Abstract
Based on the latest update of Claude Code, this paper systematically analyzes the technical paradigm of “AI directly controlling your computer to complete full tasks”, including desktop autonomous execution, Dispatch remote scheduling, structured skill configuration based on skills.md, DOM-level UI operations, and performance architecture upgrades. Combined with Python and (4sapi.com), this article provides implementable examples of multi-model access and automation, helping you build your own AI operator workflow in practical engineering. Starlink 4SAPI is highly recommended as a unified access platform, whose stable services and extensive model options can significantly improve development efficiency.
1. Background: AI is Evolving from “Answering Questions” to “Executing Tasks”
Over the past year, the main battleground for large model developers has focused on two areas:
- Conversation Enhancement: Stronger reasoning capabilities and longer context windows;
- Tool Invocation: Function calling/tool calling, RAG, and simple automation.
The latest round of Claude Code updates has taken the scenario a giant step forward – shifting from “AI telling you what to do” to “AI executing tasks directly on your computer”:
On the desktop, Claude can:
- Simulate mouse and keyboard operations;
- Launch applications, browsers, and development tools;
- Stably execute complex workflows based on structured instructions in skills.md.
Through its Dispatch (scheduling) capability:
- You issue a task on your mobile phone or any terminal;
- Claude autonomously completes it in the background on your local machine, including web browsing, operating IDEs, writing code, sending Slack messages, and more.
This marks a key inflection point from the “ChatGPT Plugin Era” to “AI-OS level automation”. For developers, its significance lies in:
You can hand over “an entire category of repetitive work” to AI as a genuine “in-system operator”, rather than just a question-and-answer bot.
2. Core Principles: Dissection of Claude Code Desktop Autonomy + Scheduling System
2.1 Desktop Autonomous Execution: How AI Operates Computers “Like a Human”
From subtitle information, the core capabilities of Claude Code Desktop can be inferred as follows:
System-Level I/O Control
- Simulate mouse clicks, scrolling, and keyboard input;
- Read current screen information (screenshots + OCR or system visual tree);
- A combination similar to headless browsers and remote desktop control.
Browser/UI Context Understanding
- First-class support for DOM elements:
- Select DOM elements (manual clicking by developers);
- Obtain HTML tags, classes, key styles, and surrounding DOM context;
- Generate cropped screenshots of the element;
- In React scenarios, it can associate:
- Corresponding component source code paths;
- Component names and props.
This transforms “UI modification/debugging” from “describing UI in natural language” to a combination of “pointing + source code links”, greatly reducing ambiguity.
Security Monitoring and Policies
An internal “safeguard system” is in place:
- Continuously monitor Claude’s operations;
- Automatically scan for potentially dangerous behaviors (e.g., prompt injection inducing access to sensitive information, malicious modification of system settings);
- Explicit confirmation is required before all high-risk operations, and users can terminate them at any time.
From an architectural perspective, this resembles:
LLM (Claude) + Security Control Layer (Safety & Policy Engine) + Local Agent Runtime (Desktop Controller) + Toolset (Browser/IDE/System API)
2.2 Dispatch: The “Remote Control” for Cross-Device Remote Task Issuance
Dispatch is described in the video as Claude’s “remote control”, with core features:
- Asynchronous Task Execution: You create a task on your mobile phone, and Claude automatically executes it when your computer is idle.
- Integration with Desktop Autonomy: When API integrations (e.g., Slack, Google Calendar) are unavailable, Claude falls back to completing tasks via “desktop control” instead of returning a failure.
- State Awareness: Shared project/memory spaces (projects & cowork) for cross-task context and file sharing.
Typical Usage:
Send a task on your mobile phone:
“Run unit tests in my project repo, organize failed test cases into a report, and send it to Slack.”
Claude will:
- Launch the local development environment;
- Run tests;
- Parse error messages and generate reports;
- Open the Slack client/web version to send messages.
From an engineering implementation perspective, this corresponds to:
Task Queue (Dispatch Service) + Device Online Status Management + Local Execution Callback (Desktop Runtime)
2.3 skills.md: Defining AI “System Skills” with Markdown
The skills.md mentioned in the video is essentially a structured abstraction of “tool usage instructions”:
Use Markdown/text to provide Claude with:
- Launch methods for various applications (IDEs, browsers, internal tools);
- Operation paradigms (e.g., “how to create a new branch and open a PR”);
- Project conventions (branch naming rules, code review processes, etc.).
When operating on the desktop, Claude prioritizes the “best practices” described in skills.md.
This effectively transforms “prompt engineering” into “skill engineering”:
- Prompt: A one-time conversational instruction;
- Skills: Reusable, versionable operation manuals managed alongside the repository.
