¶ Not Only Write a Good Article, but Also Publish It
Writing an article, what truly takes time is often not just writing. Preparing materials, designing a cover, converting formatting, adjusting titles, logging into different websites, copying and pasting, and then recording the publishing results of each platform—when added together, these tasks make up the complete content production process.
AceData Studio puts these steps into the same creation entry point: use models to generate content, use connectors to access your own platform accounts, use Skills to save reusable methods, and then use scheduled tasks to execute according to plan.
This article takes Zhihu, CSDN, Juejin, Medium, Hashnode, Blogger, and X as examples to introduce how to move from a single article creation to a continuously running content workflow. The illustrations in this article are AI-generated functional diagrams, not console screenshots; platform icons are used to identify publishing channels.
¶ I. Connectors: Let Articles Move from Conversations to Various Platforms
Figure 1: Organize content in one creation entry point, then deliver it to the corresponding platforms through connectors.
Connectors can be understood as operational channels between Studio and external accounts. After account authorization is completed, AI can not only provide a piece of text in a conversation, but can also call the capabilities of the corresponding platform, save drafts, submit articles, and return execution results.[1]
Before use, open the connector management page and connect your own accounts according to the page instructions. Different channels use different authorization methods: some authorize login sessions through the ACE browser extension, while some use official OAuth. After authorization is completed, you still need to explicitly specify in the specific creation task which account, which channel, and whether to save as a draft or publish publicly.
¶ The Same Topic, Different Platform Expressions
| Platform | Content That Can Be Organized Around It | What to Explain Clearly During Creation |
|---|---|---|
| Zhihu | Long-form articles, question analysis, answers | What question it addresses, and what the conclusion is based on |
| CSDN | Technical tutorials, API calls, troubleshooting | Code, steps, environment description, and tags |
| Juejin | Development practices, project retrospectives | Technical process, categories, and tags |
| Medium | English tutorials, product cases | English expression, narrative structure, and text-image layout |
| Hashnode | Developer technical blogs | Markdown, cover image, tags, and target blog |
| Blogger | Blog articles and subsequent maintenance | Target blog, body formatting, and update target |
| X | Article summaries, opinion threads | Core conclusions and links to already published articles |
The table above provides content organization suggestions, rather than requiring all platforms to accept the same format. The connector documentation for Zhihu, CSDN, Juejin, Medium, and Hashnode respectively introduces article-writing capabilities; the current Blogger Skill provides article creation, drafts, publishing, and updating operations. X is more suitable for summary distribution, rather than directly receiving complete blog body text.[3–7]
For example, you can directly say to Studio:
Write a technical tutorial around “how to integrate AI-generated images into a product,” first generating a Chinese master draft and one cover image. Then separately organize versions suitable for CSDN, Zhihu, and Juejin. Give me a preview first, do not publish; after I confirm, call the corresponding connectors.
The key here is not “copying one piece of text seven times,” but sharing facts, code, and materials, while organizing the expression for each platform separately.
¶ II. After the Article Is Complete, Save the Creation Method as a Skill
Figure 2: Save verified creation methods so the next task can reuse the same set of requirements.
After an article is complete, the most worthwhile thing to retain is not only the body text, but also the method you have already refined: who the article is written for, what structure it uses, what images it needs, how it is rewritten for different platforms, and what information is returned after publishing.
A Skill is the way to save this set of execution instructions. It does not create interfaces for a certain platform out of thin air, nor does it retrain a model; instead, it allows AI to reuse clear goals, rules, and steps in the next task.
¶ 1. First, Ask Studio to Summarize This Process
After completing an article preview or first publication, you can continue by entering:
Please organize the article structure, tone, image specifications, platform rewriting methods, and publishing steps that have been confirmed this time into a reusable Skill. Set the topic, material sources, target platforms, output language, and publishing mode as input variables, generate drafts only by default, and finally return a results table for each platform.
¶ 2. Save It in Skill Management
Open the Studio Skill management page. The current interface supports directly writing Markdown, and also supports uploading Markdown files or skill packages. You can save the organized execution instructions as your own Skill, and then use it in subsequent conversations or tasks.[8]
A content Skill should at least clearly specify the following items:
| Item | Example |
|---|---|
| Inputs | This issue’s topic, reliable materials, target platforms, language, publishing mode |
| Article standards | First explain the problem, then provide operation steps, and finally show verifiable results |
| Image standards | Landscape cover image, unified visual style, and short labels in flowcharts |
| Platform adaptation | Chinese tutorial, Zhihu analysis version, English blog version, X summary |
| Execution order | Research materials → master draft → images → rewriting → confirmation → submission |
| Completion criteria | Return article title, platform, actual status, link, and pending items |
What is saved here is “what should be done each time,” rather than treating the body text of the previous article as a fixed answer. The next time you change the topic and materials, you can still reuse the same creation standards.
