BD jianying-ai-text-video-infinite
自动化剪映电脑客户端的文字成片功能。使用场景：(1) 用户说"AI文字成片"、"剪映AI文字成片"、"用剪映生成视频"；(2) 用户需要将分镜脚本转换为视频；(3) 用户提到剪映、剪映专业版。注意：此技能操作的是电脑上安装的剪映桌面软件，不是剪映网页版。配置画面素材为未来科幻、分镜类型为一镜到底、视频比例为9:16、配音为真人播客女。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
ReferenceMedia and videoInfrastructuretype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
- 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 · 0
✓ No critical or high findings
Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Implicit keys need to be on a single line at line 1, column 1: name:jianying-ai-text-video-infinite ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 11 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1512 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -4Absolute local paths (C:\Users, /home/…): not portable
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 168: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 11 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.
External checks
ClawHub: suspicious
This appears to be a real Jianying desktop automation skill, but it needs review because it can control the desktop, overwrite clipboard/text, save screenshots, and install a dependency.
LLM: suspicious (high) · VirusTotal: · 29 May 2026