| name | game-logic |
| description | Game development rules: formal rule encoding, immutable state transitions, bounded AI search (minimax/MCTS), and independent win/draw/loss verification. Use for board games, card games, turn-based strategies, or any game with defined rules.
|
Game Logic
Leading word: bounds — every search, every state, every move has a hard bound. Enforce it.
Rules
- Formal rules — Encode game rules explicitly as data. No implicit assumptions about valid moves or win conditions.
- Immutable state — Model game state as immutable data structures. Each move produces a new state; never mutate in-place.
- Bounded search — AI opponents use bounded search: minimax with depth limit, MCTS with iteration cap. Never search unbounded.
- Independent win detection — Implement win/draw/loss detection that is verified independently of the game loop, so the same check works from any phase.
- Input validation — Validate every player move against the current state before applying it. Reject with the rule citation.
Usage
See references/state-transitions.md for immutable state patterns.
Pseudocode
SKILL game-logic
INPUTS:
gameType: string // board, card, turn-based, realtime
playerCount: number // 1-N
ruleSource: string // Formal rules location
aiOpponent: boolean // Whether AI opponent is needed
OUTPUTS:
gameState: object // Immutable game state
validMoves: array // Legal moves from current state
winDetection: function // Win/draw/loss check
aiMove?: object // AI-selected move (if applicable)
PRECONDITIONS:
- Game rules encoded before state management
- State transitions produce new state (no mutation)
- All player moves validated before application
POSTCONDITIONS:
- Game state always valid after each move
- Win/draw/loss detected correctly regardless of phase
- AI search bounded by depth or iteration limit
- Invalid moves rejected with rule citation
ERROR_HANDLING:
- Invalid move → reject with rule citation
- Inconsistent state → reconstruct from move history
- AI search timeout → return best move found so far
