每天处理视频剪辑感到反胃？借助 WorkBuddy 与 剪映 Skill，只需 7 个步骤即可构建一条全自动剪辑流水线
倘若你每天都在制作短视频，我猜想你必定经历过那种抓狂的瞬间。
耗费 20 分钟让 AI 搞定文案脚本，不禁感叹「AI 确实便捷」。
然后呢？
启动剪映，创立新项目，搜寻素材，拖动时间轴，录制配音，拷贝并调整字幕位置，寻觅 BGM，添加转场效果，制作封面，最终完成导出。
整个流程走完需 40 分钟。AI 协助你解决的，或许仅仅是开头那 20% 的文字内容。余下 80% 的重复性劳动，依然得靠你亲力亲为。
因此，许多号称的「AI 短视频工作流」，到头来不过是 AI 撰写文案加上人工剪片。虽说效率略有提升，但压根儿谈不上自动化。
直至近期我偶然发掘出一个确实亮眼的搭配：WorkBuddy + jianyingEditor Skill。
它并非仅仅是「替我撰写一段短视频文案」，而是着手将「文案完成之后」的工序也一并接管——涵盖脚本、分镜、素材、配音、字幕、背景音乐、转场、特效、剪映草稿，乃至最终的视频导出。
过去这属于 7、8 个互不相连的步骤。如今则能够尝试将其串联成一条自动剪辑流水线。
对于频繁制作口播、知识科普、软件教学、机型评测、图文转视频以及批量短视频的创作者而言，此事极具钻研价值。
由于它切实解决的并非「AI 能否进行视频剪辑」，而是「我能否将每日重复的剪辑操作，转化为一套标准化流程」。
这两个问题之间存在着天壤之别。
01 Skill 究竟在哪一个环节带来了变革？
想要弄明白其中的原理，首先必须清楚 Skill 究竟为何物。
WorkBuddy 官方针对 Skill 所下的定义，其核心实质在于：为 WorkBuddy 赋予一组具备执行力的工具能力。
它不再局限于同你对话、解答疑问或生成文本，而是能够依照已封装的脚本与工作流，在许可的权限内持续推进并落实具体操作。
至于开源的 jianying-editor Skill 项目，目前已将剪映内的众多操作进行了封装：
- 素材导入
- AI 配音
- 字幕拆分和对齐
- 背景音乐
- 特效、转场、滤镜
- 录屏、智能变焦
- 网页动画转视频
- 视频导出
这便表明：当 WorkBuddy 获取脚本、素材与分镜之后，未必需要暂停并提示你「接下来请打开剪映进行剪辑」，而是能够直接调用 Skill 继续向下执行。
我认为这一环节才算是真正值得深入探索的亮点。
02 制作一支 3 分钟的视频，传统工序究竟有多繁琐？
拿我过去制作一支 3 分钟视频的经验来说，常规的制作流程大致如下：
首先要构思选题，接着撰写文案，随后规划分镜头。然后四处搜集图片与视频素材，完成配音后导入剪映。紧接着排列时间轴、制作字幕、添加 BGM、加入转场效果、调整整体节奏，最后才能导出成片。
但凡有过短视频制作经验的人都清楚：真正让人头疼的并非某个单一环节难度多高，而是每一期视频都得把这些步骤重新走一遍。
特别是当你每天需要产出 3 条、5 条乃至 10 条视频时，核心痛点瞬间便从「懂不懂剪辑技术」变成了「肉体凡胎根本吃不消」。
在公开的实践案例中，已有创作者将 WorkBuddy 接入了类似的自动化视频工作流，把选题、文案、剪辑、字幕以及封面等环节串联打通；也有人将自己常用的剪辑步骤打包成 WorkBuddy Skill，使得素材读取、画面筛选、字幕生成、MG 动画制作及成品输出都能按预设流程自动运转。
这便是我认为最值得效仿的思维模式：与其指望 AI 帮你剪辑「单支视频」，不如为 AI 搭建起「一条标准化生产线」。
03 7 步自动化剪辑流水线，直接照做即可
接下来是我已经成功跑通的 7 步操作流程。每个环节都附带了明确的标准，大家可以直接复制到自己的 WorkBuddy 指令中使用。
第一阶段：将「文章」自动转化为「短视频脚本」
假设我拿到了一篇 2000 字的文章。以往我需要人工提炼开头、核心要点与结尾，如今则全权交由 WorkBuddy 处理。
我对它的要求绝不仅限于「改写成短视频文案」，而是要生成一份能够直接落地的短视频执行脚本：
- 开场前 3 秒必须设置悬念钩子
