BC china-payment-integration
Integrate Chinese payment methods (WeChat Pay, Alipay, UnionPay) into applications. Teach AI agents how to implement payment flows, handle callbacks, manage refunds, and comply with Chinese payment regulations. Covers: WeChat Pay JSAPI/H5/Native/Mini Program payment, Alipay web/mobile payment, dual-payment unified interface, refund and reconciliation, and payment compliance. Triggers on: 微信支付集成, wechat pay integration, 支付宝集成, alipay integration, 银联支付, unionpay, 中国支付接入, china payment integration, 微信小程序支付, mini program payment, 支付回调, payment callback, 退款处理, refund processing, 支付合规, payment compliance, 双支付统一接口, dual payment interface
Integrate Chinese payment methods (WeChat Pay, Alipay, UnionPay) into applications.
As a process C 64/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 64/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 5 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 28 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2366 tokens
- 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 638: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 28 items
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.