SEOmatic Traffic Drop Diagnosis uses real Google Search Console data to explain organic traffic losses and prioritize evidence-based fixes.
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SEOmatic Traffic Drop Diagnosis is an Openclaw Skills workflow for investigating declines in organic clicks, traffic, or rankings on a connected site. It compares performance with seasonal patterns, pinpoints the pages and queries responsible for losses, checks indexing and competitor signals, and distinguishes SEO problems from measurement issues.
The skill produces a data-backed verdict before recommending changes. Work can be staged as SEOmatic tasks when the connected plan supports it, but nothing goes live without the user's approval, and every change can be rolled back.
gsc_insights seasonality across 16 months year over year, then compare the latest 28 days with the preceding 28 days using gsc_performance. Use the trend data to identify when the decline began. Treat very recent figures as provisional because Search Console data can lag by 2–3 days.https://app.seomatic.ai/api/mcp using the streamable HTTP transport and OAuth authentication. Alternatively, configure a Bearer API key beginning with smk_ from the SEOmatic AI Agents settings page: https://app.seomatic.ai/dashboard/settings?tab=ai-agents.https://app.seomatic.ai/connect/gsc. Search Console connection is required before diagnosis; if it is missing, stop and ask the user to connect it.seomatic MCP server available to the agent. This skill declares no environment-variable or binary requirements.agents:act scope; on a free key, describe the recommended fix instead of attempting to stage it.No CLI installation command is specified by the skill; setup is performed through the SEOmatic MCP connection and account links above.
The skill reads data through SEOmatic MCP tools scoped to the connected site; it does not specify local generated files or a separate on-disk data format.
| Data area | Tool and actions | Diagnostic purpose |
|---|---|---|
| Seasonality and insights | gsc_insights: seasonality, sitemaps |
Compare 16 months year over year and identify sitemap errors. |
| Search performance | gsc_performance: compare_periods, trend, folder_rollup, fresh |
Compare date ranges, locate the start of a decline, group losses by site folder, and check recent provisional data. |
| Indexing | gsc_indexing: batch_inspect |
Inspect losing URLs for indexing status, canonicalization, noindex directives, and fetch errors. |
| Search results and competitors | serp_competitors: features |
Check SERP features for queries where clicks fell despite steady position. |
| Page content | site_pages: crawl |
Review titles, H1s, and body content on affected pages. |
| Analytics | traffic_analytics: sources |
Compare GA4 traffic sources with Search Console performance when GA4 is connected. |
| Remediation tasks | task_manage: list, create, get, decide |
Check for duplicates, stage fixes, inspect task results, and record a decision only after the user explicitly approves a specific task. |
Staged tasks use targetKey for a full page URL or a content topic, a user-oriented title, and a rationale containing supporting numbers. Task types include content_refresh, ctr_fix (requires queries), index_request, and technical_fix. Each task provides an estimatedCost.label to quote to the user.
seomatic MCP server using OAuth or a Bearer API key; the tools operate on the connected site, so the agent should not ask the user for a domain.Loading
Identify pages losing search traffic, pinpoint what they need, and stage evidence-backed refreshes for user approval.

Check which AI crawlers can reach your website, what they fetch, and where the path from crawl to citation breaks.

SEOmatic Keyword Research turns a site's real search performance into focused keyword clusters mapped to the pages best suited to rank.

SEOmatic Content Brief turns a target keyword into a research-backed, approval-ready content plan using live SERP and Google Search Console data.

Find pages competing for the same Google query, identify which should rank, and stage a data-backed fix for approval.

Folkmoot connects AI agents in a public forum to discuss proposals, collaborate on tasks, and make decisions through sealed ranked ballots.








































