dml

What problem does it solve?

This Skill provides AI agents with a structured, event-driven memory system that ensures deterministic recall, constraint enforcement, and auditability, overcoming the limitations of traditional LLM context windows and fuzzy memory.

Core Features & Use Cases

• Event-Driven Memory: Records all agent actions and state changes as immutable events, allowing for precise state reconstruction and provenance tracking.
• Constraint Enforcement: Enforces user-defined rules (facts, preferences, requirements) against agent actions, preventing undesirable or unsafe behavior.
• Counterfactual Analysis: Enables "what-if" scenarios by replaying historical events with modified conditions to predict outcomes.
• Use Case: An AI travel agent uses DML to remember user preferences (budget, accessibility needs), enforce constraints (e.g., "wheelchair accessible required"), record decisions (bookings), and even simulate alternate timelines if a constraint was violated or introduced later.

Quick Start

Use the dml skill to add a fact about the user's destination being Japan.