Write Android Automation Scripts with AI Agents: Cursor/Trae + CLI Tutorial (Natural-Language Coding)

1. Why Automation Scripts Are a Perfect Fit for AI Programming

A few years ago, writing Android automation scripts was a craftsman’s job: you had to understand the control tree, color-and-image recognition, OCR, and how to call dozens of APIs. AI programming has since lowered the bar dramatically—but “AI programming” feels very different from one scenario to the next. In some scenarios the AI only helps you autocomplete a few lines; in others it can complete an entire pipeline on its own. Android automation scripts belong to the latter category. Why? Three reasons: structured documentation, a CLI-closable loop, and verifiable APIs.

Reason	What it means for AI programming
Structured documentation	API docs are clear and complete; the AI can read them and generate code with a factual basis
CLI-closable loop	Build/run/screenshot/node-capture/OCR all have commands; the AI can “see” results itself
Verifiable APIs	Every step’s output can be checked, so the AI is far less likely to confidently hallucinate

First, structured documentation. All of EasyClick’s APIs are documented: the js files under the project’s libs directory carry function comments in their headers (base functions in basic.js, image processing in image.js, OCR also in image.js, node-related APIs in nodeimage, screen-capture in clorimage), and each function’s purpose, parameters, and return values are spelled out. Large language models excel at digesting exactly this kind of structured manual.

Next, the CLI-closable loop. This is the crucial part: every step of the development chain—compile, preview, run, stop, screenshot, node capture, OCR recognition, log inspection—has a corresponding command-line tool (ec-android-cli). The AI can not only “write code,” it can execute commands itself and collect the results itself. Write the script → compile → run it → look at the screen → read the logs: the AI participates in the whole loop, with no human shuttling intermediate artifacts around.

Finally, verifiable APIs. The biggest weakness of large models is fabricating APIs that do not exist. Once you feed the AI real documentation, it generates code against the docs, and when compilation or runtime fails, it can fix things against the docs too. Verifiable results are the prerequisite for trustworthy AI programming.

The takeaway in one sentence: Android automation is an ideal scenario for AI programming—the docs are complete, the tooling closes the loop, and the results can be verified. The remaining question is just “how to feed the project and docs to the AI,” which is exactly what this guide walks through. We use EasyClick for Android (root-free, official docs) as the running example. Scripts are written in JavaScript—a little JS is enough to get started, and all Java class libraries can be called directly, so deep customization is no problem either.

2. Environment Setup: Create the Project in IDEA, Open the “Project Folder” in Cursor/Trae

Set up the environment before writing any code. Three steps:

1. Install the plugin in IDEA and create a project. Install the EasyClick Android development plugin (the current latest EasyClick for Android version is 12.4; IDEA 2026.2 and above is recommended—install the plugin and you are ready to go, no IDEA activation needed). Open an empty folder in IDEA as your workspace, right-click to create a new module, choose “EasyClick Android - Script Project,” and give it a module name (Chinese or English both work; avoid special characters and spaces).
2. Connect a device over USB. Enable USB debugging on the phone, then in IDEA go to the menu “EasyClick Android - Device Connection - Select USB Connection.” The tool installs the runtime environment APK automatically during connection (first project tutorial).
3. Open the project in Cursor/Trae. When opening a folder, select the project folder, not the module—pick the wrong level and the AI cannot see the full project structure.

Here is a pitfall beginners often hit: if you do not see the ec_work_config/android/bin directory in the project, the plugin has not generated the CLI yet—close IDEA and reopen the project folder. This directory holds the EC Android build-related CLI and SKILL.md, and it is the foundation of the whole “AI writes scripts” approach.

3. Feeding Knowledge: Teaching the AI About EasyClick

Once the environment is ready, the first task is not writing code but letting the AI know what tools it has. The official tutorial recommends three things, in order:

1. Have the AI scan the project structure and SKILL.md The SKILL.md in ec_work_config/android/bin is a “CLI manual” written specifically for AI: it describes the purpose, parameters, and examples of every subcommand, and includes an EC script & API quick reference. Talk to the AI and have it scan the project structure and SKILL.md—then it knows “this project can be compiled, run, screenshotted, and node-dumped.”

2. One-click install all docs with AI DOCS (v12.1.0+) If you find “letting the AI browse the docs itself” too slow, EasyClick for Android v12.1.0+ adds an “Install AI DOCS” option in the IDEA top menu—one click installs all documentation into the project, so the AI can search the full API docs locally instead of relying on training memory. Note: what gets installed is the documentation; prompt files for different AI platforms must be generated by you, as the official docs explain.

3. Add a documentation link via @Docs and let the AI fetch it Type @ in the AI chat box, choose Docs → Add new doc, and paste the EasyClick documentation URL (https://ieasyclick.com/docs/) so the AI fetches and understands it itself. The advantage of this approach is that the docs are always current—when the official docs are updated, the AI fetches the newest version.

4. The CLI Closed Loop: Turning “What It Can Do” into the AI’s “Input”

The key to AI programming is: the AI must be able to “see” the runtime results before it can correct itself. ec-android-cli turns the entire development chain into commands, and each command’s output becomes the input for the AI’s next decision:

CLI subcommand	Purpose	Input it gives the AI
build	Build the IEC script package	Compilation logs (success/failure)
preview	Preview the project	UI preview logs
run	Run the project	Runtime logs, error messages
stop	Stop the current run	Stop-result logs
capture-screen	Capture a screenshot from the device connected to IDEA	The screen image
capture-node	Dump UI nodes (UIX, essentially XML)	The control-tree structure
ocr-local-image	OCR a local image	Text found in the image
ocr-screen	OCR the current screen	Text found on screen
test-image	Image template-matching test (local image/screen capture)	Recognition test results
monitor	Stream logs continuously (no -m needed)	Real-time log stream

A few usage points (all per the official SKILL.md documentation):

• All subcommands except monitor require the -m module name; for multiple windows/projects, pass the -p project root to match the correct instance.
• Logs default to JSON format for easy parsing by the AI; use -o to append logs to a file, or -r to auto-generate a log file name (-r and -o cannot be used together).
• build / run / preview have built-in default “stop keywords” (e.g., compilation success, script finished running); you can also set custom keywords with -k, and monitoring ends when they appear in the log—the AI gets precise log snippets at key points instead of a wall of output.
• preview and run require a device connected to IDEA, otherwise you get a “no device connected” message.

