Reverse-engineer how competitors do programmatic SEO. Detects URL pattern clusters (vs/, integrations/, for-{industry}/), estimates page count per pattern, analyzes template quality, infers which patterns actually drive traffic, and identifies gaps you can exploit. Outputs a competitive pSEO landscape report.
npx gooseworks install --all # then, in Claude Code, Cursor, or Codex: /gooseworks use the programmatic-seo-spy skill
Before building your own programmatic SEO pages, know what your competitors are already doing. This skill crawls competitor sitemaps, detects which URL patterns are programmatic, estimates which ones actually drive traffic, assesses template quality, and finds the gaps they're missing.
Core principle: The best pSEO strategy starts with competitive intelligence. If 3 competitors have /vs/ pages and none have /for-{industry}/ pages, that's a signal. This skill turns competitor site structures into strategic intelligence.
This skill works with existing capabilities but is dramatically better with domain analytics data that shows actual traffic per URL pattern.
"I can analyze competitor site structures with our existing crawling tools. For the most accurate results — especially knowing which patterns actually drive traffic vs. just existing — I'd recommend connecting a domain analytics API."
Recommended: DataForSEO (pay-per-use, ~$0.02-0.05 per domain analysis)
- Sign up at dataforseo.com → get API login + password
- Set
DATAFORSEO_LOGINandDATAFORSEO_PASSWORDenv varsAlternatives that also work:
- SEMrush API (if you have a subscription) → set
SEMRUSH_API_KEY- Ahrefs API (if you have a subscription) → set
AHREFS_API_TOKEN- SimilarWeb API (if you have access) → set
SIMILARWEB_API_KEY"Want to use one of these, or should I proceed with baseline mode? Baseline will detect patterns and assess template quality, but traffic estimates will be inferred rather than measured."
site-content-catalog for URL crawling, seo-domain-analyzer for domain-level metrics, web_search for spot-checking rankings. Pattern detection is equally good. Traffic attribution is directional, based on page count × domain authority × keyword indicators.For each competitor, run site-content-catalog:
python3 skills/site-content-catalog/scripts/catalog_content.py \
--url "<competitor_url>" \
--output jsonCollect:
Group URLs by structural patterns using regex matching:
| Pattern Regex | Example URLs | Category |
|---|---|---|
/vs/.* or /compare/.* | /vs/competitor-a, /vs/competitor-b | Comparison |
/integrations?/.* | /integrations/slack, /integrations/hubspot | Integration |
/for-.* or /solutions/.* | /for-startups, /for-enterprise | Vertical |
/use-cases?/.* | /use-cases/project-management | Use case |
/alternatives?(-to)?/.* | /alternatives/competitor-a | Alternatives |
/templates?/.* or /examples?/.* | /templates/invoice, /templates/proposal | Templates |
/glossary/.* or /what-is/.* | /glossary/term-a, /what-is/crm | Glossary |
/tools?/.* or `/calculat(or | e)/.*` | /tools/roi-calculator |
/blog/.* (exclude from pSEO) | /blog/how-to-x | Editorial (not pSEO) |
For each detected pattern:
Not every URL pattern is programmatic. Classify each cluster:
Focus analysis on Programmatic and Semi-programmatic clusters only.
For each competitor domain, pull organic keywords data:
# DataForSEO example
POST /v3/dataforseo_labs/google/ranked_keywords/live
{
"target": "competitor.com",
"filters": [
["ranked_serp_element.serp_item.url", "contains", "/vs/"]
]
}Per pattern cluster:
Estimate traffic indicators:
seo-domain-analyzer for overall domain metricsweb_search — do they appear in top 10?For each programmatic pattern, fetch 3-5 sample pages via fetch_webpage:
# Pick pages from the pattern — one high-variation, one low-variation, one middleEvaluate template quality on:
| Dimension | Score 1-5 | What to Look For |
|---|---|---|
| Content depth | Word count, sections, detail level | |
| Unique value per page | Does each page offer something you can't get from the template alone? | |
| Data richness | Tables, comparisons, stats, screenshots | |
| Freshness signals | Updated dates, current pricing, recent reviews | |
| Internal linking | Links to related pages, hub structure | |
| CTA integration | Contextual CTAs vs. generic banners | |
| Schema markup | Structured data, FAQ schema, review schema |
Quality tiers:
| Pattern Type | Competitor A | Competitor B | Competitor C | You | Gap? |
|---|---|---|---|---|---|
| vs/ comparisons | 25 pages ★★★★ | 10 pages ★★★ | 0 | 0 | ✓ A leads |
| integrations/ | 40 pages ★★★ | 60 pages ★★★★ | 20 pages ★★ | 0 | ✓ B leads |
| for-{industry}/ | 0 | 0 | 8 pages ★★ | 0 | ✓ Wide open |
| alternatives/ | 5 pages ★★★ | 0 | 0 | 0 | ✓ Lightly competed |
| glossary/ | 100 pages ★★ | 0 | 50 pages ★★★ | 0 | ⚠️ Volume play |
Within each pattern competitors use, find missing variations:
Entire pattern types no competitor has built:
Patterns where competitors have pages but they're low quality:
# Programmatic SEO Competitive Landscape — [Category] — [DATE]
## Competitors Analyzed
- [Competitor A] — [domain metrics, total pages crawled]
- [Competitor B] — [domain metrics, total pages crawled]
- [Competitor C] — [domain metrics, total pages crawled]
## Executive Summary
- [N] programmatic patterns detected across [M] competitors
- Strongest competitor pSEO: [Competitor X] with [pattern] ([N] pages, [quality])
- Biggest opportunity: [pattern type] — [reasoning]
- Quick wins: [N] variation gaps in existing competitor patterns
---
## Pattern-by-Pattern Analysis
### Pattern: [vs/ Comparisons]
**Who's doing it:** [Competitor A (25 pages, ★★★★), Competitor B (10 pages, ★★★)]
**Traffic estimate:** [X monthly visits across pattern] (enhanced) / [directional estimate] (baseline)
**Template quality:** [summary of strengths/weaknesses]
**Top-performing pages:** [specific pages that rank well]
**Variation gaps:** [competitors/variations they're missing]
**Your opportunity:** [specific recommendation]
### Pattern: [integrations/]
...
