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Search Console SEO Analyst
Agency-grade SEO analysis from Google Search Console: brand vs non-brand trends, drop diagnosis, quick wins and client-ready reports.
$9.99
Search Console SEO Analyst
Example session with this skill installed
Give me a brand / non-brand table for sc-domain:example-hotel.com for the last 6 full months with year-over-year, then tell me why organic clicks dropped since June. Brand terms: "example hotel", "hotel example", "examplehotel". Ignore the booking.example-hotel.com sub-domain.
- Read your context and instructions
- Compiled the search console seo
Summary
Organic clicks on sc-domain:example-hotel.com fell 38% year over year in July-September. The decline is almost entirely
non-brand (-42%), while brand demand is flat (-17% on a small base, one-off spikes excluded). The break is dated mid-June and coincides with a Google core update; the pages that lost are English informational articles ranking for queries the site does not actually answer.
Data: gsc_query on sc-domain:example-hotel.com, range 2025-04-01..2026-09-30, dimensions=date, data_state=final, 2 calls (total and brand filter), last 3 days dropped. booking.example-hotel.com is a separate property and was not included.
Monthly table (6 full months)
| Month | Clicks | Brand | Non-brand | Impressions | Avg. position | Clicks prev. year | YoY |
|---|---|---|---|---|---|---|---|
| Apr 2026 | 1,550 | 288 | 1,262 | 212,400 | 14.8 | n/a | n/a |
| May 2026 | 1,465 | 266 | 1,199 | 204,900 | 15.1 | n/a | n/a |
| Jun 2026 | 939 | 183 | 756 | 141,200 | 18.6 | n/a | n/a |
| Jul 2026 | 826 | 149 | 677 | 118,700 | 19.9 | 1,331 | -38% |
| Aug 2026 | 710 | 112 | 598 | 101,300 | 20.4 | 1,146 | -38% |
| Sep 2026 | 977 | 220 | 757 | 109,800 | 19.1 | 1,578 | -38% |
Brand is the sum of queries matching (?i)example hotel|hotel example|examplehotel. Non-brand is total minus brand, so anonymised queries are counted. YoY is shown only where the previous year is inside the 16-month retention window (first available day: 2025-06-13), which is why April-June 2026 have no comparison. Two spikes on 18 and 19 January 2026 (press coverage) were excluded from the baseline and are reported separately.
Why it dropped
Timing: the daily series breaks between 12 and 16 June 2026, not gradually. That matches a core update, not seasonality (the previous year shows a summer plateau, not a cliff).
2.
Where: 71% of the lost non-brand clicks sit on 14 English blog articles (/en/blog/...). Their average position moved from 6.2 to 15.4; impressions fell less than clicks, so the pages are still indexed and still shown, just lower.
3.
What kind of queries: the queries that left are generic travel-information searches ("things to do in <region>", "best time to visit"). The site answers them with short promotional pages. This is an intent mismatch, and Google re-ranked accordingly.
4.
What is not the cause: no pages removed (gsc_indexing_issues: 0 new errors), no redirect consolidation, no sub-domain anomaly, brand demand stable (Sep 2026 brand 220 vs 223 the year before).
Recoverable or not
- Brand: nothing to recover, it never dropped.
- Non-brand informational: partly recoverable by rewriting the 14 articles to actually answer the query (length, structure, local detail) or by consolidating them into 4-5 stronger guides. Expected upside: back to 60-70% of the previous level, not 100%, because AI Overviews now take part of those clicks (CTR at position 5-8 is 40% lower than a year ago on the same queries).
- Non-brand commercial ("hotel <town>", "spa hotel <region>"): stable, protect it.
Next steps
- Rewrite or merge the 14 articles (list attached in the working file).
- Re-check in
gsc_compare_periodsafter the next core update. - Monthly tracking with the same table, same brand terms, same exclusions, so the numbers stay comparable.
(Demo property and figures are illustrative.)
Connects securely to your tools. The creator never sees your data.
About this skill
Turns Google Search Console into the analyses SEO specialists actually deliver, using the free, read-only mcp-google-search-console MCP server (17 tools).
What it does
- Brand vs non-brand trend tables, month by month, with year-over-year and quarter-over-quarter comparisons computed the right way: anonymised queries handled (non-brand = total minus brand, never a regex exclusion), one-off spikes excluded, partial days dropped, position weighted by impressions. A bundled script builds the table from two Search Console exports.
- Traffic drop diagnosis with eleven documented signatures: core update, lost pages, redirect consolidation, CTR erosion from AI Overviews, seasonality, inflated baseline, brand disambiguation, intent mismatch, thin pages absorbed as brand variants, technical blocks, hijacked sub-domains. Each one comes with the check that confirms it and what is actually recoverable.
- Opportunity plan: quick wins, CTR gaps, cannibalisation and content decay, ranked by estimated click gain.
- Indexing audit: URL Inspection grouped by coverage state, sitemap health.
- Cross-checks: why a rank tracker says position 1 and Search Console says 14 (AI Overview citations), how to verify a client's keyword list, why GA4 and GSC diverge (bot traffic).
- Client reports: the structure of a monthly or quarterly SEO report, honest framing, impersonal voice, and the white-label HTML audit in English or Italian.
Ground rules the skill enforces
Every answer quotes the meta line of the data it used, never sums rows into a total, declares every exclusion, respects the 16-month retention and the 2-3 day data lag, and never mixes two properties of the same site. Search Console says what happened, not why: the skill keeps the two apart.
Where it comes from
Built from real agency engagements (retail, hospitality, B2B manufacturing, utilities, construction). Works in Claude Code, Cursor, Codex CLI and any agent that reads SKILL.md and connects to MCP servers.
Requirements
The mcp-google-search-console MCP server (https://github.com/acamolese/google-search-console-mcp, MIT) connected to the agent, and Search Console access to the property. No credentials are stored in the skill. The only script is a pure Python standard-library file that reads local TSV exports: no network, no environment variables.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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