In one sentence: the CLI’s job is to “tell the AI what happened”; the AI then analyzes the information and decides “what to do next.”

5. A Hands-On Demo: From a Plain-Language Request to a Running Script

Putting the whole chain together, a complete AI development loop looks like this:

Step 1: Describe the requirement in plain language. No code needed—just say it: “Write a script: launch the app, find the ‘Recommended’ button and click it, then swipe to the bottom of the page.”

Step 2: The AI generates the script. Combining SKILL.md and the API docs, the AI produces JavaScript, for example (selector & node documentation):

// Find and click the "Recommended" node; log a message if not found
let node = text("Recommended").getOneNodeInfo(0);
if (node) {
    node.click();
} else {
    loge("Not found");
}

Step 3: Build the IEC. Ask the AI to “build the testai project,” and it runs ./ec_work_config/android/bin/ec-android-cli build -m testai. A successful build produces the IEC script package; on failure, hand the compilation log back to the AI and let it fix things until it passes.

Step 4: Preview / run. With the device connected, ask the AI to “preview testai” or “run testai,” and the script starts running on the real device. Real-device preview has a nice side effect: the screen is mirrored to the IDE in real time, so both you and the AI can see exactly what the script is doing on screen.

Step 5: The AI reads the logs and fixes bugs. When an error occurs at runtime—say a card-key verification failure or a node not found—just hand the log to the AI: “We hit a card-key issue just now; fix it and keep running.” The AI locates the cause against the logs, modifies the code, recompiles, and reruns—until it works.

The official tutorial also links a full video case: “EasyClick + Trae AI writes scripts fully automatically! No more hand-typing code” (Bilibili). It gives you a feel for the whole “conversational development” rhythm: you describe the requirement, and the AI writes the script, compiles it, runs it, and fixes problems on its own—almost no hand-typed code throughout.

6. Capability Boundaries and Caveats

The AI is powerful, but this is not “self-driving.” A few boundaries to keep in mind:

• The AI needs human confirmation. Review generated code, especially irreversible operations like text input, payments, and card-key verification—confirm them manually before they run.
• The real device is the source of truth. Compilation, preview, run, screenshots, and OCR all close the loop on the real device (or the device connected to IDEA). The AI saying “it should be fine” does not count—a full run does.
• The CLI has prerequisites. ec-android-cli requires IDEA to be running with the EasyClick development plugin loaded and responsive; the module name on the command line must match the module name in IDEA. When these conditions are not met, commands simply fail—that is an environment issue, not an AI issue.
• AI DOCS installs documentation, not prompts. Prompt/configuration files for different AI platforms must be generated by you, as the official docs explain.
• The CLI only reports “what happened.” It gives the AI facts (logs, screenshots, nodes, OCR results); what to change and how is analyzed by the AI and decided by the human.

7. FAQ

Q1: Why are automation scripts such a great fit for AI programming? A: Because the entire development loop can be tool-ified: the API documentation is structured, every step has a CLI command behind it, and results can be verified at any time. The AI can both write code and see the screen, read logs, and close the loop on its own.

Q2: Do I have to use Cursor? Can I use Trae? A: Either works. All major LLMs are supported, not just Cursor—configuration is similar; Cursor and Trae are simply two examples from the official tutorial.

Q3: What is ec_work_config/android/bin in the project? A: It stores the EC Android build-related CLI and SKILL.md. SKILL.md is a CLI manual written for AI to read, covering every subcommand, parameter, and example.

Q4: How do I teach the AI the EasyClick APIs? A: Three ways: have the AI scan the project structure and SKILL.md; use “Install AI DOCS” (v12.1.0+) to install all the docs in one click; or add the official documentation link via @Docs so the AI fetches it itself.

Q5: What subcommands does the CLI have? A: Ten: build, preview, run, stop, capture-screen, capture-node, ocr-local-image, ocr-screen, test-image, and monitor. All but monitor require the -m module name.

Q6: Under what conditions can the CLI commands be used? A: IDEA must be running with the EasyClick plugin loaded; the module name in the command must match the one in IDEA; for multiple windows/projects, pass the project root with -p. preview and run also need a connected device.

Q7: Do AI-generated scripts need manual review? A: Yes. Review the code; irreversible operations such as input, payments, and card-key verification must be confirmed by a human; finally, run the whole thing on a real device to verify.

Q8: What language are the scripts written in? A: JavaScript, and all Java class libraries can be called directly. A little JS is enough to get started; a little Java enables deep customization. AI-generated code can be compiled and run directly.

Q9: Does OCR text recognition cost anything? A: No. The PPOCR-V4/V5/V6 model families are all free and run locally and offline, with no dependency on cloud interfaces.

Q10: Which Android versions are supported? A: EasyClick for Android supports Android 5.0 through the latest system versions, so older devices work as well.

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About EasyClick: An AI-agent platform for mobile automation covering three ecosystems—root-free Android, jailbreak-free iOS, and HarmonyOS Next—offering script development, iPhone cluster control, local central-control screen mirroring, and a cloud control system. → Explore all products