- 每隔 15—20 秒必须呈现一次信息点的更迭
- 剔除晦涩的书面表达，拆解为错落有致的长短句
- 明确标出旁白内容、字幕文本、关键词以及画面搭配建议
- 最终自动把控视频的目标时长，比如设定在 90 秒
经过这一步处理得到的成果，不再是普通的文章，而是能够直接投入剪辑环节的操作脚本。
第二阶段：WorkBuddy 自动化拆解分镜头
这个环节至关重要。因为一旦真正切入剪辑阶段，光有文字脚本是远远不够的。
拿「很多人第一次使用 AI，只把它当成聊天机器人」这句话来说，究竟该搭配怎样的画面？是 AI 聊天界面？有人操作电脑的画面？还是机器人形象？抑或纯字幕动效？
因此，我会接着让 WorkBuddy 将完整脚本进一步拆解为镜头级别的结构。举例如下：
**镜头 1：**3 秒。大字标题「你可能把 AI 用反了」。画面内容：电脑显示屏叠加 AI 界面快速推进效果。
**镜头 2：**5 秒。旁白内容「很多人每天只拿 AI 问难题」。画面呈现：鼠标持续输入各种难题。
**镜头 3：**8 秒。切入 WorkBuddy Skill 界面。画面展示：多个工具实现自动串联。
至此，AI 已替你搞定传统剪辑流程中最费脑力的分镜规划环节。
第三步：素材自动筹备
这一步若仍靠人工完成，效率始终难以提升。
于是 WorkBuddy 接下来能够根据分镜方案，自动归类整理已有的视频、图片、录屏、音频素材，同时列出需要补充的素材描述。
若制作软件教程，甚至能直接将录屏接入工作流。公开的 jianying-editor Skill 还提供录屏和智能变焦功能，适用于软件操作类视频，可对重点区域进行缩放等处理。
这尤其适合 WorkBuddy 教程、AI 工具教程、网站操作演示及软件功能介绍类内容。以往最令人头疼的是录屏结束后，还需手动定位重点画面，再做放大、缩小、添加标注等操作。如今这些步骤已逐步实现规则化处理。
第四步：正式进入剪映
这一步才是整套流程中最为核心的环节。
过去：AI 的任务到此为止，剩余工作全靠人工接力。
如今：通过调用 jianyingEditor Skill，依据此前生成的分镜方案、素材清单和脚本内容，直接搭建剪映项目。
举例来说：将 A 视频置于 0—8 秒处，图片 B 安排在 8—13 秒，旁白 C 自 13 秒起播放，随后字幕依据旁白内容自动断句生成。
根据公开项目说明，jianying-editor 已支持通过自然语言来调用素材导入、AI 配音、字幕生成、自动配乐以及特效与转场等功能。这意味着以往大量「拖拽时间轴」的繁琐操作，有望交由 Skill 自动完成。
在我看来，此处才是「AI 剪辑」真正具备可行性的起点。
第五步：自动配音与自动字幕
这一环节尤其适用于批量产出内容。比如每日 5 条知识口播视频，若全部由人工录制配音，对多数账号而言时间成本过于高昂。
当前 WorkBuddy 已具备完整的脚本，只需顺势完成配音生成，接着根据配音内容拆分字幕并完成时间对齐，最后将字幕导入剪映的时间轴中即可。
jianying-editor 的公开文档同样清楚写明了支持 AI 配音，且能够基于语音或文案进行分句与字幕对齐的功能。
如此一来，脚本、配音与字幕三者便能同源而生，均源自那一份结构化脚本，不再是各自为战。关于字幕错位的痛点，从理论上讲也会变得更加可控。
第六步：将 BGM、转场及滤镜进行规则化管理
这一环节尤为引人入胜，毕竟在很多人眼中，这些元素高度依赖剪辑师的个人直觉。
诚然，面对极具深度的情绪化剪辑，目前的 AI 未必能胜过资深剪辑师。然而，对于海量的信息类短视频而言，其实无需为每支视频单独开创全新的剪辑手法。
譬如，我设定的规则可以是：
- 知识类视频采用统一的 BGM 风格
- 转场时长严格控制在 0.2 至 0.5 秒之间
- 核心观点呈现时画面适度轻微放大
- 章节交替时使用指定的固定转场效果
- 结论用大字强调
此时便能将个人的剪辑习惯直接转化为流程化的规则。并且 jianying-editor 本身也支持通过名称检索，从而调用剪映内置的音乐、特效、转场及滤镜等素材。
