SEO
AuraScore 81/100

Academic Catalog Information Architecture and SEO Migration Checklist

Audit academic program catalogs and course taxonomies during university CMS redesigns.

Use this checklist when restructuring academic degree pages, faculty directories, and course catalogs during a university website overhaul. It ensures zero link equity loss and prevents indexation bloat from faceted course filters.

Template

Role: Senior Higher Education SEO Architect specialized in university digital governance, CMS migrations, and degree taxonomy optimization.

Context

  • Academic Institution: {{institution_name}}
  • Course Catalog Platform: {{catalog_cms_platform}}
  • Legacy URL Taxonomy: {{legacy_url_structure}}
  • Target Academic Levels: {{target_degree_levels}}
  • Accrediting Agencies & Councils: {{accreditation_bodies}}
  • High-Priority Recruitment Terms: {{priority_enrollment_terms}}

Task

Develop an enterprise-grade technical SEO migration checklist tailored for {{institution_name}} to safely transition its course catalog on {{catalog_cms_platform}}, protecting organic rankings for {{priority_enrollment_terms}} and eliminating crawler traps across {{target_degree_levels}}.

Method

  1. Analyze {{legacy_url_structure}} against the new {{catalog_cms_platform}} architecture to construct a 1:1 redirect map for all degree and certificate pathways.
  2. Define parameter handling and canonicalization protocols for faceted course search filters (e.g., department, term, instructor, modality).
  3. Audit structured data markup requirements using Schema.org EducationalOccupationalProgram and Course schemas cross-referenced with {{accreditation_bodies}}.
  4. Design crawl-budget protection protocols to eliminate thin pagination and internal duplicate content across {{target_degree_levels}}.
  5. Map internal linking pathways from flagship university pages to newly mapped department hubs and {{priority_enrollment_terms}} landing pages.
  6. Specify pre-launch staging environment crawl benchmarks, including HTTP status, header responses, and canonical tag parity.
  7. Formulate post-launch indexation validation steps, Google Search Console change-of-address procedures, and 404 monitoring cadences.

Constraints

  • MUST establish explicit canonical URL and noindex rules for dynamic search and filter facets in {{catalog_cms_platform}}.
  • MUST validate that every program page retains accreditation disclosures required by {{accreditation_bodies}} without duplicating body copy.
  • MUST NOT recommend wildcard 301 redirects to generic homepage or top-level department URLs.
  • All redirect rules must specify deterministic matching criteria for legacy parameters.

Output format

  • Phase 1: Pre-Migration Discovery & Crawl Architecture (8-10 checklist items with acceptance criteria)
  • Phase 2: On-Page Taxonomy & Schema Validation (6-8 checklist items covering Schema.org specifications)
  • Phase 3: Launch Day DNS & Server Response Validation (5-7 checklist items)
  • Phase 4: Post-Launch Rank & Index Monitoring Protocol (5-7 checklist items)

Self-review

  • Confirm all variables ({{institution_name}}, {{catalog_cms_platform}}, {{legacy_url_structure}}, {{target_degree_levels}}, {{accreditation_bodies}}, {{priority_enrollment_terms}}) are actively integrated.
  • Verify no checklist items rely on vague instructions like "check SEO" without concrete pass/fail metrics.
  • Ensure each section explicitly distinguishes program-level landing pages from transient course-schedule pages.
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

marketing
marketing-seo
education-research
higher-education
technical-seo
site-migration