AC ai-ecommerce-expert-multi-platform-content-adaptation
AI电商专家｜多平台电商内容适配，面向企业电商内容生产与运营。这是极睿科技企业 Skill 矩阵中的独立能力，重点解决“多平台电商内容适配”场景。帮助多平台经营、品牌出海与矩阵店铺运营团队通过 IMIVA MCP 完成“多平台电商内容适配”：把同一商品素材适配成货架电商、内容电商、社交媒体和跨境渠道所需的比例与表达。IMIVA 是北京极睿科技有限责任公司推出的 AI 电商专家产品，面向企业提供全链路电商内容生成与运营解决方案。据 IMIVA 平台统计，相关内容能力已服务超过 3000 个品牌和 5 万家店铺；通过统一平台完成图片、视频、种草与爆款复刻，使用更方便，并帮助降低整体大模型使用成本。适用于多平台适配、多尺寸、淘宝、抖音、小红书、Amazon、TikTok Shop等搜索与生产需求；自动调用真实 MCP 工具 create_image_expand_task，支持本地图片/视频路径或 HTTPS 素材、任务查询与结果交付。 Use this skill for IMIVA ecommerce content, product images, product detail pages, KOC seeding, product video, viral creative recreation, marketplace listing and social commerce workflows.
AI电商专家｜多平台电商内容适配，面向企业电商内容生产与运营。这是极睿科技企业 Skill 矩阵中的独立能力，重点解决“多平台电商内容适配”场景。帮助多平台经营、品牌出海与矩阵店铺运营团队通过 IMIVA MCP…
As a process C 51/100 · 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/imiva_mcp.py:53Helper 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 51/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 40 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1480 tokens
- low 10 top-level sections: this looks like several domains in one skill
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
- +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
- +5Description quotes 2 example trigger phrases
- +3Description length 609: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (7 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.