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BB ecom-analytics

Use this skill when the user wants to analyze e-commerce performance, review funnel conversion, investigate AOV or repeat-purchase trends, build an ecom report, check sales velocity, understand customer cohorts and retention, or answer questions about their store's sales data. Triggers include 'how is my store performing', 'what's my conversion rate', 'cart abandonment', 'AOV trend', 'repeat purchase rate', 'why did revenue drop', 'cohort retention', 'top products', 'new vs returning customer revenue', 'lifetime value', 'pull my Shopify numbers', 'ecom report'.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 3 746 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Use this skill when the user wants to analyze e-commerce performance, review funnel conversion, investigate AOV or repeat-purchase trends, build an ecom…

As a process B 71/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerShopifyStripeWordPressGoogle AnalyticsData and analyticstype 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%
84
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
30
Failures and branches w 10
50
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 71/100

    • 0Result and completion. Does not say what the result is
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 50Failures and branches. 0 branches, has a failure section
    • 85Steps. 33 steps, 3 vague phrases
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3746 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

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 567: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 33 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.