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AC black-friday-ads-readiness-check

Use for "are our ads ready for Black Friday", "what should we fix in the ad accounts before Black Friday", "how should we ramp the Black Friday ad budget", "will our tracking hold up on Cyber Monday", or a BFCM or Prime Day prep check 4–6 weeks out. Use when the decision is what to fix and how to ramp budget before the sale. Google Ads, Facebook Ads and TikTok Ads, with Shopify where connected.

coupler-io/skills Agent Skills author: coupler-io 1 file body ≈ 4 136 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Use for "are our ads ready for Black Friday", "what should we fix in the ad accounts before Black Friday", "how should we ramp the Black Friday ad budget"…

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureShopifyMarketingtype 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 70Execution cost. Instruction body is 4136 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 29 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 397: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 29 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.