AC crypto-payments-ecommerce
Accept crypto and stablecoin payments for e-commerce stores with self-hosted PayRam. Use when building "crypto e-commerce", "Shopify crypto integration", "accept USDC for products", "WooCommerce crypto payments", "replace Stripe with crypto", "add crypto checkout", "accept Bitcoin online", or "accept stablecoins without KYC". Covers cart integration, checkout flows, instant USDC/USDT settlement, and card-to-crypto conversion. No signup, no KYC required. $300B stablecoin market with 56% of holders planning to buy more (2026).
As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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: 2. 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 58/100
- 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
- 30Running it twice. 26 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 70Execution cost. Instruction body is 4223 tokens
- 100Tools and files. No external tools needed
- 100Steps. 89 steps
- 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
- -250 emoji in the instructions: noise for the model
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +3Description length 530: enough signal without eating the budget
- +4Structure: 51 headings
- +3Step-by-step instructions: 89 items
- +4Has examples (21 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.