AC happy-horse-video-toolkit-ai-hive
Use this skill when the user asks for Happy Horse、快乐马、AI视频、参考生视频、视频编辑，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成Happy Horse 视频工具箱。它服务需要生成、参考或编辑视频的创作者和商业团队：接收创意、参考素材、动作、镜头、平台和时长，交付T2V/I2V/R2V模式建议、提示词、编辑任务和交付检查。特色是覆盖Happy Horse生成与编辑能力，按输入条件自动选模式；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 Happy Horse、快乐马、AI视频、参考生视频、视频编辑，或要为电商、广告、营销、短剧、漫剧、带货与种草内容完成Happy Horse…
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/videogen.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: 6. 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 883 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 357: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 39 items
- +4Has examples (3 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.