BC douyin-video-studio
抖音短视频一站式解析与创作工场。用户直接输入抖音视频链接加一句简单要求即可：视频转文字（字幕/口播/画面内容）、内容分析总结、爆款视频拆解（按带货/流量逻辑拆结构分段、脚本类型、爆款归因、六维评分报告，照着学照着抄）、视频转脚本（镜头/运镜/转场/情绪/时间线/屏幕文字）、爆款脚本生成（原骨架改写成你的脚本，或按方向/目的/商品原创口播脚本，含分段表与六维质检评分）、视频提示词反推（画面和内容转成 agent 可理解的提示词）。底层经 Cue Omni Reader 远程端点解析（自动绕过抖音反爬），再由大模型按场景加工交付。
抖音短视频一站式解析与创作工场。用户直接输入抖音视频链接加一句简单要求即可：视频转文字（字幕/口播/画面内容）、内容分析总结、爆款视频拆解（按带货/流量逻辑拆结构分段、脚本类型、爆款归因、六维评分报告，照着学照着抄）、视频转脚本（镜头/运镜/转场/情绪/时间线/屏幕文字）、爆款脚本生成（原骨架改写成你的脚本，或按方向…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "description_zh"
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
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 100Steps. 86 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3421 tokens
- 100Running it twice. No mutating operations
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 tags): a typed call is more reliable
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 266: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 86 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.