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Agent Skill · 研究检索

security-generate-security-sample-data(security 生成安全样本数据)

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

security-generate-security-sample-data 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,权限为接触敏感数据;许可证信息还不明确,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 14,220。

总安装 8,730周安装 375GitHub Stars 445下载 3,060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
unknown

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:security-generate-security-sample-data(security 生成安全样本数据)
来源仓库:https://github.com/elastic/agent-skills
仓库路径:skills/security-generate-security-sample-data
安装命令:
npx skills add https://github.com/elastic/agent-skills --skill security-generate-security-sample-data
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/elastic/agent-skills --skill security-generate-security-sample-data

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。

用于生成脱敏的安全测试样本数据集。security-generate-security-sample-data 属于研究检索类 Skill,可作为该场景下的辅助能力补充。支持模拟用户行为、权限滥用等安全测试场景。辅助渗透测试、审计演练和监控规则验证。必须确保数据不包含真实敏感信息,仅限测试用途。

适用于 Cursor、Claude 等支持数据操作的 AI 工具。

来源仓库内路径:skills/security-generate-security-sample-data

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/elastic/agent-skills --skill security-generate…;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

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

security-generate-security-sample-data 怎么安装?

在终端执行 npx skills add https://github.com/elastic/agent-skills --skill security-generate-security-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

security-generate-security-sample-data 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。

security-generate-security-sample-data 需要什么权限?

权限级别是接触敏感数据。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。

security-generate-security-sample-data 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

security-generate-security-sample-data 用的人多吗?

公开数据是累计安装 8,730 次,近一周安装 375 次,下载 3,060 次,GitHub 445 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。

security-generate-security-sample-data 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。
  • 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 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
security-generate-security-sample-data

security 生成安全样本数据 · 本页

8,730445接触敏感数据未明确通过

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

Generate Security Sample Data

Generate ECS-compliant security events, multi-step attack scenarios, and synthetic alert documents that populate Elastic Security dashboards, the Alerts tab, and Attack Discovery.

Quick start

For a zero-friction experience that generates everything and opens Kibana:

node skills/security/generate-security-sample-data/scripts/demo-walkthrough.js

Workflow

- [ ] Step 1: Set environment variables
- [ ] Step 2: Generate sample data
- [ ] Step 3: Explore in Kibana
- [ ] Step 4: Clean up when done

Step 1: Set environment variables

export ELASTICSEARCH_URL="https://your-project.es.region.aws.elastic.cloud"
export ELASTICSEARCH_USERNAME="admin"
export ELASTICSEARCH_PASSWORD="your-password"
export KIBANA_URL="https://your-project.kb.region.aws.elastic.cloud"

Step 2: Generate sample data

Generate everything at once
node skills/security/generate-security-sample-data/scripts/sample-data.js \
  system endpoint okta aws windows --scenarios --alerts
Generate only events
node skills/security/generate-security-sample-data/scripts/sample-data.js \
  system endpoint --count 100
Generate only attack scenarios
node skills/security/generate-security-sample-data/scripts/sample-data.js --scenarios
Generate only synthetic alerts
node skills/security/generate-security-sample-data/scripts/sample-data.js --alerts

Step 3: Explore in Kibana

After generating data, direct the user to these pages:

  • Security > Alerts — synthetic alerts with MITRE ATT&CK mappings
  • Security > Attack Discovery — requires an LLM connector to analyze alerts
  • Security > Hosts — host activity from sample events
  • Security > Overview — summary of all security data
  • Discover — raw events across all data streams

Step 4: Clean up when done

node skills/security/generate-security-sample-data/scripts/sample-data.js --cleanup

What gets generated

Sample data spans 5 packages (system, endpoint, windows, aws, okta) and 4 focused attack scenarios covering the most common demo themes: Windows credential theft, AWS cloud privilege escalation, Okta identity takeover, and a full ransomware kill chain. Synthetic alert documents are indexed into .alerts-security.alerts-default with MITRE ATT&CK mappings, severity levels, and risk scores.

