BD smyx-payment
生命涌现支付技能，提供技能账户增值服务（充值/续费）、账户查询（余额/使用次数）、余额检查、支付宝支付集成和支付页面生成。🔴【强制性要求】每次展示套餐列表时，必须完整显示套餐 1-4，包括专属定制套餐，禁止省略定制套餐。当用户需要查询账户状态、检查余额、进行充值续费、或处理支付流程时使用此技能。自动触发关键词：技能充值、技能续费、技能余额不足、技能账户、充值、续费、账户充值、账户续费、余额不足、支付宝支付、增值账户、技能套餐、充值套餐、查询余额。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.
Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.
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 · 2
✓ No critical or high findings
Medium and low: 2
-
medium Obfuscation
obf-base64-blobscripts/qr_base64.txt:1Long base64-looking blobiVBO…AAR+ElEQ…4zz+8acN…sQS
-
low Secrets in code
secret-high-entropy-tokenscripts/qr_base64.txt:1High-entropy token-like string (may be an id, hash or a credential)iVBO…AAR+ElEQ…4zz+8acN…sQS
Files scanned: 80. 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 46/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 35 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2699 tokens
- 100Running it twice. No mutating operations
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
- -297 emoji in the instructions: noise for the model
- -43 reference files, but SKILL.md never points to them: the model will not open them
- -353 of 53 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 226: enough signal without eating the budget
- +4Structure: 34 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (16 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.