siem-query-languages

siem-query-languages is a skill for Claude Code from HermeticOrmus/LibreSecOps-Claude-Code. It costs 56 tokens per session (2,718 once invoked), scanned A, original, MIT.

A reference guide to the query languages used by Splunk, Elastic, and Microsoft Sentinel, which search and analyze security logs.

In plain words
What is it for?
Use it to write or adapt searches that count events, group activity, build time-based summaries, and compare platform syntax.
Why use it?
It helps people understand the differences between these tools and find suitable patterns for investigating events.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the siem-log-management plugin — 2 skills, 2 commands, 2 agents shipped together

Good fit Use it to write or adapt searches that count events, group activity, build time-based summaries, and compare platform syntax.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hermeticormus/libresecops-claude-code/siem-query-languages
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add HermeticOrmus/LibreSecOps-Claude-Code --skill siem-query-languages
Clone the repo
git clone --depth 1 https://github.com/HermeticOrmus/LibreSecOps-Claude-Code

Made for: Claude Code.

Or install siem-log-management, the plugin that ships this one along with the rest of its 2 skills, 2 commands, 2 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for siem-query-languages

README.md
[![agentmods](https://agentmods.dev/badge/skills/hermeticormus/libresecops-claude-code/siem-query-languages/github.svg)](https://agentmods.dev/skills/hermeticormus/libresecops-claude-code/siem-query-languages)
Your own site
<a href="https://agentmods.dev/skills/hermeticormus/libresecops-claude-code/siem-query-languages"><img src="https://agentmods.dev/badge/skills/hermeticormus/libresecops-claude-code/siem-query-languages/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for siem-query-languages

Your own site · 80×15
<a href="https://agentmods.dev/skills/hermeticormus/libresecops-claude-code/siem-query-languages"><img src="https://agentmods.dev/badge/skills/hermeticormus/libresecops-claude-code/siem-query-languages.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,718 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.02718
Opus 5.5 $0.00022 $0.01087
Sonnet 5.5 $0.00011 $0.00544
Haiku 4.5 $0.00006 $0.00272

Measured 12d ago against content hash 8321f2e2bb65, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

siem-query-languages scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/siem-log-management/skills/siem-query-languages/SKILL.md · 283 lines

How it starts

The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SIEM Query Languages

Reference for Splunk SPL, Elastic KQL/EQL, and Microsoft Sentinel KQL with security-focused query patterns and platform comparison.

Knowledge Base

Language Overview

Feature Splunk SPL Elastic KQL Sentinel KQL (Kusto)
Paradigm Pipe-based search Filter expressions Pipe-based tabular
Strengths Flexible, powerful stats Simple filtering, EQL sequences Rich analytics, joins, time-series
Learning curve Medium Low Medium-High
Sequence detection Transaction command EQL sequences Custom via joins/mv-expand
Machine learning MLTK addon ML jobs Built-in KQL functions
Subsearch/subquery Subsearch, join N/A (use EQL) let statements, join

Splunk SPL Reference

Core search syntax:

index=<index> sourcetype=<sourcetype> <keyword>
| where <condition>
| eval <new_field> = <expression>
| stats <function> by <field>
| table <fields>
| sort <field>

Essential SPL commands for security:

# Count events by field
| stats count by src_ip, dest_ip, dest_port

# Time-based aggregation
| timechart span=1h count by action

# Field extraction (regex)
| rex field=_raw "user=(?<username>\w+)"

# Lookup enrichment
| lookup asset_inventory ip AS src_ip OUTPUT hostname, owner, criticality

# Transaction (session grouping)
| transaction src_ip maxspan=5m maxpause=30s

# Subsearch
[search index=threat_intel | fields ioc_ip | rename ioc_ip AS dest_ip]

# Statistical outlier detection
| eventstats avg(bytes) AS avg_bytes, stdev(bytes) AS stdev_bytes by src_ip
| where bytes > (avg_bytes + 3*stdev_bytes)

# Accelerated datamodel search
| tstats count FROM datamodel=Authentication WHERE Authentication.action=failure BY Authentication.src, Authentication.user

Security query examples:

# Failed logins followed by success (brute force)
index=windows sourcetype=WinEventLog:Security EventCode IN (4625, 4624)
| stats values(EventCode) AS events, count(eval(EventCode=4625)) AS failures, count(eval(EventCode=4624)) AS successes, earliest(_time) AS first_seen, latest(_time) AS last_seen BY TargetUserName, IpAddress
| where failures >= 5 AND successes >= 1

# PowerShell encoded command execution
index=sysmon EventCode=1 Image="*\\powershell.exe"
| where match(CommandLine, "(?i)-e(nc|ncodedcommand)\s+[A-Za-z0-9+/=]{20,}")
| table _time, ComputerName, User, CommandLine, ParentImage

# Rare outbound connections (hunting)
index=proxy
| stats count BY dest_domain
| where count < 5
| sort count

# Data exfiltration (large transfers)
index=firewall action=allowed direction=outbound
| stats sum(bytes_out) AS total_bytes BY src_ip
| where total_bytes > 1073741824
| eval GB=round(total_bytes/1073741824, 2)
| sort -total_bytes

Read the full file on GitHub · 283 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago Changed · +5 lines · +56 tokens per session 8321f2e2bb65
  2. 1mo ago First seen · 278 lines · 0 tokens per session scan A 4048223249d1

Subscribe to this mod's changes

siem-query-languages is a skill published in the GitHub repository HermeticOrmus/LibreSecOps-Claude-Code (4 stars, last pushed 9d ago), licensed MIT. It adds 56 tokens to every session and 2,718 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

implementing-devsecops-security-scanning

Integrates Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Software Composition Analysis (SCA) into CI/CD pipelines using open-source tools. Covers Semgrep for SAST, Trivy for SCA and container scanning, OWASP ZAP for DAST, and Gitleaks for secrets detection. Activates for…

xalgorix/xalgorix · 109 tokens

building-devsecops-pipeline-with-gitlab-ci

Design and implement a comprehensive DevSecOps pipeline in GitLab CI/CD integrating SAST, DAST, container scanning, dependency scanning, and secret detection.

xalgorix/xalgorix · 43 tokens

integrating-dast-with-owasp-zap-in-pipeline

This skill covers integrating OWASP ZAP (Zed Attack Proxy) for Dynamic Application Security Testing in CI/CD pipelines. It addresses configuring baseline, full, and API scans against running applications, interpreting ZAP findings, tuning scan policies, and establishing DAST quality gates in GitHub Actions and GitLab…

xalgorix/xalgorix · 76 tokens

implementing-runtime-application-self-protection

Deploy Runtime Application Self-Protection (RASP) agents to detect and block attacks from within application runtime, covering OpenRASP integration, attack pattern detection, and security policy configuration for Java and Python web applications.

xalgorix/xalgorix · 51 tokens

integrating-dast-with-owasp-zap-in-pipeline

This skill covers integrating OWASP ZAP (Zed Attack Proxy) for Dynamic Application Security Testing in CI/CD pipelines. It addresses configuring baseline, full, and API scans against running applications, interpreting ZAP findings, tuning scan policies, and establishing DAST quality gates in GitHub Actions and GitLab…

adriannoes/awesome-agentic-ai · 76 tokens

audit-production-readiness

Audit a repository or service for release-blocking bugs, security, data/privacy, AI-agent, supply-chain, operability, and scale risks. Use for deep code audits, threat models, pre-production reviews, vulnerability triage, incidents, defensive investigation, release gates, or evidence-based 10k-to-1M-user readiness.

kingggg5/shipproof · 72 tokens