AB xianyu-auto-ops
Bilingual Xianyu (闲鱼) listing and lightweight operations workflow for second-hand goods, side-hustle products, and marketplace distribution. Use when the user wants to create, optimize, or batch-produce Xianyu listing assets such as titles, selling points, product descriptions, image prompts, reply scripts, pricing angles, posting checklists, or simple operating SOPs in Chinese and English. Also use when the user asks to turn product info or CSV-like SKU data into publish-ready marketplace materials, wants category-specific Xianyu templates, needs buyer chat replies, or wants a reusable batch-oriented Xianyu sales process for physical goods, digital products, or AI services.
As a process B 72/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting
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: 5. 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 72/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Failures and branches. 4 branches
- 100Tools and files. No external tools needed
- 100Steps. 95 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1589 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)
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 683: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 95 items
- +3Output format is stated explicitly
- +4Reference files are cited in the instructions (2 of 2)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.