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BC seo-landing-page-performance

Joins Google Search Console and GA4 to show which landing pages bring organic traffic and what that traffic does once it lands, from live data in your Coupler.io workspace — search clicks and rankings next to engagement, key events and revenue, per page. Use for "does my organic search traffic convert", "which landing pages actually make money from SEO", "which SEO pages are worth the effort", "my rankings are up but sales aren't", "which pages get search clicks but no conversions", "landing page SEO report" — even when the user never says "landing page". This is the search-to-outcome view: the page ranks, gets clicked, then what. For search-side only use google-search-console-keyword-opportunities; for AI-referral traffic use ai-traffic-vs-organic-search-report. Needs GA4 and Search Console.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 3 905 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Joins Google Search Console and GA4 to show which landing pages bring organic traffic and what that traffic does once it lands, from live data in your…

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureGoogle 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 61/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 26 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3905 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 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 803: 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 7 example trigger phrases
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 26 items

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