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BC shopify-automation

Automate Shopify tasks via Rube MCP (Composio): products, orders, customers, inventory, collections. Always search tools first for current schemas.

The skillemall take

Promises to automate Shopify tasks via MCP Composio: products, orders, customers, inventory, collections. Single instruction file at 1528 tokens, no scripts. Quality score 84/100, process score 57/100, zero critical findings. Safety intact, but the gap between quality and process suggests sparse examples or test coverage. Works across 12 platforms including Claude and Cursor. In practice, you get routine Shopify API integration through an existing MCP without surprises. Install if you need basic store automation—the skill points the way but won't solve edge cases on its own.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 1 641 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Automate Shopify tasks via Rube MCP (Composio): products, orders, customers, inventory, collections.

As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

GeneratorShopifyAI and agentstype 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
C
62/100
Has gaps
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: agentic-awesome-skills

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 62/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 7 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 59 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1641 tokens

    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 147: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 59 items
    • +4Has examples (1 code blocks)

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