[Vibe Coding Guide] Architecting Intelligent Systems with Agents and Skills

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💡 What You Will Learn

Move beyond simple copy-pasting of prompts. Learn how to design an AI system that evolves and operates with professional-grade precision.

  1. The Essence of Agentic Systems: How AI maintains your intent and business context.
  2. Real-world Fintech Architecture: Folder structures and data pipelines used in the US_Market_Analyzer.
  3. Core Document Strategy: Detailed roles of documents within the context/ and skills/ layers.
  4. Intelligent Skill Design: Mastering ‘Imperative SOPs’ for high-fidelity stock analysis.
  5. The Self-Evolving Loop: Building agents that learn from market shifts and user feedback.

1. The Vision: Why ‘Vibe Coding’?

Many seek the ‘magic prompt,’ but the real power lies in the ‘System’ that executes it.

Vibe Coding is a modern methodology where developers, instead of typing every line of code, provide the AI with project Context, Rules, and Skills. This allows for the rapid development of complex software that remains maintainable and robust.

In demanding domains like Fintech, where data is massive and logic must be precise, this Agentic Design capability creates the true gap between average and elite development.

🏛️ System Architecture Overview

The flow of a system built with Vibe Coding is as follows:

graph TD
    User((User)) -->|Instruction| Orchestrator[AI Orchestrator]
    Orchestrator -->|Read Rules| AgentRules[.agent/ Rules]
    Orchestrator -->|Load Memory| Context[context/ Memory]
    Orchestrator -->|Execute| Skills[skills/ Functional]
    
    subgraph "Core Agentic Layers"
        AgentRules
        Context
        Skills
    end
    
    Skills -->|Analyze Data| Data[data/ Stock Data]
    Data -->|Python Engine| Utils[utils/ Analytics]
    Utils -->|Final Result| Web[web/ Dashboard]
    
    Web -->|Feedback| Learnings[context/learnings.md]
    Learnings -->|Evolve| Skills

2. Technical Implementation (6 Practical Steps)

Let’s dive into a real-world example: building a US Stock Market Analyzer.

Step 1: Environment Setup & Initializing Antigravity

Development starts with an empty folder, but you don’t have to start from scratch. Let Antigravity handle the foundation.

Create an empty folder (e.g., US_Market_Analyzer), open it in Antigravity (or Cursor), and enter the following prompt:

🚀 Initialization Prompt:
“I want to build a US Stock Market Analyzer using Vibe Coding. Set up the environment in this folder:
1. Create project folders for Next.js 14 (Frontend) and Python (Backend).
2. Create the core agentic layers: .agent (Rules), context (Memory), and skills (Functional).
3. Provide a script or guideline to install essential packages: pandas, yfinance, and google-generativeai.”

Step 2: Folder Structure Comparison (Basic vs. Fintech Extended)

The core of Vibe Coding is the Structure. Compare the foundational agentic layout with its professional fintech extension.

1) The Foundation: Generic Agentic Design

These are the base documents for any AI-orchestrated project.

my-vibe-project/
├── .agent/              # System Constitution (Identity)
├── context/             # Project Memory (Context)
│   ├── philosophy.md    # Core values and logic principles
│   ├── target.md        # Identified target audience
│   └── strategy.md      # Market/system positioning
└── skills/              # Functional Muscle (Execution)
    ├── analysis.md      # Core analysis instructions
    └── copywriting.md   # Final output/tone guidelines

2) Professional Extension: US Market Analyzer (Fintech)

The extended structure integrates real-time data and visualization layers.

US_Market_Analyzer/
├── .agent/              # AI persona and strict operational rules
├── context/             # Investment philosophy and market views
│   ├── philosophy.md    # Quantitative vs. Fundamental approach
│   ├── target.md        # Institutional vs. Retail audience settings
│   ├── strategy.md      # Unique edge (e.g., Macro + Sector correlation)
│   ├── voice.md         # Professional analyst tone & manner
│   ├── risk_profile.md  # [Fintech] User risk tolerance settings
│   └── market_view.md   # [Fintech] Current macro-outlook memory
├── skills/              # Task-specific SOPs (Imperative logic)
│   ├── macro_intelligence.md # Macroeconomic analysis skill
│   ├── portfolio_risk.md     # Risk measurement skill
│   ├── sector_analysis.md    # Sector dominance analysis skill
│   ├── newsletter.md         # Research brief generation skill
│   └── copywriting.md        # Report styling and editing skill
│
├── data/ [NEW]          # Real-time stock data (JSON/CSV storage)
├── utils/ [NEW]         # Python toolset for data processing
├── web/ [NEW]           # Next.js Fintech dashboard source
└── macro_analyzer.py    # Master pipeline orchestrator

