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BB google-search-console-keyword-opportunities

Finds the clicks you're leaving on the table in Google Search, from live Search Console data in your Coupler.io workspace — queries ranking just off page one, pages that get seen but not clicked, and pages quietly competing with each other for the same query. Use for "which keywords are close to page one on my site", "striking distance keyword report", "which of my pages get search impressions but no clicks", "find my organic CTR gaps", "are my pages cannibalizing each other in search", "where are my quick SEO wins", "what should I optimize for organic search next" — even when the user never says "opportunity". The earning view: which search positions are worth acting on and what the action is. For pages losing traffic they used to have, use content-decay-analysis. Google Search Console only.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 4 250 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Finds the clicks you're leaving on the table in Google Search, from live Search Console data in your Coupler.io workspace — queries ranking just off page one…

As a process B 70/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerData 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
70/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
70
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 70/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4250 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 27 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 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 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 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 8 example trigger phrases
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 27 items

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