms? 6. What's your technical stack?
---
Related Skills
- seo-audit — WHEN: programmatic pages are live and you need to verify indexation, detect thin content penalties, or diagnose ranking drops across the page set. WHEN NOT: don't run an audit before you've even designed the template strategy.
- schema-markup — WHEN: the chosen playbook benefits from structured data (e.g., Product, Review, FAQ, LocalBusiness schemas on location or comparison pages). WHEN NOT: don't prioritize schema before the core template and data pipeline are working.
- competitor-alternatives — WHEN: the playbook selected is Comparisons ("[X] vs [Y]") or Alternatives; that skill has dedicated comparison page frameworks. WHEN NOT: don't overlap with it for non-comparison playbooks like Locations or Glossary.
- content-strategy — WHEN: user needs to decide which pSEO playbook to pursue or how it fits into a broader editorial strategy. WHEN NOT: don't use when the playbook is decided and the task is pure implementation.
- site-architecture — WHEN: the pSEO build is large (500+ pages) and hub-and-spoke or crawl budget management decisions need explicit architectural planning. WHEN NOT: skip for small pSEO pilots (<100 pages) where default hub-and-spoke is sufficient.
- marketing-context — WHEN: always check
.claude/product-marketing-context.mdfirst to understand ICP, value prop, and conversion goals before keyword pattern research. WHEN NOT: skip if the user has provided all context directly in the conversation.
---
Communication
All programmatic SEO output follows this quality standard:
- Lead with the Opportunity Analysis — estimated page count, aggregate search volume, and data source feasibility
- Strategy documents use the Strategy → Template → Checklist structure consistently
- Every playbook recommendation is paired with a real-world example and a data source suggestion
- Call out thin-content risk explicitly when the data source is public/scraped
- Pre-launch checklists are always included before any "go build it" instruction
- Post-launch monitoring metrics are defined before launch, not after problems appear
---
Proactive Triggers
Automatically surface programmatic-seo when:
1. "We want to rank for hundreds of keywords" — User describes a large keyword set with a repeating pattern; immediately map it to one of the 12 playbooks. 2. Competitor has a directory or integration page set — When competitive analysis reveals a rival ranking via pSEO; proactively propose matching or superior playbook. 3. Product has many integrations or use-case personas — Detect integration or persona variety in the product description; suggest Integrations or Personas playbooks. 4. Location-based service — Any mention of serving multiple cities or regions triggers the Locations playbook discussion. 5. seo-audit reveals keyword gap cluster — When seo-audit finds dozens of unaddressed queries following a pattern, proactively suggest a pSEO build to fill the gap at scale.
---
Output Artifacts
| Artifact | Format | Description |
|---|---|---|
| Opportunity Analysis | Markdown table | Keyword patterns × estimated volume × data source × difficulty rating |
| Playbook Selection Matrix | Table | If/then mapping of business context to recommended playbook with rationale |
| Page Template Spec | Markdown with annotated sections | URL pattern, title/meta templates, content block structure, unique value rules |
| Pre-Launch Checklist | Checkbox list | Content quality, technical SEO, internal linking, indexation gates |
| Post-Launch Monitoring Plan | Table | Metrics to track × tools × alert thresholds × review cadence |
#!/usr/bin/env python3
"""
URL Pattern Generator for Programmatic SEO
Generates URL patterns and page templates from a data source.
Helps plan template-based page generation at scale.
Usage:
python3 url_pattern_generator.py # Demo mode
python3 url_pattern_generator.py data.json # From data file
python3 url_pattern_generator.py data.json --json # JSON output
Input format (JSON):
{
"template": "{tool}-vs-{competitor}-comparison",
"variables": {
"tool": ["slack", "teams", "discord"],
"competitor": ["zoom", "webex"]
},
"base_url": "https://example.com/compare"
}
"""
import json
import sys
import os
from itertools import product as cartesian_product
def generate_urls(config):
"""Generate all URL combinations from template and variables."""
template = config["template"]
variables = config["variables"]
base_url = config.get("base_url", "https://example.com")
var_names = list(variables.keys())
var_values = [variables[name] for name in var_names]
urls = []
for combo in cartesian_product(*var_values):
mapping = dict(zip(var_names, combo))
# Skip self-comparisons
values = list(mapping.values())
if len(values) != len(set(values)):
continue
slug = template
for key, val in mapping.items():
slug = slug.replace("{" + key + "}", str(val).lower().replace(" ", "-"))
url = f"{base_url}/{slug}"
urls.append({
"url": url,
"slug": slug,
"variables": mapping
})
return urls
def analyze_patterns(urls, config):
"""Analyze generated URL patterns for SEO concerns."""
issues = []
warnings = []
# Check total page count
total = len(urls)
if total > 10000:
issues.append(f"Generating {total:,} pages — risk of thin content penalty. Consider narrowing variables.")
elif total > 1000:
warnings.append(f"Generating {total:,} pages — ensure each has unique, substantial content.")
