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AC shopify-review-triage

Use this skill when someone wants public Shopify App Store reviews, low-star reviews, or merchant feedback triaged, prioritized, clustered, or turned into a product or support brief. Trigger for prompts like "triage these app store reviews", "what should we fix first from this feedback", "cluster our 1-star reviews", or "write a weekly low-star review brief", for a single Shopify app or a portfolio plus watched competitors. Produces a P0-P3 brief covering incident risk, repeated friction, pricing confusion, feature requests, and an explicit needs-human-read bucket, where every item keeps its public source link and stays labeled first pass or human-checked. Do not trigger for support tickets, order data, or any other private merchant data, and never use it to reply to or contact a reviewer.

github/awesome-copilot Agent Skills author: github MIT 1 file body ≈ 3 831 tokens Open the sourcegithub.com↗ analyzed 30 h ago

Use this skill when someone wants public Shopify App Store reviews, low-star reviews, or merchant feedback triaged, prioritized, clustered, or turned into a…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions

AnalyzerShopifyInfrastructuretype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
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 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 36 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3831 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 10 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

    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 800: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 36 items
    • +4Has examples (5 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.