¶ 3. Then Add an Automation Task
Open Studio Scheduled Tasks, create a task, and fill in the name, model, execution cycle, time zone, and prompt. The current page provides schedule configurations such as daily, weekly, interval, or Cron, and can select the Skills and MCPs that the task is allowed to use.[2][9]
A weekly content task can be organized like this:
Task Name: Weekly Developer Tutorial
Execution Schedule: Every Monday at 09:00, Asia/Shanghai
Call my saved content creation Skill, based on the verified product materials from this week:
1. Select a tutorial topic that has not yet been covered;
2. Generate a Chinese master draft, one landscape cover image, and one flowchart;
3. Prepare three versions for CSDN, Zhihu, and Juejin;
4. Execute according to the publishing mode approved for this task;
5. Return the title, platform, actual status, link, and items requiring manual handling.
On the first run, only save drafts.
When configuring, in addition to selecting the Skill, you also need to add capabilities that need to perform write operations to the unattended allowlist; the image capabilities required for illustrations also need to be included in the task configuration. Whether scheduled invocation can complete publishing also depends on whether the corresponding Skill supports unattended execution and the authorization obtained by the task. After saving, first click “Run Now,” check the complete output, and then enable scheduled execution.[2]
“Continuously updating platform content” can have two meanings. One is publishing new articles on a schedule to keep the account updated; the other is modifying an already published article. The latter must use a channel that supports editing and specify an existing article ID or link. For example, the current Blogger Skill includes an article update operation; this capability cannot be directly applied to all platforms.
If you plan to maintain old articles, you can explicitly write the task as: “Read this article and the latest materials, generate a summary of modifications; only submit when the target channel supports updates and authorization has been obtained, otherwise output a draft to be updated.”
¶ III. From Requirements to Publishing: How a Complete Creation Proceeds
Figure 3: Requirements, materials, generation, adaptation, submission, and verification constitute a complete content delivery.
¶ Step One: Explain the Topic, Audience, and Delivery Format
Do not just say “Help me write an article.” A more effective starting point is to explain the topic, readers, purpose, and output requirements at the same time:
Write an introductory AI image API tutorial for junior developers, about 1,500 words. Readers need to understand the invocation steps, and the article includes environment preparation, request examples, and result explanations. First provide three titles, then generate the main text, cover image, and flowchart, prepared for publication on CSDN and Zhihu.
In this way, AI knows from the beginning that what it needs to deliver is a “publishable illustrated tutorial,” rather than a general introduction.
¶ Step Two: Provide Materials and Establish a Factual Foundation
Give official documentation, product pages, confirmed feature descriptions, or your own materials to Studio. When there are many materials, you can first ask it to organize an “article outline + reference list,” and then begin writing.
For code tutorials, the real runtime environment and inputs and outputs should be stated; for case-study articles, completed practices and proposed solutions should be distinguished. Untested data should not be written as test conclusions, and experiences that have not been provided should not be written as first-person hands-on tests.
The deliverable at this stage is a clear outline and the materials on which the main text will be based, rather than immediately publishing externally.
¶ Step Three: Generate the Master Draft, Cover Image, and Body Illustrations
After determining the title and outline, have the text model generate the master draft, then call the authorized image model to create the cover image, architecture diagram, or operational flowchart. Image generation is usually an asynchronous task, and the final image results need to be obtained before embedding them in the main text.
You can make the visual requirements specific:
The cover image should be landscape, in a dark blue and cyan-purple technological style, with a concise title. The body flowchart contains six steps, with each step expressed using a short label; no unverified numbers or product interfaces should appear in the image.
What is completed at this stage is a reusable set of materials: main text, summary, images, and image descriptions. Different platforms should share facts and core materials as much as possible, reducing repeated work.
¶ Step Four: Adjust Titles, Formatting, and Tags by Platform
Generate platform versions separately based on the master draft, rather than fabricating content again. The CSDN version highlights code and troubleshooting steps, the Zhihu version supplements background and explanations, the English blog version reorganizes the language, and the X version retains only key viewpoints and the article entry point.
Platform submission fields should also be specified: Juejin public submissions require valid categories and tags; Blogger body text uses HTML; Medium converts Markdown into platform content structure. Whether images are transferred and how the cover image is used should also be handled according to the corresponding connector.[3–6]
After this stage ends, it is best to form a preview checklist: platform, title, summary, body version, cover image, category, and tags. You can check all channels at once, without having to supplement information only at the time of submission.
