SKILLEMALL.ai

BB content-decay-analysis

Finds pages that are quietly losing organic traffic over time, from live Google Search Console data in your Coupler.io workspace — which pages are down on clicks, whether it's a ranking slip or fewer people searching, and which are worth updating first. Use for "which of my pages are losing search traffic", "what content is decaying", "which blog posts should I refresh", "why is my organic search traffic sliding", "which pages dropped in Google since last quarter", "find pages that need updating for SEO" — even when the user never says "decay". This is the losing-ground view: pages that used to do better and why. For pages that never ranked well in the first place use google-search-console-keyword-opportunities. A sudden site-wide drop isn't decay — this skill flags it rather than diagnosing it. Google Search Console only.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 3 783 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Finds pages that are quietly losing organic traffic over time, from live Google Search Console data in your Coupler.io workspace — which pages are down on…

As a process B 75/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerData and analyticsMarketingWriting and documentstype 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
75/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
100
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 75/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 100Tools and files. No external tools needed
    • 100Steps. 25 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3783 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 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 834: 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 6 example trigger phrases
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 25 items

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