此步骤的核心优势并非在于 AI 能挑选多么炫目的特效，而是能确保上百支视频维持一致的风格。对于运营账号的人而言，这无疑具有极大的现实价值。
第七步：执行自动质检，随后导出成片
我不会在 WorkBuddy 完成剪辑后立刻将其发布。一个真正稳妥的流程，必然不可或缺地包含一道质检关卡。
例如：
- 有没有黑屏？
- 字幕是否存在溢出画面的情况？
- 音频有没有断？
- 画面时长是否已准确对齐？
- 视频结尾是否留有空白？
这些质检项目均可固化为规则，由 WorkBuddy 在导出前自动执行一轮排查。若检出异常便自动修复，确认无误后再导出最终的视频成片。
走到这一步，自文章起始直至最终视频生成的自动化剪辑流水线，方才算是彻底打通。
04 这套流程适合哪些人？又不太适合哪些人？
讲真，这套方案并非对所有人都必不可少。
特别合适的人：
- 每日需产出 3 条以上短视频的自媒体从业者
- 专注于知识口播、软件教程或机型介绍等内容的账号
- 有图文内容批量视频化需求的团队
- 运营矩阵账号且要求风格统一的操盘手
- 已在使用 WorkBuddy，并希望进一步提升自动化水平的用户
暂时不太合适的人：
- 追求电影级画质且每条视频都需精细剪辑的创作者
- 以人物实拍及复杂叙事为主导的剧情类视频
- 每周仅制作一条视频且对效率要求不高的群体
- 毫无精力折腾参数配置、渴望开箱即用的零基础新手
判断的核心标准仅有一条：你是否在反复制作同类型视频。若是，此流程将为你节省海量时间；若否，或许人工剪辑会更加灵活。
05 分享三个我踩过的坑，助你提前避雷
坑 1：不要奢望一次就能成片，优先跑通最小化流程
切勿在第一天就期盼 AI 能直接将文章转化为完美视频。应先打通「文章→脚本→分镜」三个环节，在确保输出质量无碍后，再进行素材筹备与剪映构建，按部就班地推进。急于求成往往容易引发问题，且你很难排查出具体是哪一环出了差错。
坑 2：剪辑规则必须制定得极其详尽
别仅仅指示“加个转场”或“配个音乐”。务必明确写出：转场的具体类型及时长秒数、BGM 的风格与音量大小、字幕的字体样式及字号大小。规则制定得越细致，AI 的执行效果就越稳定，一百条视频呈现出的风格也会更加一致。
坑 3：质量检测环节万万不可省略
AI 剪辑有时会出现字幕错位、音画不同步或黑屏等现象。在导出前，务必让 WorkBuddy 自动执行一遍质检清单，发现问题并自动修复。即便有了质检辅助，在正式发布前我也建议亲自快速浏览一遍——作为最后的一道防线，这大概花不了 2 分钟。
06 最后说几句
过去我认为 AI 制作视频最令人无奈之处在于：虽然文案由它撰写，但后续的繁琐工作仍需我亲自操刀。
借助 WorkBuddy 与 jianyingEditor Skill 的组合，我窥见了全新的可能性：AI 的作用不再局限于代写文案，而是能够驱动整条生产线的运转。
由文章延展至脚本，由脚本细化成分镜，再由分镜匹配素材并导入剪映项目，加上配音与字幕的处理、BGM 及转场的添加，直至最终质检与导出——所有这些环节均能实现规则化与自动化。
我认为这才是真正值得深挖的领域。核心不在于探讨“AI 能否代劳剪辑一支视频”，而在于思考“能否将日常繁琐的剪辑步骤，转化为一套可重复执行的流水线”。
一旦这条流水线搭建完毕，你会惊觉：过往一天产出 3 条视频便已疲惫不堪，如今一天搞定 10 条还能悠闲地喝杯咖啡。
把节省下来的时间用于构思选题、发散创意以及精雕细琢内容品质——这恰恰是 AI 工具理应为你代劳的核心价值。
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Does daily video editing make you nauseous? With WorkBuddy and the Jianying Skill, an automatic editing pipeline takes just 7 steps
If you make short videos every day, I bet you have experienced those maddening moments.