All events use RFC 5737 / RFC 2606 safe addresses. For full tables of packages, scenarios, and alerts see references/sample-data-reference.md.

Continuous mode

Stream events to simulate a live environment:

node skills/security/generate-security-sample-data/scripts/sample-data.js \
  --continuous --interval 15

Every 5th batch includes an attack scenario; every 10th batch adds synthetic alerts. Press Ctrl+C to stop.

Tool reference

sample-data.js

FlagDescription
--count, -nEvents per package (default: 50)
--scenariosRun all attack simulation scenarios
--scenario NAMERun a specific scenario
--alertsGenerate synthetic alert documents
--cleanupRemove all sample data and alerts
--continuousStream live events (Ctrl+C to stop)
--interval NSeconds between continuous batches (default: 30)
--json, -jOutput results as JSON
--yes, -ySkip confirmation prompts

demo-walkthrough.js

Zero-friction runner that generates everything and opens Kibana.

FlagDescription
--cleanupRemove all sample data, alerts, case
--continuousGenerate then stream live events
--count NEvents per package (default: 50)
--interval NSeconds between batches (default: 30)

Examples

Quick demo for a stakeholder

"Set up a demo environment so I can show Attack Discovery to my VP."
node skills/security/generate-security-sample-data/scripts/demo-walkthrough.js

Targeted scenario testing

"Generate only the ransomware attack chain to test our detection rules."
node skills/security/generate-security-sample-data/scripts/sample-data.js \
  --scenario ransomwareChain --alerts

Simulating a live SOC

"Keep generating events so the dashboards stay active during the demo."
node skills/security/generate-security-sample-data/scripts/demo-walkthrough.js --continuous

Cleaning up after a demo

"Remove all sample data from my project."
node skills/security/generate-security-sample-data/scripts/sample-data.js --cleanup

Guidelines

  • All generated documents are tagged with tags: ["elastic-security-sample-data"] for safe cleanup. The cleanup command only deletes documents with this marker.
  • If marker fields are not indexed in a data stream, cleanup falls back to scanning _source.tags for matching sample documents from the last 14 days.
  • Synthetic alerts are indexed directly into .alerts-security.alerts-default — they do not require detection rules to be installed or enabled.
  • Attack Discovery requires an LLM connector (OpenAI, Anthropic, Google Gemini, or similar) configured in Kibana under Stack Management > Connectors. The "Complete" project tier unlocks the feature, but the connector must be set up separately.
  • Use the case-management skill for creating investigation cases from alerts.

Production use

  • Do not run against production clusters unless you intend to inject synthetic data alongside real alerts. Sample events and alerts are tagged for cleanup but will appear in dashboards, the Alerts tab, and Attack Discovery alongside real data.
  • All write operations (generate, --cleanup, --continuous) prompt for confirmation. Pass --yes or -y to skip when called by an agent.
  • --cleanup runs deleteByQuery across all sample data indices — verify environment variables point to the intended cluster before running.
  • --continuous mode indexes events indefinitely until manually stopped with Ctrl+C.

Environment variables

VariableRequiredDescription
ELASTICSEARCH_URLYesElasticsearch URL
ELASTICSEARCH_API_KEYYes*Elasticsearch API key
ELASTICSEARCH_USERNAMEYes*Elasticsearch username (alternative)
ELASTICSEARCH_PASSWORDYes*Elasticsearch password (alternative)
KIBANA_URLNoKibana URL (for case creation and links)
KIBANA_USERNAMENoKibana username (if using Kibana features)
KIBANA_PASSWORDNoKibana password (if using Kibana features)

*Either API key or username/password is required for Elasticsearch.

平台分布

Codex

34.57%
按下载量换算1,058

Claude

32.7%
按下载量换算1,001

Cursor

17.58%
按下载量换算538

Gemini CLI

10.78%
按下载量换算330

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

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。目前只有一个公开来源,建议同时核对源仓库。

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