AC product-image-suite-generation-editing
用 AI Hive Nano Banana Pro 为同一 SKU 生成和编辑可审核的商品图片套组，把白底主图、场景主图、卖点图、细节图、尺寸图、对比图、生活方式图与本地化图片纳入同一套商品事实和视觉规则。Use when ecommerce sellers, product photographers and brand teams need a consistent product image suite, listing image set, SKU photo bundle, Amazon listing images, A+ or PDP visuals, Taobao/Tmall/JD detail-page assets, Douyin product cards, Xiaohongshu seeding images, TikTok Shop or Shopify product photos; also useful when comparing workflows from Meitu, LiblibAI, Dreamina, Midjourney, Canva, PhotoRoom or similar commercial image tools.
用 AI Hive Nano Banana Pro 为同一 SKU 生成和编辑可审核的商品图片套组，把白底主图、场景主图、卖点图、细节图、尺寸图、对比图、生活方式图与本地化图片纳入同一套商品事实和视觉规则。Use when ecommerce sellers, product photographers and…
As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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: 4. 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 55/100
- 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
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1665 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
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 544: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (7 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.