# Check URL length
long_urls = [u for u in urls if len(u["url"]) > 75]
if long_urls:
warnings.append(f"{len(long_urls)} URLs exceed 75 chars — may truncate in SERPs.")
# Check for potential duplicate intent
template = config["template"]
var_names = list(config["variables"].keys())
if len(var_names) >= 2:
# Check if swapped variables create duplicate intent
# e.g., "slack-vs-zoom" and "zoom-vs-slack"
seen_pairs = set()
dupes = 0
for u in urls:
vals = tuple(sorted(u["variables"].values()))
if vals in seen_pairs:
dupes += 1
seen_pairs.add(vals)
if dupes > 0:
warnings.append(f"{dupes} URL pairs may have duplicate search intent (e.g., 'A vs B' and 'B vs A'). Consider canonicalizing.")
# Score
score = 100
score -= len(issues) * 20
score -= len(warnings) * 5
score = max(0, min(100, score))
return {
"total_pages": total,
"avg_url_length": sum(len(u["url"]) for u in urls) // max(len(urls), 1),
"long_urls": len(long_urls),
"issues": issues,
"warnings": warnings,
"score": score
}
def format_report(urls, analysis, config):
"""Format human-readable report."""
lines = []
lines.append("")
lines.append("=" * 60)
lines.append(" PROGRAMMATIC SEO — URL PATTERN REPORT")
lines.append("=" * 60)
lines.append("")
lines.append(f" Template: {config['template']}")
lines.append(f" Base URL: {config.get('base_url', 'https://example.com')}")
lines.append(f" Variables: {len(config['variables'])} ({', '.join(config['variables'].keys())})")
lines.append(f" Total Pages: {analysis['total_pages']:,}")
lines.append(f" Avg URL Len: {analysis['avg_url_length']} chars")
lines.append("")
# Score
score = analysis["score"]
bar_filled = score // 5
bar = "█" * bar_filled + "░" * (20 - bar_filled)
lines.append(f" PATTERN SCORE: {score}/100")
lines.append(f" [{bar}]")
lines.append("")
# Issues
if analysis["issues"]:
lines.append(" 🔴 ISSUES:")
for issue in analysis["issues"]:
lines.append(f" • {issue}")
lines.append("")
if analysis["warnings"]:
lines.append(" 🟡 WARNINGS:")
for warn in analysis["warnings"]:
lines.append(f" • {warn}")
lines.append("")
# Sample URLs
lines.append(" 📋 SAMPLE URLS (first 10):")
for u in urls[:10]:
lines.append(f" {u['url']}")
if len(urls) > 10:
lines.append(f" ... and {len(urls) - 10} more")
lines.append("")
return "\n".join(lines)
SAMPLE_CONFIG = {
"template": "{tool}-vs-{competitor}-comparison",
"variables": {
"tool": ["slack", "microsoft-teams", "discord", "zoom"],
"competitor": ["slack", "microsoft-teams", "discord", "zoom", "webex", "google-meet"]
},
"base_url": "https://example.com/compare"
}
def main():
use_json = "--json" in sys.argv
args = [a for a in sys.argv[1:] if a != "--json"]
if args and os.path.isfile(args[0]):
with open(args[0]) as f:
config = json.load(f)
else:
if not args:
print("[Demo mode — using sample comparison page config]")
config = SAMPLE_CONFIG
urls = generate_urls(config)
analysis = analyze_patterns(urls, config)
if use_json:
print(json.dumps({
"config": config,
"urls": urls,
"analysis": analysis
}, indent=2))
else:
print(format_report(urls, analysis, config))
if __name__ == "__main__":
main()
Related skills
How it compares
Pick programmatic-seo over manual sitemap planning when URL cardinality depends on cartesian combinations of template variables.
FAQ
What input format does programmatic-seo url_pattern_generator.py expect?
programmatic-seo accepts JSON with a template string like {tool}-vs-{competitor}-comparison, a variables object mapping keys to value arrays, and a base_url. The Python script expands all combinations via cartesian product.
How do you run programmatic-seo URL pattern generation?
programmatic-seo ships url_pattern_generator.py runnable as python3 url_pattern_generator.py for demo mode, python3 url_pattern_generator.py data.json from a file, or with --json for structured output.
Is Programmatic Seo safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.