AC seedream-5-image-studio-ai-hive
Use this skill when the user asks for Seedream 5、即梦图片、AI生图、图片编辑、广告图片，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成Seedream 5 图片工作室。它服务即梦图片创作者、品牌设计和电商视觉团队：接收创意、商品或人物参考、比例、风格、文字和禁用元素，交付图片提示词、参考图方案、批量变体和平台适配稿。特色是通过AI-HIVE统一完成模型查询、任务跟踪和结果下载；需要生成素材时通过AI-HIVE OpenAPI完成配置查询、参考素材上传、价格快照、COST_FIRST/SPEED_FIRST/SUCCESS_FIRST路由、异步轮询和下载。不要把本Skill用于未授权复刻、虚假商品声明、伪造用户证言或规避平台规则。
Use this skill when the user asks for Seedream 5、即梦图片、AI生图、图片编辑、广告图片，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成Seedream 5…
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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 59/100
- 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
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 39 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 788 tokens
- 100Running it twice. No mutating operations
- 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
- +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 347: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 39 items
- +4Has examples (2 code blocks)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.