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psypi-dev

Analyze codebase architecture and quality using psypi dual-agent workflows.

Updated Apr 30, 2026
Search Tags:#code quality#architecture#AI agent#system prompt#psypi
One-click install
npx skills add https://github.com/emptist/psypi --skill psypi-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: psypi-dev
Source: https://github.com/emptist/psypi/tree/main/ppi_skills/psypi-dev
Command: npx skills add https://github.com/emptist/psypi --skill psypi-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides deep knowledge for optimizing code quality and architecture, guiding users through best practices and system design principles within the psypi framework.

Core Features & Use Cases

  • Dual-Agent Architecture: Leverage A-bot (Autonomic) and S-bot (Somatic) for continuous quality improvement.
  • PDCA Cycle: Follow the Plan-Do-Check-Act cycle for structured problem-solving.
  • SOUL Mechanism: Understand and utilize the SOUL mechanism for defining and managing AI identities.
  • Context Window Constraint: Ensure system prompts are efficiently composed within token constraints.
  • Database-First Design: Learn how psypi utilizes a database-centric approach for flexibility across projects.

Quick Start

Analyze your codebase with psypi to enhance its architecture and ensure quality with A-bot and S-bot workflows.

Frequently Asked Questions about psypi-dev

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

FAQPage Schema
How do I optimize code quality and architecture using an AI agent workflow?▼

Optimizing code quality and architecture with an AI agent workflow involves leveraging dual-agent systems like A-bot and S-bot to continuously assess and improve codebase health within a structured framework.

What is the dual-agent architecture for codebase health and how does it work?▼

Dual-agent architecture deploys an Autonomic agent and a Somatic agent to systematically manage and refine codebase health through continuous feedback loops and structured problem-solving.

How do I compose system prompts within context window constraints for AI agents?▼

Composing system prompts within context window constraints requires efficiently organizing AI identity definitions and operational guidelines to ensure the agent functions optimally without exceeding token limits.

When do I need database-first design for AI system architecture?▼

Database-first design is needed for AI system architecture when you require high flexibility across multiple projects, utilizing a database-centric approach to manage system prompts and operational data.

Do I need to understand the SOUL mechanism to use dual-agent workflows?▼

Yes, understanding the SOUL mechanism is required because it provides the foundational structure for defining, managing, and deploying AI identities within the dual-agent workflow.

How do I apply the PDCA cycle to continuous code quality improvement?▼

Applying the PDCA cycle to code quality involves following the Plan-Do-Check-Act methodology to structure problem-solving and enable continuous architectural refinement within your codebase.

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