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.
npx skills add 26zl/cybersec-toolkit --skill correlating-security-events-in-qradargit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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.
[](https://agentmods.dev/skills/26zl/cybersec-toolkit/correlating-security-events-in-qradar)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/correlating-security-events-in-qradar"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/correlating-security-events-in-qradar/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.
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/correlating-security-events-in-qradar"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/correlating-security-events-in-qradar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 |
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 9d 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.
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" \ This is a copy
100% identical to correlating-security-events-in-qradar — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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)
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.
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.
- 9d ago First seen · 296 lines · 78 tokens per session scan A e93700ad34d3
correlating-security-events-in-qradar is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed today), 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). It is 100% identical to correlating-security-events-in-qradar, differing in 0 lines, and is treated as a copy.
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