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abstract-strategy抽象策略

Agent Skill

abstract-strategy 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:abstract-strategy(抽象策略)
来源仓库:https://github.com/jwynia/agent-skills
仓库路径:skills/abstract-strategy
安装命令:
npx skills add https://github.com/jwynia/agent-skills --skill abstract-strategy
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

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skills.shnpx skills
npx skills add https://github.com/jwynia/agent-skills --skill abstract-strategy

简介

用于设计抽象策略类游戏,聚焦完美信息、无随机性和深度战略机制。

  • 提供游戏框架构建、机制设计与评估方法,强调玩家决策主导性。
  • 适用于开发如 Hex、Go、Chess 类纯策略型桌游或数字游戏原型。
  • 安装命令:npx skills add https://github.com/jwynia/agent-skills --skill abstract-strategy
  • 支持 Codex、Claude、Cursor、Gemini CLI,通过 GitHub 安装。

SKILL.md

Abstract Strategy Game Design

Purpose

Design abstract strategy games—games with perfect information, no randomness, and strategic depth. Provides frameworks for ideation, design, and evaluation.

Core Definition

Abstract strategy games require:

  • Perfect Information: All game state visible to all players
  • No Randomness: Outcomes determined solely by player decisions
  • Minimal Theme: Mechanics over narrative
  • Player Agency: Success depends on strategic thinking

Quick Reference: Game Types

TypeCore MechanicExamples
ConnectionForm paths/networksHex, TwixT
TerritoryControl areasGo, Othello
CaptureEliminate piecesChess, Checkers
PatternCreate arrangementsGomoku, Pentago
RacingReach goal firstChinese Checkers

Design Principles

The Holy Grail: Depth-to-Complexity Ratio

Maximum strategic depth with minimum rules complexity.

How to achieve:

  • Start with single strong core mechanism
  • Remove anything that doesn't support the core
  • Every rule should create multiple strategic implications
  • Prefer emergent complexity over explicit rules

Meaningful Decision Architecture

Four components of meaningful choice:

  1. Awareness: Players understand options
  2. Consequence: Immediate and long-term effects
  3. Permanence: Decisions have lasting impact
  4. Reminders: Game state reflects past choices

Ideal Parameters:

  • Branching factor: 20-40 moves/turn for human play
  • Horizon: 3-5 moves ahead with effort
  • Multiple paths: 3-4 viable strategies minimum

Core Mechanisms Toolkit

Board Topology

  • Grids: Square, hexagonal, triangular, irregular
  • Connectivity: How spaces relate
  • Edges: How boundaries affect strategy
  • Size: Larger = exponentially more complex

Piece Systems

  • Uniform: All pieces identical (Go)
  • Differentiated: Unique abilities (Chess)
  • Transforming: Change during play (Checkers kings)
  • Ownership: Fixed vs. capturable

Movement & Placement

  • Placement only: Pieces don't move once placed (Go)
  • Movement only: Pieces start on board (Chess)
  • Hybrid: Both placement and movement (Hive)

Victory Conditions

  • Elimination, Position, Pattern, Territory, Points, Stalemate

Balance Considerations

First-Player Advantage Mitigation

  • Pie Rule: Second player can swap after first move
  • Komi: Point compensation for second player
  • Variable Setup: Randomized starting positions
  • Simultaneous: Both move at once

Avoiding Degenerate Strategies

  • No single dominant path
  • Counter-strategies exist for every strong position
  • Passive play punishable
  • Aggressive play doesn't guarantee victory

Design Process

Three Starting Points

1. Mechanism-First

  1. Identify interesting core mechanic
  2. Build minimal game around it
  3. Add only what enhances core
  4. Remove everything else

2. Experience-First

  1. Define target player experience
  2. Identify mechanisms that create it
  3. Prototype and test rapidly
  4. Iterate on feedback

3. Constraint-Based

  1. Set specific limitations (components, time, space)
  2. Find creative solutions within constraints
  3. Often leads to elegant designs

When to Add/Remove Complexity

Add when:

  • Core feels solved too quickly
  • Players master in <10 plays
  • Decisions feel obvious

Remove when:

  • Rules take >10 minutes
  • Players forget rules
  • Strategies feel arbitrary

Scrap when:

  • No tweaking fixes fundamentals
  • Core mechanism isn't interesting
  • Feels like inferior version of existing game

Brainstorming Techniques

1. Mechanism Extraction from Non-Games

Extract from physics, biology, economics, chemistry, social systems:

  • Pieces that "decay" unless refreshed (entropy)
  • Moves creating "waves" along patterns (physics)
  • Pieces forming "bonds" limiting movement (chemistry)
  • "Market" squares with fluctuating values (economics)

2. Extreme Property Isolation

Take one property to absolute extreme:

  • Game where pieces visible only when adjacent to your others
  • Every move must maintain rotational symmetry
  • Pieces exist only one turn unless refreshed
  • Board wraps in non-intuitive ways (Klein bottle)

3. Impossible Constraint Challenges

Start with seemingly impossible constraints:

  • Game on a 1D line
  • Pieces in probability clouds until observed
  • Victory condition voted on by piece positions
  • Pieces leave "trails" becoming new pieces

4. Anti-Pattern Starting Points

Design intentionally bad games, then invert:

  • Always-draw game → Add accumulating positional advantages
  • Pure calculation → Add pieces that change rules
  • Dominant strategy → Make it vulnerable to specific counters

5. Mathematical Structure Mining

  • Pieces move along Hamiltonian paths only
  • Positions valued by prime factorization
  • Fractal boards with repeating patterns
  • Moves must preserve mathematical invariants

Evaluation Framework

Strategic Richness Indicators

Depth:

  • Games last 20+ meaningful turns
  • Opening, midgame, endgame feel distinct
  • Multiple viable opening strategies
  • Comebacks possible but not trivial

Complexity:

  • New players grasp rules in <5 minutes
  • Experts keep discovering patterns
  • High-level play looks different from beginner

Common Failures

ProblemSymptomsSolution
Analysis ParalysisExcessive turn timeLimit options, clearer objectives
Solved GameSame outcome alwaysIncrease branching, add variety
KingmakerLoser picks winnerSimultaneous resolution

Testing Protocol

Phase 1: Proof of Concept

  • Test core mechanic in isolation
  • Verify basic fun factor
  • Identify broken strategies

Phase 2: Mechanics

  • Test each subsystem
  • Look for unintended interactions
  • Measure game length

Phase 3: Integration

  • Full game, all systems
  • Different skill levels
  • Quantitative data

Phase 4: Blind Testing

  • Players learn from rulebook only
  • Identify ambiguities
  • Test learning curve

Testing Checklist

Mechanical

  • All rule interactions verified
  • Edge cases resolved
  • Victory achievable but not trivial
  • No unbreakable stalemates

Balance

  • First player wins 45-55%
  • Multiple strategies win regularly
  • No dominant opening
  • Skill affects outcome

Experience

  • Games complete in target time
  • Players want rematch
  • Decisions feel meaningful
  • Players improve with practice

Accessibility

  • Rules learned in <5 minutes
  • Rules fit one page
  • No ambiguous situations
  • Components distinguishable

Quick Evaluation Filters

30-Second Test: Can you explain core concept in 30 seconds?

Originality Test: Does it feel like variant of existing game?

Decision Test: Are there obviously interesting decisions?

Depth Test: Could this sustain interest for 50+ plays?


Session Structure (2 Hours)

  1. 10 min: Pick 3-4 brainstorming techniques
  2. 60 min: Generate 15-20 ideas per technique
  3. 20 min: Expand 5-10 promising ideas
  4. 20 min: Combine and explore hybrids
  5. 10 min: Apply filters, select for prototyping

Anti-Patterns

1. Complexity as Depth

Pattern: Adding rules, exceptions, and special cases to make the game feel "deeper." Why it fails: Complexity and depth are different. Complex rules create burden; depth emerges from simple rules with rich interactions. Chess has simpler rules than many shallow games. Fix: Ruthlessly remove complexity that doesn't add strategic options. If a rule requires explanation but doesn't create interesting decisions, cut it.

2. Solved Game Blindness

Pattern: Creating a game where optimal play always produces the same outcome—often draws or first-player wins. Why it fails: Once players discover the solution, the game becomes rote execution rather than strategic exploration. No amount of polish fixes a solved game. Fix: Test extensively with strong players. If games start converging on identical patterns, add asymmetry or increase branching factor. The pie rule helps but doesn't solve fundamental issues.

3. Decision Paralysis

Pattern: Every position has dozens of equally viable options with unclear consequences. Why it fails: Strategic games need meaningful comparison between choices. When all options seem equivalent, decisions become random rather than strategic. Fix: Reduce branching factor or create clearer evaluation heuristics. Players should be able to identify 3-5 promising moves without analyzing every possibility.

4. Theme Creep

Pattern: Adding narrative or thematic elements that don't connect to mechanical decisions. Why it fails: Abstract strategy games work because mechanics are the content. Theme that doesn't inform decisions is decoration that slows play without adding depth. Fix: Either commit to a themed game (different framework) or keep theme purely cosmetic. Don't let theme suggest mechanics that don't serve strategy.

5. Perfect Information Violations

Pattern: Adding hidden information, simultaneous resolution, or dice "for variety." Why it fails: Abstract strategy games are defined by perfect information and determinism. Adding randomness or hidden elements creates a different game type with different design principles. Fix: If the game needs variety, add it through board setup, victory condition selection, or piece starting positions—not through mid-game randomness.

Integration

Inbound (feeds into this skill)

SkillWhat it provides
brainstormingIdeation techniques for mechanism discovery
researchHistorical game analysis and mathematical structure research

Outbound (this skill enables)

SkillWhat this provides
(playtesting)Designs ready for player validation
(rulebook writing)Tested mechanics ready for documentation

Complementary

SkillRelationship
brainstormingUse brainstorming for raw idea generation; abstract-strategy provides evaluation and refinement frameworks

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