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winning-avg-corewars赢得平均核心战争

Agent Skill

winning-avg-corewars 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。

总安装

840

周安装

34

GitHub Stars

93

下载量

264
CodexClaudeCursorGemini CLI

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:winning-avg-corewars(赢得平均核心战争)
来源仓库:https://github.com/letta-ai/skills
仓库路径:skills/winning-avg-corewars
安装命令:
npx skills add https://github.com/letta-ai/skills --skill winning-avg-corewars
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。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/letta-ai/skills --skill winning-avg-corewars

简介

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 支持基于关键词、任务场景或来源线索进行信息检索与筛选。
  • 通过命令行工具调用,可直接集成到宿主环境中使用。
  • 安装命令:npx skills add https://github.com/letta-ai/skills --skill winning-avg-corewars。
  • 使用前请确认权限范围、维护状态及是否涉及联网或文件操作。

SKILL.md

CoreWars Warrior Development

Overview

This skill provides a systematic methodology for developing CoreWars warriors that achieve specified win rate thresholds against multiple opponents. It emphasizes deep opponent analysis, systematic parameter tuning, automated testing, and targeted strategy development rather than ad-hoc trial-and-error approaches.

Prerequisites

Before beginning warrior development:

  1. Verify pmars (Portable MARS) is available for testing battles
  2. Identify all opponent warriors and their required win rate thresholds
  3. Read and understand the opponent warrior code before writing any counter-strategies
  4. Determine core size and other battle parameters from the task requirements

Development Workflow

Phase 1: Opponent Analysis

Before writing any warrior code, thoroughly analyze each opponent:

  1. Read opponent source code - Understand the warrior's strategy, not just observe its behavior
  2. Categorize opponent type - Identify if it's a bomber, scanner, replicator, imp, paper, stone, or hybrid
  3. Extract exploitable patterns:

- What step/interval does it use for bombing or scanning? - What positions in the core does it avoid or target? - Does it have predictable timing or movement patterns? - What are its weaknesses (e.g., vulnerable to imps, slow startup)?

  1. Document findings - Create a brief profile for each opponent: Opponent: stone.red Type: Bomber Step: 4 (bombs every 4 positions) Weakness: Predictable bombing pattern, vulnerable to positions not divisible by 4 Counter-strategy ideas: Use step sizes that avoid multiples of 4

Phase 2: Strategy Selection

Based on opponent analysis, determine appropriate warrior archetypes:

Common CoreWars Warrior Types:

  • Scanner: Searches for enemies before attacking
  • Bomber: Blindly drops DAT bombs at regular intervals
  • Replicator (Paper): Creates copies of itself throughout the core
  • Imp: Simple MOV 0,1 that creates self-propagating spirals
  • Vampire: Uses JMP traps to capture enemy processes
  • Stone: Bomber with defensive spl/jmp stuns
  • Silk: Optimized replicator with specific step patterns

Strategy Decision Tree:

  1. Against slow bombers → Fast replicators or scanners
  2. Against replicators → Scanners with quick-scan or vampires
  3. Against imps → Imp gates (spl 0, dat 0 pairs) or core clears
  4. Against scanners → Decoys combined with attack components
  5. Against core clears → Fast-spreading replicators

Phase 3: Initial Implementation

Start with simple, focused warriors rather than complex multi-component designs:

  1. Begin minimal - Test the simplest version of chosen strategy first
  2. One component at a time - Add complexity only after understanding base performance
  3. Use proven patterns - Reference established CoreWars strategies from documentation
  4. Avoid premature optimization - Get basic functionality working before tuning

Phase 4: Systematic Testing

Use the automated test script (scripts/test_warrior.sh) for consistent evaluation:

./scripts/test_warrior.sh warrior.red

Testing principles:

  • Run sufficient rounds (100+) for statistical significance
  • Track results in a structured format across iterations
  • Test against ALL opponents after every change, not just the one being targeted
  • Record parameter values alongside results for later analysis

Phase 5: Parameter Optimization

When tuning parameters (step sizes, gate positions, spl counts):

  1. Systematic search - Use grid search or binary search over parameter ranges
  2. Understand relationships - Document why certain values work (e.g., step=17 avoids common bombing intervals)
  3. Track tradeoffs - Changes that improve one matchup may harm another
  4. Hypothesis-driven changes - Before each modification, state the expected outcome

Key parameters to tune:

  • Step size for bombers/scanners (affects coverage pattern)
  • Gate position for imp defenses
  • Number of SPL instructions (affects process count vs. speed)
  • Starting offset from main code

Phase 6: Debugging Failures

When a warrior consistently loses to an opponent:

  1. Profile the failure - Use pmars debugging to watch the battle: pmars -b -r 1 warrior.red opponent.red
  2. Identify failure mode - Is the warrior being bombed, captured, out-replicated, or core-cleared?
  3. Trace causation - At what point in the battle does the warrior lose control?
  4. Develop targeted fix - Address the specific failure mechanism, not symptoms

Phase 7: Multi-Opponent Optimization

When facing different win rate requirements:

  1. Prioritize high-threshold opponents - Focus more effort on 75% targets than 33% targets
  2. Consider specialized warriors - Sometimes separate warriors per opponent outperform one universal warrior
  3. Identify compatible strategies - Find approaches that don't harm each other
  4. Accept strategic tradeoffs - A warrior optimized for hard targets may sacrifice performance on easy targets

Common Pitfalls

Mistakes to Avoid

  1. Ad-hoc parameter changes - Changing values without clear hypotheses leads to random walks
  2. Ignoring opponent code - Reading behavior isn't understanding; examine the actual source
  3. Over-engineering early - Complex multi-component warriors obscure what works and what doesn't
  4. Neglecting hardest opponents - Easy wins don't compensate for failing required thresholds
  5. Batch testing - Test after EVERY change to understand impact immediately
  6. Syntax/spacing issues - Redcode assemblers are sensitive; verify syntax with dry runs
  7. Copying without understanding - Adapting code from examples requires understanding why it works

Red Flags During Development

  • Making changes without being able to explain expected improvement
  • Win rate oscillating without trending upward
  • Spending most time on already-passing matchups
  • Not using debugging tools to understand failures
  • Submitting warriors that don't meet all thresholds

Verification Checklist

Before considering warrior complete:

  • All opponents analyzed and weaknesses documented
  • Win rates meet or exceed ALL required thresholds
  • Tested with statistically significant round counts (100+)
  • Parameter choices can be justified with reasoning
  • Failure modes against difficult opponents understood
  • No syntax errors or assembly warnings

Resources

scripts/

Contains test_warrior.sh - An automated test script that runs battles against all opponents and reports results in a consistent format. Use this for every iteration to track progress systematically.

references/

Contains corewars_strategies.md - Reference documentation covering CoreWars warrior archetypes, common patterns, and known effective techniques. Consult this before implementing unfamiliar strategies.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

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Gemini CLI

23.21%
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Antigravity

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按下载量换算45

windsurf

13.19%
按下载量换算35

OpenCode

7.88%
按下载量换算21

Codex

3.81%
按下载量换算10

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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