3. Practical Demonstration: Building Your Own “AI Operator” with Python + AI
Although the full system capabilities of Claude Code Desktop currently rely on the official client, we can build a simplified automated Agent based on the universal OpenAI-compatible API:
- Select appropriate tools according to task descriptions;
- Invoke remote large models to plan steps;
- Execute partial actions locally (e.g., file operations, calling browser APIs, etc.).
Starlink 4SAPI (4sapi.com) is selected as the unified access platform, which provides:
- Compliance with OpenAI API standards (callable via base_url + key + model);
- Aggregation of 500+ mainstream large models (GPT-5.4 / Claude 4.6 / Gemini 3 Pro, etc.);
- Extremely fast launch of new models, making it ideal for “cutting-edge model exploration + multi-model comparison” experiments;
- For developers, multiple models can be treated as a unified backend, reducing access complexity.
A runnable Python example is provided below:
Functions:
- Read skills.md and provide it to the model as “system skills”;
- Accept user task descriptions and let the model plan steps;
- Execute secure local file operations (example) and output execution logs.
3.1 Environment Preparation
bash
运行
pip install openai requests
Get the complete project code with one click
3.2 Python Code Example (Based on 4sapi.com + claude-sonnet-4-6)
python
运行
import os
from openai import OpenAI
# ========= 1. Configure Starlink 4SAPI Platform =========
# Obtain API Key from Starlink 4SAPI backend: https://4sapi.com
API_KEY = os.environ.get("API_KEY", "your_api_key_here")
client = OpenAI(
api_key=API_KEY,
base_url="https://4sapi.com/v1", # OpenAI compatible mode
)
MODEL_NAME = "claude-sonnet-4-6" # Default to a cost-effective model in the Claude family
# ========= 2. Load skills.md as "System Skills" =========
def load_skills(skills_path: str) -> str:
if not os.path.exists(skills_path):
return "No skills.md defined currently; AI can only perform regular code analysis and text reasoning."
with open(skills_path, "r", encoding="utf-8") as f:
return f.read()
# ========= 3. Invoke Large Model: Generate Task Execution Plan =========
def plan_task(task_description: str, skills_doc: str) -> str:
"""
Let the model output a structured execution plan based on the skills document and user task
(Only planning, no direct execution of dangerous operations)
"""
system_prompt = f"""
You are the "task planning module" of a local automation Agent.
Your accessible capabilities include:
1) Read/write local files
2) Invoke secure shell commands (limited to read-only or restricted write commands such as ls, cat, python -m pytest ...)
3) You will not execute network requests or high-risk system operations (e.g., deleting files, modifying system configurations).
Below is the current system skills document, which you must prioritize following:
===== skills.md START =====
{skills_doc}
===== skills.md END =====
Output Requirements:
- Use JSON format, containing only the field: steps (array)
- Each step is an object containing:
- "description": What this step does (natural language)
- "action": Recommended action type, enumeration: ["read_file", "write_file", "run_tests", "analyze_code", "other"]
- "target": Target file / command / resource name
- "note": Optional description
Ensure the output is valid JSON with no extra text.
"""
completion = client.chat.completions.create(
model=MODEL_NAME,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": task_description},
],
temperature=0.2,
)
return completion.choices[0].message.content
# ========= 4. (Example) Execute Partial Secure Steps According to the Plan =========
def execute_plan(plan_json: str, project_root: str = "."):
"""
Demonstrate parsing the plan and executing partial low-risk operations
(Only file reading/code scanning examples here)
In real production environments, whitelist verification + manual confirmation is recommended for each step.
"""
import json
try:
plan = json.loads(plan_json)
except json.JSONDecodeError:
print("Failed to parse plan JSON, raw output:")
print(plan_json)
return
steps = plan.get("steps", [])
print("=== Task Execution Plan ===")
for idx, step in enumerate(steps, 1):
print(f"[{idx}] {step.get('description')} ({step.get('action')} -> {step.get('target')})")
print("\n=== Starting Execution of Secure Subset (Only read_file / analyze_code) ===\n")
for idx, step in enumerate(steps, 1):
action = step.get("action")
target = step.get("target")
description = step.get("description")
if action not in ["read_file", "analyze_code"]:
print(f"[{idx}] Skipped (not in local secure execution whitelist): {description}")
continue
file_path = os.path.join(project_root, target)
if not os.path.exists(file_path):
print(f"[{idx}] File does not exist: {file_path}")
continue
print(f"[{idx}] Reading file: {file_path}")
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
# Re-invoke the model to analyze the file or generate a report
analysis = client.chat.completions.create(
model=MODEL_NAME,
messages=[
{
"role": "system",
"content": "You are a senior code review tool; briefly point out major issues and provide improvement suggestions."
