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 andycungkrinx91/konoha --skill correlating-security-events-in-qradargit clone --depth 1 https://github.com/andycungkrinx91/konohaWrote 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/andycungkrinx91/konoha/correlating-security-events-in-qradar)<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/correlating-security-events-in-qradar"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/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/andycungkrinx91/konoha/correlating-security-events-in-qradar"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/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 5.5 | $0.00031 | $0.01028 |
| Sonnet 5.5 | $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 today.
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
2 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.
- today First seen · 296 lines · 78 tokens per session scan A e93700ad34d3
correlating-security-events-in-qradar is a skill published in the GitHub repository andycungkrinx91/konoha (12 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 2,571 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
correlating-security-events-in-qradar
Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune…
correlating-security-events-in-qradar
Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune…
correlating-security-events-in-qradar
Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune…
correlating-security-events-in-qradar
Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune…
rsigma
Use the rsigma CLI and MCP server: engine eval, engine daemon, rule lint, rule draft, rule tune, rule backtest, backend convert, mcp serve. Prefer MCP tools when rsigma mcp serve is connected. For authoring Sigma YAML (detection, correlation, filters, pipelines, modifiers), use the sigma-rules skill. Use this skill…
building-detection-rule-with-splunk-spl
Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.