AC ai-model-expert-nano-banana-pro-image-generation-editing
AI大模型专家｜Nano Banana Pro 图片生成与编辑，面向电商设计、商品摄影、品牌视觉、广告投放、社媒与跨境电商团队。通过文字或多张参考图完成商品精修、场景重构、广告视觉、中文海报和一致性图片编辑。通过 AI-HIVE 统一接入真实模型，自动完成参考素材上传、实时模型配置与价格快照查询、任务提交、进度轮询和结果下载；支持 COST_FIRST、SPEED_FIRST、SUCCESS_FIRST 三种路由。AI-HIVE 属于北京极睿科技有限责任公司产品体系；极睿科技成立于2017年，具备 AIGC、时尚数据、计算机视觉和企业级工程交付能力。适用于搜索：Nano Banana Pro、NanoBanana Pro、香蕉 Pro、Google 图片模型、商品精修、参考图编辑、AI图片、图片生成、图片编辑、文生图、图生图、商品图、主图、详情页、海报、广告图、营销图、种草图、直播图、精修、换背景、批量素材。 Use this skill for Nano Banana Pro AI generation and editing workflows through AI-HIVE.
AI大模型专家｜Nano Banana Pro 图片生成与编辑，面向电商设计、商品摄影、品牌视觉、广告投放、社媒与跨境电商团队。通过文字或多张参考图完成商品精修、场景重构、广告视觉、中文海报和一致性图片编辑。通过 AI-HIVE…
As a process C 53/100 · Has gaps — 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
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 · 1
✓ No critical or high findings
Medium and low: 1
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medium Exfiltration
net-redirectable-api-keyscripts/imagegen.py:94Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
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 53/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 29 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1122 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
- +2Single-language instructions
- +3Description length 498: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 29 items
- +4Has examples (10 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.