security-generate-security-sample-data

elastic/agent-skills · updated May 19, 2026

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$npx skills add https://github.com/elastic/agent-skills --skill security-generate-security-sample-data
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summary

Generate ECS-compliant security events, multi-step attack scenarios, and synthetic alert documents that populate Elastic

  • Security dashboards, the Alerts tab, and Attack Discovery.
skill.md

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

Flag Description
--count, -n Events per package (default: 50)
--scenarios Run all attack simulation scenarios
--scenario NAME Run a specific scenario
--alerts Generate synthetic alert documents
--cleanup Remove all sample data and alerts
--continuous Stream live events (Ctrl+C to stop)
--interval N Seconds between continuous batches (default: 30)
--json, -j Output results as JSON
--yes, -y Skip confirmation prompts

demo-walkthrough.js

Zero-friction runner that generates everything and opens Kibana.

Flag Description
--cleanup Remove all sample data, alerts, case
--continuous Generate then stream live events
--count N Events per package (default: 50)
--interval N Seconds 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

Variable Required Description
ELASTICSEARCH_URL Yes Elasticsearch URL
ELASTICSEARCH_API_KEY Yes* Elasticsearch API key
ELASTICSEARCH_USERNAME Yes* Elasticsearch username (alternative)
ELASTICSEARCH_PASSWORD Yes* Elasticsearch password (alternative)
KIBANA_URL No Kibana URL (for case creation and links)
KIBANA_USERNAME No Kibana username (if using Kibana features)
KIBANA_PASSWORD No Kibana password (if using Kibana features)

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

how to use security-generate-security-sample-data

How to use security-generate-security-sample-data on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add security-generate-security-sample-data
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/elastic/agent-skills --skill security-generate-security-sample-data

The skills CLI fetches security-generate-security-sample-data from GitHub repository elastic/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/security-generate-security-sample-data

Reload or restart Cursor to activate security-generate-security-sample-data. Access the skill through slash commands (e.g., /security-generate-security-sample-data) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.539 reviews
  • Tariq Taylor· Dec 28, 2024

    Solid pick for teams standardizing on skills: security-generate-security-sample-data is focused, and the summary matches what you get after install.

  • Diya Martinez· Dec 16, 2024

    I recommend security-generate-security-sample-data for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chaitanya Patil· Dec 4, 2024

    We added security-generate-security-sample-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Piyush G· Nov 23, 2024

    security-generate-security-sample-data reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yuki Gonzalez· Nov 19, 2024

    Registry listing for security-generate-security-sample-data matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Tariq Brown· Nov 7, 2024

    Useful defaults in security-generate-security-sample-data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Rahul Santra· Nov 3, 2024

    security-generate-security-sample-data fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Isabella Kapoor· Oct 26, 2024

    Registry listing for security-generate-security-sample-data matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Pratham Ware· Oct 22, 2024

    security-generate-security-sample-data has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Shikha Mishra· Oct 14, 2024

    security-generate-security-sample-data is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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