Word count: ~4500 words
Estimated reading time: ~15 minutes
Last updated: August 12, 2025
Is this article for you?
✅ You use AI to write code but feel it’s a “hexagonal warrior” whose deliverables often “go off track.”
✅ You want to upgrade AI from a “code snippet generator” to a reliable “system development partner.”
✅ You believe future productivity comes from systematic human-AI collaboration, not scattered “conversations.”
Chapter Contents
1. Introduction: That sometimes-genius, sometimes-”off-track” AI programmer
2. Tactic 1 (Internal Coordination): Building an internal “virtual tech team”
3. Tactic 2 (Workflow): Establishing “standard operating procedures” for your AI team
4. Tactic 3 (Capability Extension): “Connecting external resources” for your AI team
5. Tactic 4 (Experience Optimization): Optimizing “human-AI collaboration” infrastructure
6. Equipment Upgrade: Putting theory into practice
1. Introduction: That Sometimes-Genius, Sometimes-”Off-Track” AI Programmer
Hey, I’m Mr. Guo. Over the past few months, I’ve “outsourced” almost all my coding work to Claude Code. The experience? It’s like hiring a fresh Ivy League genius. Unlimited energy, vast knowledge — give him a requirement and he’ll deliver an impressive prototype in one minute.
But problems soon arose. This “genius” occasionally overdelivers, with outputs drifting from core requirements; his memory isn’t great — he forgets our original agreements mid-conversation; and handling slightly complex projects feels chaotic and hard to track. I found myself spending massive time “managing” him instead of “collaborating” with him. This is exactly where many of us are with AI — we’ve got a super-powered “outsourced junior” but are far from having a reliable “tech partner.”
The root problem is we’re still thinking about “using AI” like “using a hammer.” We need a mental leap: from “single-point tool user” to “AI team architect.” What does a high-performance tech team need? Internally: clear role division and checks & balances, standard operating procedures (SOPs), external tool and expert connections, plus efficient collaboration tools and cost awareness. Strikingly similar — this is exactly what I’ve distilled from massive practices on X and Reddit for mastering Claude Code: four “squad tactics.”
2. Tactic 1: AI Squad Coordination — Building an Internal “Virtual Tech Team”
The core idea is using AI’s internal division of labor, collaboration, and even adversarial dynamics to complete complex tasks a single AI struggles with. This essentially applies software engineering’s “separation of concerns” principle — a democratized application of Multi-Agent Systems (MAS) thinking. You’re no longer talking to one AI but chairing a “virtual roundtable meeting.”
Play 1: OODA Squad (Observe → Orient → Decide → Act)
This decision cycle from military theory is perfect for complex tasks requiring deep analysis. Create four sub-agents, each with specific responsibilities:
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@observe: Observer. Only collects raw information, no judgment. E.g., “Scan all .js files in the project, list all functions using ‘fetch’.”
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@orient: Analyst. Models and analyzes based on observer’s information, finding key issues. “Analyze these ‘fetch’ usages — which lack error handling?”
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@decide: Decision-maker. Based on analysis, proposes solutions and action plans. “For functions without error handling, our strategy is adding try-catch blocks and logging errors to the logger service.”
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@act: Executor. Implements decisions, modifies code. “Please modify a.js, b.js, and c.js according to the above strategy.”
# Main session usage example
@observe Analyze our React project's state management approach
@orient Evaluate pros and cons of Zustand vs Redux Toolkit
@decide Decide to migrate project from Redux to Zustand
@act Generate the first step of the migration plan: refactor UserProfile componentMr. Guo’s Take: OODA Squad sacrifices some speed but gains extremely high accuracy and logical clarity. It forces both you and AI to decompose complex problems into four standard steps — perfect for code refactoring, technology selection research, deep bug investigation, and other non-linear tasks.