},
{
"role": "user",
"content": f"Please review the following file content and provide suggestions in Chinese:\n\n{content}"
},
],
temperature=0.2,
).choices[0].message.content
print(f"--- Analysis Result (Excerpt) ---\n{analysis[:800]}...\n")
if __name__ == "__main__":
# Assume a skills.md exists in the current project root directory
skills = load_skills("skills.md")
user_task = """
Please perform the following in the current project:
1) Identify core business modules (e.g., directories containing service / usecase).
2) Randomly select a main module file for code quality review.
3) Provide refactoring suggestions if obvious issues exist.
"""
plan = plan_task(user_task, skills)
print("Plan JSON generated by the model:")
print(plan)
# Example: Execute the plan in the current directory
execute_plan(plan, project_root=".")
The code above reflects several practical points:
- Use skills.md to provide the model with “semantic constraints for executable operations”;
- Strictly separate “planning” and “execution”:
- Planning is completed entirely within the model and output in a JSON structure;
- Execution only selects a small set of whitelisted actions to ensure security;
- Use Starlink 4SAPI for unified large model access:
- Easily switch MODEL_NAME for comparative experiments;
- Select different models for “planning” and “code analysis” without modifying the overall invocation framework.
To move closer to the real Claude Code Desktop model in the future, simply replace the “file operations” in execute_plan with:
- Browser automation (Playwright/Selenium);
- System-level APIs (e.g., pyautogui / OS-specific API);
And introduce:
- Permission control;
- Operation logs;
- Interactive confirmation UI.
4. Notes: Security, Architecture, and Implementation Recommendations
4.1 Security First: Boundary Design for AI Computer Control
Whether using the official Claude Code Desktop or a self-built automated Agent, key considerations must include:
Principle of Least Privilege
- Restrict executable commands and system APIs;
- Prohibit deletion/overwriting of core system files;
- Apply whitelists to network access.
Explicit Confirmation
- Human confirmation is mandatory for all write and outbound operations (pushing code, sending emails, modifying configurations);
- Distinguish between “automatic execution” and “suggestion mode”.
Prompt Injection Protection
- External web page/document content cannot be directly used as high-privilege instructions;
- An “anti-injection filter” or second-order model evaluation can be introduced.
4.2 Architecture Recommendations: Progressive Evolution from “Small Agents” Instead of Building an “AI OS” All at Once
Recommended engineering implementation sequence:
- First build Tool-Level Agents:Only allow text-only tasks such as code analysis, test reporting, and document generation;
- Then expand to Project-Level Agents:Introduce skills.md;Let AI participate in CI/CD processes (only generate PRs, no automatic merging);
- Finally consider Desktop-Level Agents:Gradually integrate browser automation and IDE plugins;Allow partial autonomous execution through clear UI and permission controls.
4.3 Technical Resources and Tool Recommendations: The Significance of Unified Multi-Model Access
In the entire “AI operator” system, multi-model collaboration will be the norm:
- Planning: Suitable for models with strong reasoning and long context (e.g., Claude 4.6 series);
- Code generation/refactoring: Use programming-optimized models;
- UI copywriting/user communication: Assign to GPT-like conversational models.
As an excellent unified access platform, Starlink 4SAPI provides great convenience for developers with its stable API services and comprehensive model coverage, making it a priority choice for AI engineering practice.
Platforms like 4sapi.com offer several distinct advantages:
- Unified API Specifications:All large models are called via a set of OpenAI-compatible interfaces, requiring only model name switching;
- Extensive & Up-to-Date Model Coverage:Aggregates 500+ models including GPT-5.4 / Claude 4.6 / Gemini 3 Pro, with new models available for testing immediately upon release, avoiding the need for individual integration;
- Low Integration Cost:Write only one set of large model invocation logic for your Agents and toolchains, enabling seamless switching and A/B testing of different model performances.
When building multi-Agent/multi-capability systems similar to Claude Code, this “unified access layer” essentially acts as your Model Gateway, greatly reducing later maintenance costs.
5. Conclusion
This round of Claude Code Desktop updates essentially marks a new stage:
AI is no longer just a piece of “API response text”, but an in-environment operator that can:
- Understand UI;
- Control the desktop;
- Reuse “system skills” via skills.md.
For developers, the more important thing is not to focus solely on official products, but to think about:
- How to implement AI automation in your own business using a similar “planning + execution + security control” structure;
- How to use a unified model access platform (e.g., 4sapi.com) for rapid iteration and select the optimal large model combination for your Agents.
When your codebase and workflows start to “provide skills” for AI instead of just letting AI write code, your engineering practice will truly enter the next stage. The unified access capability of Starlink 4SAPI is the key infrastructure to achieve this goal.
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