AD volcengine-ai-mediakit
火山引擎 AI MediaKit 音视频处理 Skill。当用户需要对音视频进行加工处理时触发。处理完成后自动查询任务状态并返回产物播放链接。核心能力分为七类：1. 视频处理：多片段拼接、片段裁剪、画面翻转、视频播放调速、音频播放调速、图片合成视频、音画合成、提取音轨、音频混音; 2. 音频处理：人声/伴奏分离、音频降噪; 3. 视频增强：综合画质修复、AI 超分、智能插帧; 4. 字幕处理：语音转字幕(ASR)、画面文字提取(OCR)、硬字幕擦除、添加内嵌字幕; 5. 智能分析：智能场景切分、人像抠图、绿幕抠图; 6. AI 创作：AI 视频翻译(声影智译)、短剧高光剪辑、AI 剧本还原、AI 解说视频生成、AI 漫剧转绘。 7. 媒资查询：获取媒资信息及播放地址（支持批量）。触发关键词：视频拼接、视频裁剪、视频剪辑、视频变速、视频翻转、图片转视频、音视频合成、提取音频、混音、人声分离、背景音分离、音频降噪、去噪、AI超分、超分辨率、画质修复、画质增强、智能补帧、视频插帧、提高帧率、语音转字幕、语音识别、ASR、OCR、文字提取、字幕擦除、去字幕、添加字幕、内嵌字幕、SRT字幕、智能切片、场景切分、镜头分割、人像抠图、抠人像、绿幕抠图、抠绿幕、视频抠图、视频翻译、AI翻译、声影智译、字幕翻译、语音翻译、面容翻译、多语言翻译、视频本地化、高光剪辑、高光提取、短剧剪辑、集锦、宣传片、剧本还原、AI剧本、视频转剧本、剧情提取、解说视频、AI解说、二创解说、短剧解说、漫剧转绘、漫画风格、3D卡通、视频转绘、风格转换、获取媒资信息、查询视频信息、获取播放地址、批量查询Vid。不适用场景：纯文本生成、实时流媒体处理、AI 生成式视频创作（无源素材输入）。
As a process D 46/100 · Unfinished process — 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: 70. 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")
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 (bash, python) that frontmatter does not declare
- 100Steps. 16 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1954 tokens
- 100Running it twice. No mutating operations
- low The response is described with custom markup (31 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
- -38 of 38 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 734: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 16 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (28 of 29)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.