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BA seo-traffic-diagnosis

Diagnose organic traffic changes (drops, stalls, or unexpected wins) using Ahrefs MCP plus Search Console data. Use this skill when traffic suddenly dropped, has been flat despite investment, after an algorithm update, after a migration or deploy, or when a competitor seems to be taking share. Triggers on traffic dropped, traffic decline, traffic stalled, organic decline, lost rankings, why is traffic down, algorithm update, post-migration traffic loss, traffic diagnosis. Also triggers when stakeholders are panicking about an organic traffic number they cannot explain. A panic about tracking, event, or dashboard setup is a measurement problem, not a traffic problem: route those to `analytics-strategy`.

rampstackco/claude-skills Agent Skills author: rampstackco 3 files body ≈ 1 996 tokens Open the sourcegithub.com↗ analyzed 8 h ago

Diagnose organic traffic changes (drops, stalls, or unexpected wins) using Ahrefs MCP plus Search Console data.

As a process A 80/100 · Runs to the end — weak spots: running it twice

AnalyzerInfrastructureData and analyticsMarketingtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
A
80/100
Runs to the end
Running it twice w 4
30
Failures and branches w 10
50
Result and completion w 14
60
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: seo-traffic-diagnosis (rampstackco/claude-skills)

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "catalog_summary"
    • note frontmatter-key unknown frontmatter key "display_order"

    Process rating: all ten parameters 80/100

    • 30Running it twice. 21 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 77 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1996 tokens
    • 100Progress reporting. Reports progress

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 711: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 77 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.