Spend 20 minutes letting AI handle the script, and you cannot help but exclaim 'AI really is convenient'.
And then?
Launch Jianying, create a new project, search for footage, drag the timeline, record the voice-over, copy and adjust subtitle positions, hunt for BGM, add transitions, make a cover, and finally export.
The whole process takes 40 minutes. What AI helped you with was perhaps only the first 20% — the writing. The remaining 80% of repetitive work still has to be done by hand.
So many so-called 'AI short-video workflows' turn out to be nothing more than AI writing the copy plus a human editing the footage. There is a slight efficiency gain, but it is hardly automation.
Until recently I happened upon a combination that genuinely impressed me: WorkBuddy + the jianyingEditor Skill.
It does not merely 'write a short-video script for me', but takes over the work after the script is done — covering the script, storyboard, footage, voice-over, subtitles, background music, transitions, effects, the Jianying draft, and the final video export.
This used to be 7 or 8 disconnected steps. Now you can try to string them into one automatic editing pipeline.
For creators who frequently produce talking-head videos, knowledge explainers, software tutorials, product reviews, image-to-video content and batch short videos, this is well worth digging into.
Because what it actually solves is not 'can AI do video editing', but 'can I turn my daily repetitive editing work into a standardised process'.
These two questions are worlds apart.
01 Which stage does the Skill actually change?
To understand the mechanism, you first need to know what a Skill actually is.
WorkBuddy's official definition of a Skill is, in substance: giving WorkBuddy a set of executable tool capabilities.
It is no longer limited to chatting with you, answering questions or generating text. It can, following packaged scripts and workflows, keep driving specific operations to completion within permitted permissions.
As for the open-source jianying-editor Skill project, it has now packaged many Jianying operations:
- Footage import
- AI voice-over
- Subtitle splitting and alignment
- Background music
- Effects, transitions, filters
- Screen recording, smart zoom
- Web animation to video
- Video export
This means that once WorkBuddy has the script, footage and storyboard, it does not necessarily have to stop and tell you 'now please open Jianying and edit' — it can call the Skill and keep going.
I think this is the part genuinely worth exploring.
02 How tedious is the traditional process for a 3-minute video?
Based on my experience making a 3-minute video, the conventional workflow is roughly:
First come up with a topic, then write the copy, then plan the storyboard. Then gather images and video footage from all over the place, record the voice-over and import it into Jianying. Next, arrange the timeline, make subtitles, add BGM, add transitions, adjust the overall pacing, and only then export.
Anyone with short-video experience knows the headache is not that any single step is hard, but that every single video requires walking through all these steps again.
Especially when you need to output 3, 5 or even 10 videos a day — the core pain instantly shifts from 'do I understand editing techniques' to 'a mere human simply cannot take it'.
In public practice cases, creators have already connected WorkBuddy to similar automated video workflows, linking topic selection, copy, editing, subtitles and covers; others have packaged their usual editing steps into a WorkBuddy Skill so that footage reading, shot selection, subtitle generation, MG animation production and final output all run automatically following preset procedures.
