| name | visibly-seo-status-quo |
| description | Map a client domain's current organic visibility. Use when the user asks to analyse where a site ranks, audit its Search Console performance, cross-reference target keywords against live GSC data, find quick wins, or benchmark against competitors. Pulls real GSC/keyword data via the Visibly AI MCP rather than guessing. |
SEO Status-Quo Analysis
Establish, with live data only, where a domain stands organically in a target
market today. This is the foundation every later phase builds on — so every fact here
must be verified, not assumed.
Tier note. Steps 1–2 use Visibly AI's Google tools (query_search_console,
list_projects), which need a Visibly key (pro tier — GSC/GA run at 0 credits
once Google is connected). No key? Skip to Step 3: export Search Console
(Performance → Queries → CSV) yourself and feed that CSV to the Python template in
Step 4 — the cross-reference, classification and quick-win logic all run locally and
keyless. Setup tiers: docs/setup.md §3.
Step 1 — Discover what's wired
mcp__visiblyai__list_projects — find the project matching the domain.mcp__visiblyai__get_google_connections — confirm Search Console (and GA) are connected.
If no project/connection exists, stop and tell the user what to connect first.
Step 2 — Pull live GSC performance
mcp__visiblyai__query_search_console — dimension=query, target-country filter,
limit=500. This is the ground truth: clicks, impressions, CTR, average position.- Repeat with
dimension=page to find URLs with impressions but weak clicks
(underperforming pages = on-page/intent-mismatch candidates).
Step 3 — Load the client's target keywords
- Read the client's keyword file (
*.xlsx / *.csv) with pandas. These are the
keywords the business cares about — often different from what it actually ranks for.
That gap is where the strategy lives.
Step 4 — Cross-reference and classify
- Map each target keyword onto the live GSC row (clicks, impressions, CTR, position).
- Classify on two axes:
- Type: Brand · Generic · Competitor
- Ranking bucket: Top 3 · Page 1 (4-10) · Page 2 (11-20) · Weak (21-50) · Not Ranking (50+/none)
Don't do this join by hand. Save the GSC export (dimension=query) as CSV/XLSX next to
the client's keyword file and run the Python template — it does the cross-reference,
both classifications and the quick-win flag deterministically:
.\claude_tools_venv\Scripts\python.exe -m claude_tools.status_quo `
--keywords "clients/<domain>/_knowledge/keywords.xlsx" `
--gsc "clients/<domain>/<date>_Status-Quo/gsc_query.csv" `
--brand-terms <brand> --competitor-terms <comp1>,<comp2> `
--out "clients/<domain>/<date>_Status-Quo/status_quo_<date>.xlsx"
It writes a 3-sheet xlsx (Status-Quo · Quick wins · Summary) with the exact columns the
output template expects. Columns are auto-detected (DE/EN headers); first-time setup:
.\claude_tools\setup.ps1. See claude_tools/README.md.
Step 5 — Flag quick wins
- A quick win = high impressions + CTR below ~5 % + position in the 4-15 "striking
distance" band. These convert fastest: the demand and partial visibility already
exist; you're closing a gap, not creating one. List them explicitly with the lever
(title/meta, on-page, internal links).
Step 6 — Competitor benchmark
mcp__visiblyai__get_competitors and mcp__visiblyai__get_keywords to compare
positions on shared keywords. Show where the client is out-ranked and by whom.
Output
Fill in the ready skeleton at templates/status-quo-template.md —
it already has the visibility counts, classification split, cross-referenced keyword table,
underperforming pages, quick wins and competitor benchmark. Save under
clients/<domain>/YYYY-MM-DD_Status-Quo/:
status_quo_<date>.md — summary (copied from the template)status_quo_<date>.xlsx — full cross-referenced table (all rows)quick_wins.md — prioritised, actionable (specific lever per keyword)
Only live-verified facts go into any deliverable. Slash command: /visibly-seo-status-quo <domain>.
Methodology: docs/workflows.md.
