/seo-subdomain

name: seo-subdomain
description: Subdomain ownership map for a domain. Lists subdomains, queries overview/keywords/competitors/backlinks per subdomain, surfaces which subdomains own which topic clusters, where there's fragmentation, and whether consolidation is warranted. Use when the user asks "subdomain analysis", "subdomain ownership", "subdomain SEO", "blog vs main domain", "support vs docs subdomain", or "should I consolidate subdomains".

> Example output: [examples/seo-subdomain-notion-so-20260514/SUBDOMAINS.md](../../examples/seo-subdomain-notion-so-20260514/SUBDOMAINS.md)

Subdomain Analysis

Map a domain's subdomain ecosystem. Which subdomains exist, what each ranks for, where they overlap, and whether the structure is healthy or fragmented. Output: an ownership map (which topic is owned by which subdomain), a fragmentation report, and recommendations for consolidate / split / leave alone.

Prerequisites

• SE Ranking MCP server connected.
• User provides: a target root domain (e.g. `example.com`). The skill discovers subdomains automatically.
• Optional: `--limit N` to cap the number of subdomains analysed (default: top 10 by ranked keyword count).

Process

1. **Validate & preflight**

• Normalise root domain (no protocol, no `www.`).
• `DATA_getCreditBalance` — surface remaining credits. Subdomain analysis is N × ~5 calls; cost scales with subdomain count.

2. **Discover subdomains** `DATA_getDomainSubdomains`

• List all subdomains of the root domain.
• For each: keyword count, traffic estimate, backlinks count.
• Sort by ranked-keyword count descending.
• Apply `--limit` (default top 10).

3. **Per-subdomain overview** `DATA_getDomainOverviewWorldwide`

• For each subdomain in scope: domain authority, traffic estimate, organic + paid keyword counts, top regions.
• This establishes a baseline for cross-subdomain comparison.

4. **Per-subdomain top keywords** `DATA_getDomainKeywords`

• For each subdomain: top 100 organic keywords with positions, intent, traffic.
• Cluster keywords by topic. This skill's grouping is a lightweight per-subdomain ownership map, not a content plan — token-grouping by head term + intent is sufficient here. (For full content-cluster planning use `seo-keyword-cluster`, which now clusters by SERP overlap, not text similarity.)
• Each subdomain gets a list of "owned topics" (clusters where it dominates) and "minor topics".

5. **Per-subdomain competitors** `DATA_getDomainCompetitors`

• For each subdomain: top organic competitors by `common_keywords`.
• Surface: do different subdomains have different competitor sets? (Sign of legitimately separate scopes.) Do they share competitors? (Sign of redundant scopes.)

6. **Per-subdomain backlinks** `DATA_getBacklinksSummary` and `DATA_getBacklinksRefDomains` (top 20)

• Subdomains often have separate backlink profiles. Capture each.
• Surface: do subdomains have meaningfully different referring-domain populations?

7. **Authority distribution** `DATA_getDistributionOfDomainAuthority`

• For each subdomain, pull DA distribution of referring domains.

8. **Detect fragmentation**

• For each topic-cluster, identify all subdomains ranking for keywords in that cluster.
• **Fragmentation signal:** ≥ 2 subdomains rank in the top 50 for the same high-volume keyword (cannibalization).
• **Healthy split:** distinct topic clusters per subdomain, minimal overlap.

9. **Make recommendations**

• **Consolidate:** if `blog.example.com` and `example.com/blog/` both rank for the same topic cluster but neither dominates — pick one canonical home.
• **Split:** if a subdomain is ranking for a topic completely off-mission for the root domain, that's a split worth keeping.
• **Leave alone:** if subdomains have distinct topic ownership and don't cannibalize, don't disturb.
• **Investigate:** ambiguous cases flagged for human review.

10. **Synthesise** `SUBDOMAINS.md`

Output format

Create a folder `seo-subdomain-{target-slug}-{YYYYMMDD}/` with:

seo-subdomain-{target-slug}-{YYYYMMDD}/
├── SUBDOMAINS.md                       (synthesised report + recommendations — primary deliverable)
├── 06-topic-ownership-map.md           (cluster × subdomain matrix — load-bearing reference content teams brief from)
├── 07-fragmentation-flags.md           (cannibalization detected — load-bearing reference for consolidation decisions)
└── evidence/
    ├── 01-subdomains-list.md           (DATA_getDomainSubdomains — raw step output)
    ├── 02-overview-by-subdomain.md     (per-subdomain overview rows)
    ├── 03-keywords-by-subdomain/
    │   ├── blog-example-com.md
    │   ├── docs-example-com.md
    │   └── ...                          (one per subdomain)
    ├── 04-competitors-by-subdomain.md
    └── 05-backlinks-by-subdomain.md

Top-level: `SUBDOMAINS.md` + `06-topic-ownership-map.md` + `07-fragmentation-flags.md`. Content teams brief from the ownership map; consolidation decisions cite the fragmentation flags directly. The 01–05 step files preserve raw API outputs in `evidence/`.

`SUBDOMAINS.md` follows this shape:

# Subdomain Analysis: {root domain}

> Snapshot dated {YYYY-MM-DD} · Subdomains analysed: {n} of {n discovered} (limit: top {limit} by keyword count)

## Subdomain inventory

| Subdomain | Keywords | Traffic est. | Backlinks | Domain authority | Top topics owned |
|---|---|---|---|---|---|
| {root} | {n} | {n}/mo | {n} | {DA} | {topics} |
| blog.{root} | {n} | {n}/mo | {n} | {DA} | {topics} |
| docs.{root} | {n} | {n}/mo | {n} | {DA} | {topics} |
| ... |

## Topic ownership map

| Topic cluster | Owned by | Also ranks (cannibalization?) |
|---|---|---|
| {topic 1} | blog.{root} (avg pos 3.2 across 47 keywords) | {root} (avg pos 12) — {⚠ cannibalization} |
| {topic 2} | docs.{root} | (none) |
| ... |

## Fragmentation flags

### {⚠ Cannibalization detected: topic = {topic X}}
- `blog.{root}` ranks {n} keywords for {topic X}, avg pos