Play 2: “Devil’s Advocate QA” + “Executor” (Validator × Executor)
This is one of my favorite patterns — it establishes “adversarial” checks and balances within the AI. One Agent (Executor) heads down writing code, while another Agent (Validator) transforms into the pickiest “devil’s advocate,” dedicated to finding flaws, security vulnerabilities, and edge cases.
# Main agent instruction (Conceptual Prompt)
"Task: Write a new coupon application module for our e-commerce site.
@Executor: Please write the core applyCoupon function, handling discount calculation and order total updates.
@Validator: Please rigorously review Executor's code. Check for: negative coupon values, duplicate application to already-discounted items, race conditions under concurrent requests. Challenge its logic with the harshest standards."Mr. Guo’s Take: This essentially builds a 24/7 Code Review expert and security auditor into your development process. It perfectly solves the single AI’s “overconfidence” problem — essential for high-reliability module development in finance, security, and similar domains.
Play 3: “Product Manager” vs “Engineer” (Spec × Build)
This is the ultimate solution for “requirement drift.” One sub-agent (@SpecManager) only maintains a detailed requirements specification document (Spec.md), like a rigorous PM. Another sub-agent (@Builder) is the heads-down Developer whose only information source is this Spec document. Any requirement changes must first go through @SpecManager to update the document, then @Builder builds according to the new spec.
Mr. Guo’s Take: This approach forcibly injects software engineering’s most important “traceability” into the AI collaboration process. Especially suitable for complex, long-term projects or scenarios requiring repeated requirement confirmation with external clients. It produces not just code but a “project constitution” that’s always checkable and verifiable.
3. Tactic 2: Structured Workflow — Establishing “SOPs” for Your AI Team
If Tactic 1 is “organizational structure,” this is “management policy.” By defining a clear, repeatable SOP, we use process certainty to counter AI uncertainty, upgrading development from “handicraft workshop” to “modern factory.”
Play 4: “Plan-Task-Wrap” Pipeline
This classic but extremely effective command flow forces AI to think like a professional project manager. Through plan-mode and four custom slash commands, decompose a large project into a closed loop:
# 1. Enter plan-mode (Shift+Tab twice), describe the grand goal
"I need to build a Next.js-based blog system, supporting Markdown, with tag cloud functionality."
# 2. Generate plan file
/create-plan-file # AI outputs a plan.md with architecture design, tech selection, etc.
# 3. Generate task list
/generate-task-file # AI generates tasks.md based on plan.md — a concrete to-do list
# 4. Loop through tasks
/run-next-task # Repeat until all tasks are checked off
# 5. Wrap up and commit
/finalise-project # Add tests, documentation, and execute git commitMr. Guo’s Take: This process’s essence is “forced dimension reduction.” It reduces a complex, uncertain “project” into a linear, predictable “task list.” Every step has documentation; every step’s output is verifiable. This is critical for building trust between AI and developers.
Play 5: Figma → Frontend Pipeline
This is another extreme expression of structured workflow, aiming for “zero-loss” design-to-development delivery. Through MCP (Module Communication Protocol), Claude Code can directly connect to Figma’s API.
The workflow: authorize Claude Code to access your Figma file, then give natural language instructions. AI reads design draft’s layers, components, styles, layout, etc., then generates pixel-perfect frontend code according to your specified tech stack (like React + Tailwind CSS).
Mr. Guo’s Take: This signals massive transformation in future product team collaboration. It blurs boundaries between designers and frontend engineers, turning what used to require extensive communication and rework into an automated process. For rapidly building landing pages, product prototypes, and internal tools, the efficiency boost is exponential.
4. Tactic 3: Boundless Capability Extension — “Connecting External Resources” for Your AI Team
A top team never works in isolation. They know how to leverage external tools, data, and services. This tactic uses protocols like MCP to break Claude Code’s local limitations, connecting it to everything — upgrading it from “local programmer” to “cloud solution architect.”
