lorcana-rules

Retrieve cited Disney Lorcana rules and emit rulings or JSON behavior constraints.

Updated May 14, 2026
One-click install
npx skills add https://github.com/chroniicallydiistracted/LorcanaChamp --skill lorcana-rules-chroniicallydiistracted
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: lorcana-rules
Source: https://github.com/chroniicallydiistracted/LorcanaChamp/tree/main/references/lorcana-simulator/.agents/skills/lorcana-rules
Command: npx skills add https://github.com/chroniicallydiistracted/LorcanaChamp --skill lorcana-rules-chroniicallydiistracted

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill prevents incorrect Disney Lorcana rulings by retrieving authoritative Comprehensive Rules text and converting it into implementation-ready constraints for card/game-engine behavior.

Core Features & Use Cases

  • Indexed, citation-first rule grounding: Retrieves the minimal necessary rule sections from the Comprehensive Rules indexes and cites the exact rule-id-style references when available.
  • Two output modes for different audiences: Explains rulings for players or produces a structured JSON handoff (citations, behavior constraints, test implications, and ambiguities) for developers building or validating mechanics.
  • Guardrails + memory protocol: Enforces “no guidance without citations,” distinguishes confirmed text vs. inference, flags uncertainty, and updates a rules memory bank after execution.

Quick Start

Ask the AI: “Using cited Comprehensive Rules, how does Resist interact with damage that is put or moved by an effect, and what behavior constraints should my engine assert?”

Frequently Asked Questions about lorcana-rules

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

FAQPage Schema
How do I get accurate Disney Lorcana rules citations for complex card interactions?

Disney Lorcana rules citations are retrieved by searching indexed Comprehensive Rules sections and release notes, returning concise rulings with exact rule-id references. The system requires index-first retrieval and strict citation usage, explicitly labeling uncertainty when sources are insufficient.

How do I convert Disney Lorcana rulings into test-ready constraints for a game engine?

Converting Lorcana rulings into test-ready constraints uses a Mode B JSON handoff output containing citations, behavior constraints, test implications, and ambiguities. This structured output provides developers with exact engine assertions needed for validating mechanics.

What is the difference between confirmed Comprehensive Rules text and inferred rulings?

Confirmed rulings use directly retrieved Comprehensive Rules text with strict citations, while inferred rulings are explicitly labeled as uncertainty when indexed sources are insufficient. The system enforces guardrails providing no guidance without citations and distinguishes confirmed text from inference.

Can I use this to resolve gameplay mechanic questions about specific card abilities like Resist?

Gameplay mechanic questions about card abilities like Resist are resolved by retrieving relevant Comprehensive Rules sections and related release notes. The system applies index-first retrieval from master and by-topic indexes to explain interactions such as damage put or moved by effects.

Why does the rules retrieval system require an indexed memory update protocol?

The indexed memory update protocol ensures the rules memory bank stays current after execution, maintaining retrieval accuracy for future Comprehensive Rules queries. This protocol enforces the index-first retrieval approach required for generating citation-grounded rulings.

Does the Lorcana rules system provide output for both players and developers?

The Lorcana rules system provides two distinct output modes: concise ruling explanations with rule citations for players, and structured JSON handoffs containing behavior constraints and test implications for developers building or validating game mechanics.