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Agent Skill · 研究检索

winning-avg-corewars(赢得平均核心战争)

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。

winning-avg-corewars 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 45,550。

总安装 840周安装 34GitHub Stars 93下载 264核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/winning-avg-corewars

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/letta-ai/skills --skill winning-avg-corewars;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

winning-avg-corewars 怎么安装?

在终端执行 npx skills add https://github.com/letta-ai/skills --skill winning-avg-corewars。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

winning-avg-corewars 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

winning-avg-corewars 需要什么权限?

权限级别是只读。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。

winning-avg-corewars 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

winning-avg-corewars 用的人多吗?

公开数据是累计安装 840 次,近一周安装 34 次,下载 264 次,GitHub 93 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

winning-avg-corewars 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
winning-avg-corewars

赢得平均核心战争 · 本页

84093只读MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

平台分布

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27.71%
按下载量换算73

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23.21%
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16.91%
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

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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