¶ Step Five: Confirm the Versions, Then Execute Submission
In normal conversations, first review the main text and illustrations, confirm the target account and the mode of “draft” or “public publishing,” and then have Studio execute the operation. When publishing in batches, the included platforms should be specified clearly, and one confirmation should not be extended to other accounts or channels.
Scheduled tasks use the preconfigured execution scope and unattended authorization. Content requiring manual review first enters the draft stage; only approved content is submitted by the publishing task within the authorized scope.
¶ Step Six: Check Results and Save Publishing Records
After publishing, ask Studio to return results by platform, rather than only replying “Completed”:
Article Title | Target Platform | Actual Status | Article or Draft Link | Items to Be Handled
Actual status should distinguish between draft, submitted, under review, and publicly available. The official task documentation explicitly reminds that task completion does not mean the article has been made public, and the actual results on the target platform need to be checked.[2]
If content needs to be maintained long-term, also record the master draft version and platform article ID. When revising tutorials, updating screenshots, or adding an English version later, you will be able to find the corresponding content without having to locate it from scratch.
¶ IV. Multi-Model Collaboration Reduces Large Amounts of Repetitive Manual Work
Figure 4: Delegate repeatable production work to models, leaving human time for judgment, creativity, and final review.
The value of Studio is not merely “writing faster,” but being able to choose different models according to the task, and complete an entire workflow in combination with connected image and platform capabilities. Whether a model is available is subject to the current model list and account authorization.[10]
Within the same workflow, responsibilities can be assigned as needed:
- **Text models**: organize outlines, write body content, generate summaries and title candidates.
- **Reasoning models**: help check argument structures, compare options, and sort out relationships between materials; facts should still be verified against sources.
- **Image models**: generate covers, content illustrations, and explanatory diagrams.
- **Models with multilingual capabilities**: translate, rewrite, and adjust expression for different platforms.
- **Connectors**: perform draft creation, publishing, and result retrieval operations for the corresponding accounts.
These are ways of assigning responsibilities according to tasks; they do not mean that every article must invoke multiple different models, nor do they mean that the system will necessarily automatically select the optimal model. Simple tasks can be completed with one text model, while more suitable capabilities can be added for complex tasks.
### What is saved is more than just writing time
In a traditional workflow, one person needs to repeatedly switch between the roles of “writer, illustration creator, editing and layout designer, and platform operator.” With a unified master draft, reusable Skills, and scheduled tasks, AI can handle most standardized production steps, reducing the manual effort caused by repeated writing, illustration creation, translation, layout, and transferring content.
For individuals or small teams that maintain multiple channels over the long term, **this has the potential to save substantial manual time and corresponding labor costs**. The actual extent depends on content complexity, review requirements, and publishing frequency, and cannot be replaced by a uniform ratio.
You can use a simple measure to observe the benefits:
> Investment per piece of content = model and tool invocation costs + time for manual review and revision.
If the same person can spend more time on topic selection, product understanding, and real cases, instead of repeatedly copying and pasting, automation is improving content production efficiency. The time saved can also be invested in more in-depth articles and better reader feedback.
## V. Open Studio and Start Your First Creation
Now open **[AceData Studio](https://studio.acedata.cloud/)**, enter the conversation, and describe the content you want to write; complete authorization for the target platforms in [Connector Management](https://studio.acedata.cloud/console/connectors), then start with one article.
You can copy this instruction directly:
```text
I want to create an explanatory blog for developers.
Topic: [Fill in the topic]
Readers: [Fill in the readers]
Reference materials: [Provide links or materials]
Target platforms: [Choose your publishing channels]
Please complete the following in order:
1. First give me three titles and an article outline;
2. After the title is confirmed, generate the complete body text;
3. Generate one cover image and one explanatory flowchart;
4. Prepare corresponding versions for the target platforms;
5. Give me a preview first, and publish only after confirmation;
6. Return the actual status and links for each platform;
7. Organize the confirmed creation workflow from this session into a reusable Skill,
and provide configuration suggestions for the corresponding scheduled task.
After the first piece of content is completed, save the workflow in Skill Management, then go to Scheduled Tasks to configure the schedule, models, and allowed capabilities. Run it once immediately first, then let the confirmed method enter your daily creation process.
Open AceData Studio and Start Creating →
¶ Reference Materials and Feature Entry Points
- Connector Overview
- Scheduled Task Content Workflow
- Zhihu Connector, CSDN Connector
- Juejin Connector
- Medium Connector
- Hashnode Connector, Blogger API Post Resources
- X Connector
- Studio Skill Management
- Studio Scheduled Tasks
- AI Chat v2 Capabilities and Unattended Authorization Instructions
The four in-body illustrations and accompanying posters in this article were generated using GPT Image 2. The example prompts are used to explain configuration methods; no automated tasks were actually created in this article.