Step 3: The Core Documents (The Brain of the System)

Each document is a critical puzzle piece that determines how the agent “thinks.”

1) The Role of the context/ Layer

  • philosophy.md: Sets the First Principles. (e.g., “We prioritize data-driven momentum over news hype.”)
  • target.md: Controls the depth of response. Defines if the AI should explain concepts simply for beginners or use complex jargon for pros.
  • strategy.md: Defines your Competitive Edge. (e.g., “Analyze the correlation between Fed interest rates and Tech stocks.”)
  • voice.md: Ensures Consistency. (e.g., “Sound like a cold, analytical hedge fund manager.”)

2) The Role of the skills/ Layer (Skill Creator)

Each skill follows the ‘Imperative SOP’ (Standard Operating Procedure) format.

sequenceDiagram
    participant User
    participant AI as Antigravity
    participant Creator as skill_creator.md
    
    User->>AI: "Create a new analysis skill"
    AI->>Creator: Initialize Skill Creation Process
    Creator->>AI: Step 1: Define Goal
    Creator->>AI: Step 2: Write Imperative SOP
    Creator->>AI: Step 3: Add Validation Logic
    AI->>User: "skills/new_skill.md created"

Step 4: Illustrating an Intelligent Skill

macro_intelligence.md (Macro-Analysis Skill)

# 🎯 Goal
Generate an 'AI-Driven Macro Report' by correlating Fed interest rate data with S&P 500 indicators.

# 📋 Imperative SOP
1. Read the latest macro indicators (Inflation, Employment) from the 'data/' folder.
2. Analyze whether the current market is in an 'Overbought' or 'Fear' state using `sentiment_tool`.
3. Save the analysis to 'web/api/macro.json' for immediate dashboard reflection.
4. Recommend positioning based on the principles defined in 'philosophy.md'.

Step 5: The Self-Evolving Loop

The true power of Vibe Coding is that the system gets smarter the more you teach it.

graph TD
    Analysis[1. Analysis Output] --> Feedback[2. User Feedback]
    Feedback --> Learning[3. learnings.md]
    Learning --> WrapUp[4. Weekly Wrap-up]
    WrapUp --> Refactor[5. Skills Refactoring]
    Refactor --> Analysis
  1. Learning: When an analysis is too optimistic, you say “Increase the weight of risk factors.” The AI records this in learnings.md.
  2. Refactoring: “Based on recent feedback, permanently update the portfolio_risk.md skill instructions.”

Step 6: Skill Chaining (Full Automation Pipeline)

Chain individual skills together to complete a fully automated analysis pipeline.

Pipeline Flow:

  1. Data Ingestion: Run data_collector.py (Real-time load).
  2. Sector Analysis: Activate sector_analysis.md (Which sectors are leading?).
  3. Macro Intelligence: Activate macro_intelligence.md (Analyze interest rate impact).
  4. Risk Diagnostics: Activate portfolio_risk.md (Is the portfolio safe?).
  5. Visualization: Automatically update all research results on the web/ dashboard.

3. Reference & Artifacts

  1. vibealgolab/US_Market_Analyzer (GitHub) – The reference source code for this architecture.
  2. Gemini Pro API – Connect to a powerful AI brain.
  3. VibeAlgoLab Blog – Ongoing series on advanced Vibe Coding.

⚠️ Important Disclaimer

  1. Educational Purpose: All content, including code and strategies, is for educational and research purposes only.
  2. No Financial Advice: This is not financial advice. I am not a financial advisor.
  3. Risk Warning: Investing involves significant risk. Past performance does not guarantee future results.
  4. Software Liability: Any tools or code provided are “as-is” without warranty. Use at your own risk.
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