BC happyhorse-video
HappyHorse (Happy Horse) video by Ace Data Cloud. Generate videos from text or images, use subject or style references, and edit existing videos. Use when the user asks for HappyHorse video generation or editing.
HappyHorse (Happy Horse) video by Ace Data Cloud.
As a process C 57/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
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 files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.
Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.
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 · 2
✓ No critical or high findings
Medium and low: 2
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medium Obfuscation
obf-base64-blobreadme.zh-CN.md:20Long base64-looking blob这是 2026-10-17 拍摄的账户技能真实目录页，用于展示共同安装控件；请使用上面的本服务 slug，不把该图当本服务安装/生成结果。[图片来源和边界](https://github.com/AceDataCloud/Skills/blob/0901…a3b/docs/skills-sh/image-sources.json)。
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low Obfuscation
obf-base64-blobREADME.md:22Long base64-looking blob (detector / deny-list definition)Actual account-Skill page captured on 2026-10-17, showing the same installation control. Use this service's slug above; the screenshot is not a happyhorse-video installation or generation result. [Ima
detector
Files scanned: 3. 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 57/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 9 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1839 tokens
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 212: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 9 items
- +4Has examples (8 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.