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memory-forensics记忆取证

Agent Skill

memory-forensics 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

388

周安装

16

GitHub Stars

4

下载量

127
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

2

许可证

unknown

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:memory-forensics(记忆取证)
来源仓库:https://github.com/alphaonedev/openclaw-graph
仓库路径:skills/memory-forensics
安装命令:
npx skills add https://github.com/alphaonedev/openclaw-graph --skill memory-forensics
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/alphaonedev/openclaw-graph --skill memory-forensics

简介

该技能用于内存转储分析,支持 Volatility 工具集调用以识别恶意进程。

  • 适用于系统入侵调查、Rootkit 检测及 RAM 取证等安全场景。
  • 通过 GitHub 仓库安装,适用于 Codex、Claude、Cursor、Gemini CLI。
  • 需具备合法授权方可分析他人设备内存数据。
  • 插件结果可能存在偏差,建议交叉验证多个检测方法。

SKILL.md

memory-forensics

Purpose

This skill enables analysis of volatile memory dumps using tools like Volatility to identify malware, rootkits, and security breaches, supporting digital forensics investigations.

When to Use

Use this skill during incident response for suspected breaches, when analyzing RAM dumps from compromised systems, or for proactive security assessments on endpoints with potential malware infections.

Key Capabilities

  • Parse memory dumps to extract processes, network connections, and injected code using Volatility's plugins.
  • Detect hidden processes and rootkits via checks for process hollowing or DKOM (Direct Kernel Object Manipulation).
  • Analyze hibernation files or pagefiles for artifacts like command history or encryption keys.
  • Support for multiple dump formats, including raw, VMware, and crash dumps, with automated profile detection.

Usage Patterns

Invoke this skill via CLI commands in a Python script or directly in a terminal. Always specify the memory dump file and required plugins. For automation, wrap commands in a function that handles file paths and outputs. Use environment variables for API keys if extending to cloud-based forensics tools.

Example pattern in Python:

import subprocess
dump_file = 'memory.dmp'
subprocess.run(['volatility', '-f', dump_file, 'pslist'])

Common Commands/API

Use Volatility framework commands for core functionality. Set the VOLATILITY_PROFILE env var for profile mismatches, e.g., $VOLATILITY_PROFILE=Win7SP1x64.

  • Command: volatility -f memory.dmp imageinfo — Identifies the OS profile from the dump.

- Flags: -f for file path, --profile=Win10x64 to override auto-detection.

  • Command: volatility -f memory.dmp malfind — Scans for injected code or malware hooks.

- Example: Pipe output: volatility -f memory.dmp malfind > malware_output.txt

  • API Endpoint: If using Volatility3 via Python API, import as from volatility3.framework import interfaces, then call interfaces.configuration.ConfObject() for configurations.

- Snippet: from volatility3 import framework config = framework.require_plugin('windows').build_configuration() config['primary'] = 'memory.dmp'

  • Config Format: JSON-based, e.g., {"plugin": "pslist", "dumpfile": "memory.dmp"} for custom runs.

Integration Notes

Integrate by installing Volatility via pip (pip install volatility), then call from scripts. For authentication in cloud forensics (e.g., AWS Memory DB analysis), use env vars like $AWS_ACCESS_KEY_ID and $AWS_SECRET_ACCESS_KEY. Ensure the skill runs in a isolated environment to avoid contamination; pass dump files via secure paths. For multi-tool integration, chain with tools like strings or YARA by piping outputs, e.g., volatility -f memory.dmp strings | grep suspicious.

Error Handling

Handle common errors by checking Volatility's exit codes; e.g., if profile not found, use imageinfo first. For file not found errors, validate paths before running. In scripts, wrap commands in try-except blocks:

try:
    result = subprocess.run(['volatility', '-f', 'memory.dmp', 'pslist'], capture_output=True, check=True)
except subprocess.CalledProcessError as e:
    print(f"Error: {e.returncode} - {e.stderr.decode()}")

Log detailed errors for debugging, and use $VOLATILITY_DEBUG=1 env var to enable verbose output.

Concrete Usage Examples

  1. Detect Malware in a Windows Dump: Load a memory dump from a suspected infected machine and scan for anomalies.

- Command: volatility -f infected.dmp --profile=Win10x64 malfind - Steps: First run volatility -f infected.dmp imageinfo to confirm profile, then analyze output for PID and virtual address of suspicious processes.

  1. Investigate Rootkit Presence: Analyze a Linux memory dump for hidden kernel modules.

- Command: volatility -f linux.dmp linux_pslist - Steps: Cross-reference with linux_modules to spot discrepancies, then use strings on flagged addresses for further inspection.

Graph Relationships

  • Related to: blue-team cluster skills like "incident-response" (depends on outputs) and "threat-intelligence" (provides input data).
  • Connected via: tags such as "forensics" and "security", linking to skills like "network-forensics" for comprehensive breach analysis.

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

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

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

Codex

36.4%
按下载量换算46

Claude

30.43%
按下载量换算39

Cursor

18.85%
按下载量换算24

Gemini CLI

9.81%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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