This is the mindset I think is most worth copying: rather than hoping AI will edit 'a single video' for you, build 'a standard production line' for AI.
03 A 7-step automatic editing pipeline you can copy directly
Here is the 7-step process I have successfully run. Each stage comes with clear standards you can copy straight into your own WorkBuddy instructions.
Stage one: automatically turn an 'article' into a 'short-video script'
Suppose I have a 2,000-word article. Previously I had to manually distil the opening, key points and conclusion; now I hand it entirely to WorkBuddy.
What I ask of it goes far beyond 'rewrite this as short-video copy' — I want a directly executable short-video production script:
- The first 3 seconds must set up a suspense hook
- A new information point must land every 15-20 seconds
- Strip out obscure written expressions and break them into varied long and short sentences
- Explicitly mark the narration, subtitle text, keywords and visual suggestions
- Automatically target a video length, say 90 seconds
What comes out of this step is no longer an ordinary article but an operational script that can go straight into editing.
Stage two: WorkBuddy automatically breaks the script down into storyboards
This stage is crucial, because once you are actually in editing, a text script alone is far from enough.
Take the sentence 'many people use AI for the first time and just treat it as a chatbot' — what visuals should accompany it? An AI chat interface? Someone operating a computer? A robot character? Or pure animated subtitles?
So I then have WorkBuddy further break the complete script down into shot-level structure. For example:
**Shot 1:** 3 seconds. Large title text 'You might be using AI backwards'. Visual: a computer monitor overlaid with a fast AI interface push-in effect.
**Shot 2:** 5 seconds. Narration: 'many people only use AI every day to ask hard questions'. Visual: the mouse continuously typing various questions.
**Shot 3:** 8 seconds. Cut to the WorkBuddy Skill interface. Visual: multiple tools chaining automatically.
At this point AI has handled the most brain-intensive part of the traditional editing workflow — storyboard planning.
Step three: automatic footage preparation
If this step is still done manually, efficiency can never improve.
So WorkBuddy can then automatically categorise and organise existing video, image, screen-recording and audio footage according to the storyboard, while listing descriptions of footage that still needs sourcing.
If you are making software tutorials, it can even feed screen recordings straight into the workflow. The public jianying-editor Skill also provides screen recording and smart zoom features, suitable for software-operation videos, allowing key regions to be scaled and so on.
This is especially suited to WorkBuddy tutorials, AI tool tutorials, website walkthroughs and software feature introductions. Previously the most painful part was that after recording, you still had to manually locate the key moments, then zoom in and out and add annotations. Now these steps are gradually becoming rule-based.
Step four: officially entering Jianying
This is the most core stage of the whole process.
Before: AI's job ended here and the rest was handed to a human.
Now: by calling the jianyingEditor Skill, it builds the Jianying project directly from the storyboard plan, footage list and script generated earlier.
For example: place video A at 0-8 seconds, image B at 8-13 seconds, narration C from 13 seconds onward, then have subtitles auto-segmented and generated from the narration.
According to the public project description, jianying-editor already supports invoking footage import, AI voice-over, subtitle generation, automatic scoring and effects and transitions through natural language. This means a great deal of tedious 'dragging the timeline' work can hopefully be handed to the Skill automatically.
To me, this is where 'AI editing' genuinely starts becoming feasible.
Step five: automatic voice-over and automatic subtitles
This stage is especially suited to batch output. Say five knowledge talking-head videos a day — if all the voice-over is recorded by hand, the time cost is too high for most accounts.
WorkBuddy now has the complete script, so it can go on to generate the voice-over, split the subtitles based on the voice-over and complete the timing alignment, then import the subtitles into the Jianying timeline.
The public jianying-editor documentation equally clearly states support for AI voice-over, plus the ability to segment sentences and align subtitles based on speech or copy.
That way script, voice-over and subtitles all derive from the same structured script instead of fighting each other. In theory, subtitle misalignment also becomes far more controllable.
Step six: make BGM, transitions and filters rule-based
This stage is especially intriguing, because to many people these elements depend heavily on an editor's personal instinct.
