correlating-security-events-in-qradar

correlating-security-events-in-qradar is a skill for Claude Code, Codex from adriannoes/awesome-agentic-ai. It costs 78 tokens per session (2,571 once invoked), scanned A, original, MIT.

A guide to investigating and linking security events in IBM QRadar, a security information and event management system that collects logs and raises incidents called offenses. It uses AQL, QRadar's query language, rules, and reference lists to connect activity across systems.

In plain words
What is it for?
Use it to investigate QRadar offenses, query related events, create detection rules for attack patterns, tune alerts, and manage allowlists or watchlists.
Why use it?
A single alert may reveal only one part of a multi-stage attack. Correlating events across network, endpoint, and application logs can expose the wider sequence and reduce false alarms.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to investigate QRadar offenses, query related events, create detection rules for attack patterns, tune alerts, and manage allowlists or watchlists.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/adriannoes/awesome-agentic-ai/correlating-security-events-in-qradar
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 adriannoes/awesome-agentic-ai --skill correlating-security-events-in-qradar
Clone the repo
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-ai

Made for: Claude Code, Codex.

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 correlating-security-events-in-qradar

README.md
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Your own site
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Your own site · 80×15
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/correlating-security-events-in-qradar"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/correlating-security-events-in-qradar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,571 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Data Exfiltration · line 170
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
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 $0.00078 $0.02571
Opus 5 $0.00039 $0.01286
Sonnet 5 $0.00016 $0.00514
Haiku 4.5 $0.00008 $0.00257

Measured 12d ago against content hash e93700ad34d3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

correlating-security-events-in-qradar scanned grade A with 1 finding 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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -X POST "https://qradar.example.com/api/reference_data/sets" \
Origin

Copies of this mod

2 near-identical copies found in the catalogue:

cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/correlating-security-events-in-qradar/SKILL.md · 296 lines

How it starts

The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Correlating Security Events in QRadar

When to Use

Use this skill when:

  • SOC analysts need to investigate QRadar offenses and correlate events across multiple log sources
  • Detection engineers build custom correlation rules to identify multi-stage attacks
  • Alert tuning is required to reduce false positive offenses and improve signal quality
  • The team migrates from basic event monitoring to behavior-based correlation

Do not use for log source onboarding or parsing — that requires QRadar administrator access and DSM editor knowledge.

Prerequisites

  • IBM QRadar SIEM 7.5+ with offense management enabled
  • AQL knowledge for ad-hoc event and flow queries
  • Log sources normalized with proper QID mappings (Windows, firewall, proxy, endpoint)
  • User role with offense management, rule creation, and AQL search permissions
  • Reference sets/maps configured for whitelist and watchlist management

Workflow

Step 1: Investigate an Offense with AQL

Open an offense in QRadar and query contributing events using AQL (Ariel Query Language):

SELECT DATEFORMAT(startTime, 'yyyy-MM-dd HH:mm:ss') AS event_time,
       sourceIP, destinationIP, username,
       LOGSOURCENAME(logSourceId) AS log_source,
       QIDNAME(qid) AS event_name,
       category, magnitude
FROM events
WHERE INOFFENSE(12345)
ORDER BY startTime ASC
LIMIT 500

Pivot on the source IP to find all activity:

SELECT DATEFORMAT(startTime, 'yyyy-MM-dd HH:mm:ss') AS event_time,
       destinationIP, destinationPort, username,
       QIDNAME(qid) AS event_name,
       eventCount, category
FROM events
WHERE sourceIP = '192.168.1.105'
  AND startTime > NOW() - 24*60*60*1000
ORDER BY startTime ASC
LIMIT 1000

Step 2: Build a Custom Correlation Rule

Create a multi-condition rule detecting brute force followed by successful login:

Rule 1 — Brute Force Detection (Building Block):

Rule Type: Event
Rule Name: BB: Multiple Failed Logins from Same Source
Tests:
  - When the event(s) were detected by one or more of [Local]
  - AND when the event QID is one of [Authentication Failure (5000001)]
  - AND when at least 10 events are seen with the same Source IP
    in 5 minutes
Rule Action: Dispatch new event (Category: Authentication, QID: Custom_BruteForce)

Read the full file on GitHub · 296 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 First seen · 296 lines · 78 tokens per session scan A e93700ad34d3

Subscribe to this mod's changes

correlating-security-events-in-qradar is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 78 tokens to every session and 2,571 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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