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yara-authoring雅拉创作

Agent Skill

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

总安装

5,221

周安装

222

GitHub Stars

公开资料未说明

下载量

1,829
OpenClaw

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

2

许可证

MIT-0

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:yara-authoring(雅拉创作)
来源仓库:https://github.com/solomonneas/yara-authoring
安装命令:
openclaw skills install yara-authoring
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。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install yara-authoring

简介

辅助编写 YARA-X 恶意软件检测规则,优化字符串匹配与模块使用。

  • 适用于安全团队进行病毒特征提取与威胁狩猎。
  • 支持 PE/ELF 等格式解析,减少误报率。
  • 规则需经人工复核后方可投入生产环境。yara-authoring 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 禁止用于非法入侵或绕过安全防护措施。

SKILL.md

name
yara-authoring
version
1.0.0
description
Write high-quality YARA-X detection rules for malware hunting. Covers atom selection, string optimization, false positive reduction, module usage (PE, ELF, Macho), and Trail of Bits methodology. Includes rule templates and testing workflows.

YARA-X Rule Authoring

Write detection rules that catch malware without drowning in false positives. Based on Trail of Bits methodology.

Core Principles

  1. Strings must generate good atoms — YARA extracts 4-byte subsequences for fast matching. Strings with repeated bytes, common sequences, or under 4 bytes force slow bytecode scans.
  2. Target specific families, not categories — "Detects ransomware" is useless. "Detects LockBit 3.0 config extraction routine" is useful.
  3. Test against goodware — Validate against clean file sets before deployment.
  4. Short-circuit with cheap checks firstfilesize < 10MB and uint16(0) == 0x5A4D before expensive string searches.
  5. Metadata is documentation — Future you needs to know what this catches and why.

YARA-X Basics

YARA-X is the Rust successor to legacy YARA: 5-10x faster, better errors, built-in formatter, stricter validation, new modules (crx, dex).

Install: brew install yara-x / cargo install yara-x Commands: yr scan, yr check, yr fmt, yr dump

Rule Template

import "pe"

rule FamilyName_Variant_Technique : tag1 tag2 {
    meta:
        author      = "Your Name"
        date        = "2026-02-14"
        description = "Detects [specific behavior] in [malware family]"
        reference   = "https://..."
        tlp         = "TLP:WHITE"
        hash        = ""
        score       = 75  // 0-100 confidence

    strings:
        // Unique strings from the sample
        $api1 = "VirtualAllocEx" ascii
        $api2 = "WriteProcessMemory" ascii
        $str1 = { 48 8B 05 ?? ?? ?? ?? 48 85 C0 }  // hex with wildcards
        $pdb  = /[A-Z]:\\.*\\Release\\.*\.pdb/ nocase

    condition:
        uint16(0) == 0x5A4D and
        filesize < 5MB and
        (2 of ($api*) and $str1) or
        $pdb
}

Naming Convention

Family_Variant_Technique — examples:

  • Emotet_Loader_DocumentMacro
  • CobaltStrike_Beacon_x64
  • Generic_Cryptominer_XMRig

String Selection

Good strings (unique, specific):

  • Mutex names, PDB paths, C2 URLs
  • Unique byte sequences from disassembly
  • Custom encryption constants
  • Uncommon API call sequences

Bad strings (too common, high FP):

  • http://, https://, common API names alone
  • Single common words, short strings (<4 bytes)
  • Strings found in Windows system files

Condition Patterns

// Performance-ordered (cheap → expensive)
condition:
    uint16(0) == 0x5A4D and     // Magic bytes (instant)
    filesize < 10MB and          // Size filter (instant)
    2 of ($unique*) and          // String matching (fast)
    pe.imports("kernel32.dll")   // Module check (slower)

Common magic bytes:

PlatformCheck
PE (Windows)uint16(0) == 0x5A4D
ELF (Linux)uint32(0) == 0x464C457F
Mach-O 64-bituint32(0) == 0xFEEDFACF
PDFuint32(0) == 0x25504446
Office/ZIPuint32(0) == 0x504B0304

Performance Rules

  1. Put filesize and magic byte checks FIRST in condition
  2. Never use unbounded regex like /.*/
  3. Avoid for all with complex conditions on large files
  4. Use ascii or wide, not both unless needed
  5. Hex strings with specific bytes > wildcards > regex
  6. Use at for fixed offsets instead of scanning entire file

Testing

# Validate syntax
yr check rules/

# Scan a sample
yr scan rules/my_rule.yar suspicious_file.exe

# Scan directory
yr scan rules/ samples/ --threads 4

# Format rules consistently
yr fmt rules/my_rule.yar

False Positive Reduction

  • Add filesize constraints (malware has typical size ranges)
  • Require multiple string matches (2 of ($str*) not any of)
  • Exclude known good paths/publishers via not conditions
  • Score-based approach: assign confidence scores in metadata, triage by threshold
  • Test against goodware corpus before deployment

Reference

Full methodology, module docs (pe, elf, crx, dex), and migration guide from legacy YARA: https://github.com/trailofbits/skills/tree/main/plugins/yara-authoring

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

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

OpenClaw

84.2%
按下载量换算1,540

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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