SEO Traffic Diagnosis Skill for Claude

SEO Traffic Diagnosis

Diagnose why organic traffic moved (down, flat, or unexpectedly up) using Ahrefs MCP combined with Search Console and analytics data. Stack-agnostic. Produces a root-cause diagnosis and an action plan.

When to use

• Organic traffic dropped sharply
• Organic traffic has been flat for months despite content investment
• After a known Google algorithm update
• After a migration, replatform, or domain change
• After a deploy that touched routing, redirects, or rendering
• When a competitor is visibly taking organic share
• When a single page dropped from a ranked position
• When stakeholders need an explanation, fast

When NOT to use

• Routine performance reporting (use analytics-strategy)
• Pre-emptive content planning (use seo-content-gap-audit)
• Backlink-only investigations (use seo-backlink-audit)
• Technical issue triage outside of traffic concerns (use seo-site-health-audit)

Required inputs

• Description of the symptom (what changed, when, magnitude)
• Date the change started (best estimate)
• Recent SEO history: deploys, migrations, content changes, link campaigns
• Access to Ahrefs MCP, Search Console, and analytics
• Confirmation the change is real (not a tracking artifact)

The framework: 5 layers of diagnosis

A traffic change has one or more root causes. Move through the layers in order. Stop when you have enough evidence.

Layer 1: Confirm the change is real

Before diagnosing, rule out:

• Tracking gaps (analytics outages, tag manager issues)
• Bot traffic changes
• Reporting comparison errors (different date ranges, wrong segment)
• Seasonality (compare year-over-year, not just month-over-month)
• Holiday or weekday effects

Cross-check Search Console clicks against analytics organic sessions. Significant divergence often points to a tracking issue, not a real traffic change.

Layer 2: Localize the change

Where is the change happening?

Segment by:

• Country and language
• Device (mobile, desktop, tablet)
• Page or section (homepage, blog, product, category)
• Query type (branded vs non-branded)
• Landing page

A change in one segment requires different diagnosis than a change everywhere.

| Pattern | Likely cause |

| --- | --- |

| One country dropped | Local algorithm update, hreflang issue, geo redirect issue |

| Mobile dropped, desktop flat | Mobile usability or page speed regression |

| One section dropped | Topical algorithm update or section-specific quality issue |

| Branded queries dropped | Brand-level issue: site outage, reputation, manual action |

| Non-branded dropped | Algorithmic ranking issue |

| Single page dropped | Page-level issue: content, technical, or competitive |

| Sitewide dropped | Sitewide issue: penalty, technical, migration, or algorithm |

Layer 3: Page-level analysis

For affected pages, audit:

• Position changes per ranked keyword (Ahrefs Rank Tracker history)
• SERP composition changes (more ads, AI overviews, featured snippets, video)
• Click-through rate changes
• Index status (Search Console coverage)
• Crawl errors and accessibility
• Recent content changes
• Internal link changes
• Backlink changes (lost links, redirect chains)

A page can lose traffic without losing rank if SERP composition changed.

Layer 4: Technical analysis

Did anything break technically?

Check:

• Robots.txt changes
• Canonical tag changes
• Meta robots changes (accidental noindex)
• Redirect chains and loops
• Render issues (especially for JS-heavy frameworks)
• Site speed regressions
• Hreflang errors
• Sitemap freshness
• HTTP status codes (4xx, 5xx spikes)
• Server log evidence of crawl behavior changes

Recent deploys are the prime suspect. Compare deploy dates to traffic change dates.

Layer 5: External analysis

If layers 1-4 do not explain the change, look outward.

• Algorithm update calendar (cross-reference timing)
• Competitor moves (new content, new SERP features they captured)
• Industry trend (declining search demand for the topic)
• Manual action (Search Console security and manual actions)
• Negative SEO (sudden link velocity changes)

External-factor diagnosis benefits from competitive context: did your traffic drop while competitors held steady (suggests an algorithm-specific issue), or did the entire vertical lose ground (suggests a user-behavior shift)? Similarweb shows competitor traffic trends; Ahrefs shows competitor SERP movement; pairing both surfaces whether the issue is yours alone or the category's.

Workflow

• Confirm the symptom. Get exact dates, magnitude, segment if known.
• Validate the data. Layer 1 checks. Rule out tracking and seasonality.
• Localize. Layer 2. Segment until the pattern is clear.
• Page-level dive. Layer 3 on the most affected pages.
• Technical check. Layer 4. Recent deploys, robots, canonicals, redirects.
• External check. Layer 5. Algorithm updates, competitors, industry.
• Build the hypothesis. State the cause as a single sentence.
• Validate the hypothesis. Find the evidence that confirms or refutes it. See references/diagnosis-checklist.md.
• Action plan. Specific fixes mapped to specific evidence.

10. Communicate. Write up the diagnosis. Stakeholders want clarity, not exhaustive analysis.

Failure patterns

• Jumping to algorithm update. "It must be the algorithm" is the lazy answer. Eliminate technical and page-level causes first.
• Solving the wrong problem. A drop diagnosed as "content quality" when the real cause was an accidental noindex on a deploy. Validate the hypothesis before fixing.
• No baseline for "normal." Without a baseline, every fluctuation looks alarming. Establish what normal noise looks like before reacting.
• Treating one page as the site. Site-wide and page-level diagnoses are different. Confirm scope first.
• Ignoring branded vs non-branded. A drop in branded queries means a brand-level problem. A drop in non-branded means an SEO problem. Different teams own them.
• Comparing wrong date ranges. Comparing 28 days to the previous 28 days during a holiday distorts the picture. Use year-over-year for seasonal businesses.
• Stopping at correlation. A deploy and a drop on the same day is a strong correlation, not proof. Find the mechanism.
• Single-source diagnosis. Ahrefs sees position. Search Console sees clicks and queries. Analytics sees behavior. Logs see crawl. Use them together.
• Premature reassurance. Telling stakeholders "it is just an algorithm update, will recover" without evidence sets up a worse conversation later.

Output format

A diagnosis document with:

• Summary. What changed, when, magnitude, root cause in one paragraph.
• The symptom. Charts and segment breakdowns.
• Layer-by-layer findings. What each layer ruled in or out.
• Root cause hypothesis. Single statement with evidence.
• Action plan. Ordered fixes with owners and timelines.
• Recovery forecast. Realistic expectations on timeline and ceiling.
• Monitoring plan. What to watch for confirmation of recovery.

Length: 4-10 pages. Stakeholders read this fast.

Reference files

• references/diagnosis-checklist.md - Layer-by-layer diagnostic checklist with the specific data to pull at each layer and how to interpret each signal.