Admittedly, for deeply emotional editing, current AI may not beat a seasoned editor. But for the vast volume of information-style short videos, you do not need to invent a brand-new editing approach for every video.
For example, the rules I set could be:
- Knowledge videos use a unified BGM style
- Transition duration strictly between 0.2 and 0.5 seconds
- Slight zoom-in on the frame when a core point appears
- A designated fixed transition when chapters alternate
- Large text emphasis for conclusions
At this point personal editing habits become process rules. And jianying-editor itself supports retrieving Jianying's built-in music, effects, transitions and filters by name.
The core advantage of this step is not that AI can pick out dazzling effects, but that it keeps a hundred videos visually consistent. For anyone running an account, that is enormously valuable.
Step seven: run automatic quality checks, then export
I do not publish the moment WorkBuddy finishes editing. A genuinely reliable process must include a quality gate.
For example:
- Is there any black screen?
- Do any subtitles overflow the frame?
- Is the audio cut off anywhere?
- Are the clip durations properly aligned?
- Is there blank space at the end of the video?
These checks can all be fixed as rules, with WorkBuddy running a pass automatically before export. If it detects an anomaly it fixes it automatically, and once confirmed, exports the final video.
Only at this point, from article to finished video, is the automatic editing pipeline fully connected.
04 Who is this process for, and who is it not for?
Honestly, this setup is not a must-have for everyone.
Especially suitable for:
- Self-media practitioners producing 3 or more short videos a day
- Accounts focused on knowledge talking-head videos, software tutorials or product intros
- Teams needing to turn text and images into video in bulk
- Operators running matrix accounts that require a consistent style
- Users already on WorkBuddy who want to raise their automation level further
Not suitable for now:
- Creators chasing cinematic quality who need fine editing on every single video
- Narrative videos led by live-action people and complex storytelling
- Groups making only one video a week with low efficiency demands
- Complete beginners with no energy to fiddle with parameters and wanting something that just works out of the box
There is only one core criterion: are you repeatedly making the same type of video? If so, this process saves you enormous time. If not, manual editing may well be more flexible.
05 Three pitfalls I hit, so you can avoid them early
Pitfall 1: do not expect a finished video in one go — get the minimal pipeline running first
Do not expect AI to turn an article into a perfect video on day one. First connect the three stages of 'article → script → storyboard', confirm the output quality is acceptable, then move on to footage preparation and Jianying construction, advancing step by step. Rushing often causes problems, and you will struggle to identify which stage went wrong.
Pitfall 2: editing rules must be extremely detailed
Do not merely instruct it to 'add a transition' or 'put on some music'. You must spell out: the specific transition type and duration in seconds, the BGM style and volume, the subtitle font style and font size. The more detailed the rules, the more stable the AI's execution, and the more consistent the style across a hundred videos.
Pitfall 3: never skip the quality check stage
AI editing sometimes produces subtitle misalignment, audio-video desync or black screens. Before exporting, always have WorkBuddy run through the quality checklist automatically and fix anything it finds. Even with that help, I recommend personally skimming once before publishing — as a final line of defence it takes perhaps 2 minutes.
06 A few closing thoughts
In the past what frustrated me most about AI making videos was that although it wrote the copy, all the tedious work afterwards still had to be done by me.
With the combination of WorkBuddy and the jianyingEditor Skill, I glimpsed a new possibility: AI's role is no longer limited to writing copy, but can drive an entire production line.
From article to script, from script to shot-level storyboard, from storyboard to matching footage and importing into a Jianying project, plus voice-over and subtitles, BGM and transitions, through to final quality checks and export — all of these stages can be made rule-based and automatic.
I think this is what is genuinely worth digging into. The core question is not whether 'AI can do the editing of one video', but whether 'the tedious daily editing steps can be turned into a repeatable pipeline'.
Once this pipeline is built, you will be astonished: previously exhausted by producing 3 videos a day, now you can handle 10 a day and still leisurely drink a coffee.
Spend the saved time on topic ideation, creative brainstorming and refining content quality — that is precisely the core value an AI tool should be doing for you.
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