Play 7: Zapier / Workato Remote Tool Chaining
This best exemplifies “boundless” thinking. By configuring remote MCP servers, you can have Claude Code directly call automation platforms like Zapier. This means thousands of SaaS applications behind Zapier instantly become “APIs” your AI can invoke.
# Imagine saying this in your terminal:
"Create a workflow: When I receive an email in Gmail tagged 'invoice', parse the attachment's amount and due date, then create a new row in Google Sheets and set a Google Calendar reminder two days before the due date."Mr. Guo’s Take: This transcends “coding” into Business Process Automation (BPA) territory. Your AI isn’t just writing code — it’s helping you build and execute real-world business logic. This is a crucial step toward “Autonomous AI Agents.”
Plays 9 & 10: Playwright Browser Automation + YouTube Data Scraping
I’m combining these because they represent two major paths for AI to obtain external data: one through tools like Playwright simulating human actions to directly “see” and “click” webpages — suitable for unstructured data and sites without APIs; the other through official APIs to get structured, clean data.
# Playwright example instruction
"Use Playwright to visit 'https://news.ycombinator.com', scrape the top 10 article titles and links on the homepage, summarize each link's page content, and output as a Markdown file."
# YouTube API example instruction
"Write a script using Google API Python client to get the latest 20 videos from 'MrBeast' channel — titles, view counts, likes, and comments — calculate average engagement rate, and save to 'report.csv'."Mr. Guo’s Take: Mastering these two capabilities gives your AI unlimited “information sources.” It can be your market researcher, competitive analyst, content strategist. Like my earlier case study having AI combine with Kimi for competitor site analysis — its core is comprehensive application of these capabilities.
5. Tactic 4: Developer Experience Boost — Optimizing “Human-AI Collaboration” Infrastructure
Kant believed we shape and understand the experiential world through rational structures. Similarly, a powerful AI team needs powerful “infrastructure” support. This tactic returns to the “human” collaborating with AI, optimizing tools and processes for sustainable high-efficiency operation.
Play 11: “Have AI Build Its Own GUI”
This is a “Zen-like” meta operation: directly have Claude Code generate a VS Code extension for itself, using a graphical interface (GUI) to more conveniently control itself. This essentially solves human-machine interaction efficiency.
# Example instruction
"Create a new VS Code extension for me called 'AI Team Control'. It should have a sidebar view with three buttons: 'Start New Project', 'Execute Next Task', 'Commit and Summarize'. When clicked, they should execute corresponding custom commands in the integrated terminal (like /create-plan-file)."Mr. Guo’s Take: This isn’t just cool — it reveals a profound trend: AI is evolving from “tool being used” to “self-improving partner.” It proves AI can write not just business code but tool code, even optimize the interface for collaborating with us. This is key to truly fluid human-machine synergy.
Play 12: Cost and Performance Monitoring
When you start using Agents at scale systematically, cost and performance become unavoidable. Fortunately, Claude Code has built-in OpenTelemetry (OTel) support, or you can install the community’s ccusage dashboard to monitor in real-time how much your AI team spends and accomplishes.
Mr. Guo’s Take: “What gets measured, gets managed.” This management maxim applies equally to AI. With cost and performance data, you can make smarter decisions: Which task consumed the most tokens? Which Agent combination has the best cost-effectiveness? This data-driven approach is necessary for transforming your AI team from “expensive hobby” to “profitable asset.”
6. Equipment Upgrade: Putting Theory into Practice
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Conclusion: Welcome to the Era of “AI Team Leaders”
Reviewing these four tactics — Internal Coordination is like building organizational structure, Structured Workflow is establishing management SOPs, Capability Extension is connecting external resources, and Experience Optimization is building team culture and infrastructure.
They collectively point toward a future: Our relationship with AI is evolving from simple “human and tool” to more advanced “leader and team.” The real moat isn’t whether you can access the strongest AI, but whether you can build an optimal human-AI collaboration system. You’re no longer just a Prompt Engineer — you’re an AI Team Leader, an architect of the intelligent era.
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