folder-structure.md

Standardize a modular AI trading intelligence system folder architecture.

Updated May 19, 2026
One-click install
npx skills add https://github.com/sarkcesscrewpay/trading-intelligence-system --skill folder-structure-md
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: folder-structure.md
Source: https://github.com/sarkcesscrewpay/trading-intelligence-system/tree/main/skills/folder_structure_md_trading_intelligence_v_1
Command: npx skills add https://github.com/sarkcesscrewpay/trading-intelligence-system --skill folder-structure-md

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the confusion and fragility that come from unclear project layouts by prescribing a production-grade folder structure for a modular AI Trading Intelligence System.

Core Features & Use Cases

  • Modular, scalable structure: Organizes core runtime logic (src), AI reasoning skills (skills), orchestration agents (agents), and integrations (services) to support long-term growth.
  • Separation of responsibilities: Keeps responsibilities isolated to avoid spaghetti architecture and make components easier to test, maintain, and extend.
  • Operational readiness: Includes guidance for essential production directories and artifacts such as scheduler jobs, persistent memory, config, logs, database, scripts, tests, and deployment files.

Use Case: When building or onboarding to the trading intelligence system, teams can quickly place new features (e.g., a new broker integration, a new scheduling job, or an additional reasoning skill) into the correct location with predictable boundaries.

Quick Start

Provide your repository’s root and ask the AI to generate a matching directory tree and map each existing file to the recommended structure.

Frequently Asked Questions about folder-structure.md

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best folder structure for a modular AI trading system?

A modular AI trading folder structure isolates runtime code, AI reasoning skills, agent orchestration, and external services into explicit directories. This separation eliminates integration drift and ensures components remain testable and maintainable as the system scales.

How do I organize webhooks and scheduling jobs in an AI trading project?

Organize webhooks and scheduling jobs by placing them in dedicated operational directories alongside configuration and deployment assets. This isolates external integrations and automated tasks from core runtime logic, preventing spaghetti architecture in the trading system.

How do I map existing files to a production-grade trading architecture?

To map existing files, provide your repository root to generate a matching directory tree, then align each file with the recommended modular structure. This standardizes the layout and clarifies explicit directory boundaries for faster onboarding.

Does this project structure work for Node.js trading automation systems?

Yes, this project structure supports Node.js trading automation by defining explicit boundaries for runtime code, persistent memory, logs, and database directories. It ensures operational readiness for production environments regardless of the specific runtime.

Why do I need a standardized folder structure for AI trading intelligence?

You need a standardized folder structure to eliminate the confusion and fragility from unclear project layouts. It provides predictable boundaries for placing new broker integrations, reasoning skills, or scheduling jobs during active development.

Can I use this architecture to separate AI reasoning skills from broker integrations?

Yes, this architecture explicitly separates AI reasoning skills from broker integrations by placing them in distinct directories. This modular isolation ensures that extending reasoning capabilities or adding new services does not create integration drift.