DEMIURGOS: Unified Rules for AI Coding Agents in Development
AI agents for code generation often produce inconsistent results, ignoring project specifics. The solution is DEMIURGOS, a system that automatically creates and maintains a single set of rules for all coding agents on your team.
DEMIURGOS Architecture: Three Layers of Control
DEMIURGOS is built on a three-layer architecture that ensures flexibility without redundancy.
- Core (.rules/): Centralized source of project rules. It defines the tech stack, coding standards, architectural patterns, and constraints.
- Native Adapters: Lightweight projections of the core tailored to specific AI agents. For example, .cursor/rules/*.mdc for Cursor, CLAUDE.md for Claude Code, .github/copilot-instructions.md for GitHub Copilot. Rules aren't duplicated—adapters reference the core.
- Extensions: Optional layer for complex scenarios, like multi-agent systems or MCP (Model Context Protocol) integrations. Created only when truly needed.
Real-World Use: Examples Across Tech Stacks
The system shines in various tech contexts, spotting rule gaps via debug mode.
Game Development with Godot 4.3 and GDScript
When building a health component, the agent follows rules to generate typed code using node composition and signals. Debug mode (/debug full) uncovers gaps:
- Missing conventions for input action names (e.g., player_dash).
- Lack of rules for linking abilities to UI via cooldown_started signals.
- Suggestion to replace SceneTreeTimer with Timer nodes for pause-support.
Python Backend with Async SQLAlchemy
For an order API, the agent applies three-layer architecture rules, custom exceptions, and batch queries. Debug reveals:
- No exception-to-HTTP-status mapping in the router.
- Missing rules for race conditions using SELECT FOR UPDATE.
- Unclear separation of flush() in services vs. commit() in routers.
Frontend with Astro and SolidJS
Building a theme toggle, the agent sticks to Islands Architecture and UnoCSS rules. Gaps include:
- No FOUC prevention strategy via inline <head> scripts.
- Missing universal try/catch for Browser APIs.
- Uncertainty between createSignal and createStore for state.
Key Benefits of Implementing DEMIURGOS
Adopting this rules system delivers measurable wins for dev teams.
- Code Consistency: All AI agents follow the same standards, cutting review and refactoring time.
- Faster Onboarding: New devs pull rules via git, and their agents instantly generate team-aligned code.
- Iterative Improvement: Debug mode (/debug) spots rule gaps after 10–20 prompts, prioritizing fixes. Common areas:
- Game design conventions in game projects.
- Business logic and transactions in backends.
- Browser API edge cases and accessibility in frontends.
- Supports 22+ Tools: One core rule set projects to Cursor, Windsurf, Claude Code, GitHub Copilot, Kiro, JetBrains AI Assistant, and more via adapters.
- Minimal Overhead: Rules live as repo files—no extra infra or complex setup needed.
Key Takeaways
- DEMIURGOS fixes AI agents' lack of project context with centralized rules.
- Three-layer architecture keeps things flexible without code duplication.
- Debug mode uncovers rule gaps for ongoing refinement.
- Supports 22+ dev tools for team-wide consistency.
- Implementation cuts agent management time and boosts code quality.
— Editorial Team
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