Build SEO pages at scale with data
programmatic-seoskillsetup L3★368
marian2js/opengoat ↗Causal-lift measurements
content-strategy-11pp vs no-skill baselinewith-skill 43% · baseline 54%
Measured by running the task with and without this artifact, K=5, graded by deterministic checks — no LLM judging.
What it does
Build and scale SEO-optimized pages using templates and proprietary data
Best for
When you have proprietary or product-derived data and a clear keyword pattern with 100+ potential pages; builds durable SEO moat through data defensibility, not just template replication.
Inputs
- · Search pattern data (keyword research, volume, trends)
- · Proprietary or product-derived data to populate pages
- · Baseline competitive analysis (who ranks, what they cover)
Outputs
- · Programmatic SEO page templates and data schema
- · URL structure and internal linking plan
- · Content playbook (templates, examples, profiles, comparisons, or glossary)
Requires
- · Database or data source (product usage data, proprietary research, user-generated content, or public APIs)
Preconditions
- · Clear keyword pattern identified (e.g., "[type] template", "[X] vs [Y]")
- · Proprietary data available (otherwise thin-content risk)
- · SEO infrastructure in place (clean URL structure, site speed)
Failure modes
- · Creates thousands of thin pages with swapped variables — Google penalizes
- · Data quality poor or not defensibly unique — pages don't rank
- · URL structure uses subdomains instead of subfolders — domain authority splits
- · Overreliance on public data (lowest defensibility tier)
Trust signals
- · Data defensibility hierarchy explicitly named: proprietary > product-derived > user-generated > licensed > public
- · 12 playbooks with examples: templates, curation, conversions, comparisons, examples, locations, personas, integrations, glossary, translations, directory, profiles
- · Clean subfolder URL structure principle (not subdomains)
- · Three-phase implementation: keyword pattern research, data requirements, template design