SKILLEMALL.ai

BB google-search-console-country-and-device-performance

Breaks organic search performance down by country and device, from live Google Search Console data in your Coupler.io workspace — clicks, impressions, click-through rate and average position by market and by phone/desktop/tablet, against the site's own average. Use for "how does my organic search do by country", "which markets underperform in search", "is mobile or desktop better for my search traffic", "where am I losing search clicks by device", "should I localise for SEO", "which countries rank worse than my site average" — even when the user never says "country" or "device". This is the where-and-on-what view: which markets and devices earn their impressions and which lag the site. For query-level opportunities use google-search-console-keyword-opportunities; for what traffic does after landing use seo-landing-page-performance. Google Search Console only.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 3 341 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Breaks organic search performance down by country and device, from live Google Search Console data in your Coupler.io workspace — clicks, impressions…

As a process B 69/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureMarketingData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 69/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 2 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 26 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3341 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 13 top-level sections: this looks like several domains in one skill

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 871: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 26 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.