---
## Opportunity Ranking
| Priority | Pattern | Opportunity Type | Effort | Expected Impact |
|----------|---------|-----------------|--------|----------------|
| P0 | [pattern] | White space — no competitors | Medium | High |
| P0 | [pattern] | Quality gap — beat weak templates | Low | Medium |
| P1 | [pattern] | Variation gap — fill missing pages | Low | Medium |
| P2 | [pattern] | Head-to-head — outrank strong competitor | High | High |
---
## Recommended Action Plan
1. **Immediate (Week 1-2):** Build [pattern] — [rationale]
2. **Short-term (Month 1):** Build [pattern] — [rationale]
3. **Medium-term (Month 2-3):** Build [pattern] — [rationale]
---
## Raw Data
[Link to crawl data, pattern clusters, sample pages analyzed]Save to the current working directory or wherever the user prefers.
| Component | Cost |
|---|---|
| Site catalog per competitor (Apify) | ~$0.05-0.10 |
| SEO domain analyzer per competitor | ~$0.10-0.20 |
| Page fetches (3-5 per pattern × N patterns) | ~$0.01-0.05 |
| DataForSEO domain analytics (enhanced) | ~$0.10-0.50 per competitor |
| Analysis | Free (LLM reasoning) |
| Total (baseline, 3 competitors) | ~$0.50-1.00 |
| Total (enhanced, 3 competitors) | ~$0.80-2.00 |
APIFY_API_TOKEN env varsite-content-catalog, seo-domain-analyzer, fetch_webpageDATAFORSEO_LOGIN + DATAFORSEO_PASSWORD), SEMrush (SEMRUSH_API_KEY), Ahrefs (AHREFS_API_TOKEN), or SimilarWeb (SIMILARWEB_API_KEY)Assemble an expert/educator motion-graphic LISTICLE video ad from a config — a spoken authoritative voiceover carries a numbered listicle while N web-animated hyperframe beats (HTML plus the Web Animations API, one branded design system of alternating tiles, big hero numerals, and glass-pill callouts) are rendered frame-by-frame via Playwright and anchored to the VO's word-level timestamps, periodic color-graded B-roll windows give visual breath, and captions burn ONLY inside those B-roll windows (2-word chunks, ASS Format header carrying a Name field so none drop) with the VO mixed under a low music bed. This is the FREE deterministic assembly stage (Playwright beat render plus ffmpeg concat plus window-masked caption burn plus VO-and-music mix plus final composite) — the VO, the music bed, and the stock B-roll come from create-vo-elevenlabs, create-music-elevenlabs, and media-proxy. Use for the vo-anchored-motion-listicle format.
Assemble a stop-motion hand-swatch-cycle product-demo ad from a config — a sequence of still PLATES (one hand swiping a single-barrel cosmetic across a cream skin-patch, the barrel + swatch changing per plate while the hand, background, crop, and lighting stay locked) is PNG→mp4 loop-encoded at each plate's own stop-motion hold (fast motion frames 150–250ms, per-shade ~380ms, hero beats 1100–1800ms), concat-demuxed with HARD cuts into a silent master, closed on a Playwright HTML-rendered branded end card (serif tagline + sans subtitle + real logo SVG over a hero BG, never AI-rendered text), and muxed with a pre-sourced music track playing under the end card with a fade tail (no VO). This is the FREE deterministic assembly stage (loop-encode + concat-demux + end-card render + music mux); the master-anchor plate, shade plates, and end-card BG come from create-image-gpt-image-fal and the track from create-music-elevenlabs. Use for the stopmotion-hand-swatch-cycle format.
Assemble a split-screen creator ad from a config — a two-zone vertical composite where a supplied AI-creator lip-sync take fills the BOTTOM ~48% while real 16:9 product/demo clips run uncropped in the TOP ~52%, each top clip contain-fit with a darkened blurred cover-scale fill of the same clip (never black bars), a 3px brand-color divider between the zones, the creator slice cover-fit per the per-scene VO timing, scenes hard-concatenated with the body audio being the concatenated creator VO slices, an end card held on the last sharp frame ~3s, then the ASSEMBLED cut transcribed with local Whisper (not the raw VO — concat drops inter-scene silence) and word-level captions burned in the chosen style. This is the FREE deterministic assembly + caption stage (two-zone composite + blurred fill + divider + hard-concat + end card + captions); the VO comes from create-vo-elevenlabs, the anchor from create-image-gpt-image-fal, and the whole-VO lip-sync from a paid VEED Fabric 1.0 take (a no-atom upstream input). Use for the split-screen-creator format.